Simulation Based Earthquake Forecasting with RSQSim
Gilchrist, J. J.; Jordan, T. H.; Dieterich, J. H.; Richards-Dinger, K. B.
2016-12-01
We are developing a physics-based forecasting model for earthquake ruptures in California. We employ the 3D boundary element code RSQSim to generate synthetic catalogs with millions of events that span up to a million years. The simulations incorporate rate-state fault constitutive properties in complex, fully interacting fault systems. The Unified California Earthquake Rupture Forecast Version 3 (UCERF3) model and data sets are used for calibration of the catalogs and specification of fault geometry. Fault slip rates match the UCERF3 geologic slip rates and catalogs are tuned such that earthquake recurrence matches the UCERF3 model. Utilizing the Blue Waters Supercomputer, we produce a suite of million-year catalogs to investigate the epistemic uncertainty in the physical parameters used in the simulations. In particular, values of the rate- and state-friction parameters a and b, the initial shear and normal stress, as well as the earthquake slip speed, are varied over several simulations. In addition to testing multiple models with homogeneous values of the physical parameters, the parameters a, b, and the normal stress are varied with depth as well as in heterogeneous patterns across the faults. Cross validation of UCERF3 and RSQSim is performed within the SCEC Collaboratory for Interseismic Simulation and Modeling (CISM) to determine the affect of the uncertainties in physical parameters observed in the field and measured in the lab, on the uncertainties in probabilistic forecasting. We are particularly interested in the short-term hazards of multi-event sequences due to complex faulting and multi-fault ruptures.
Earthquake forecast enrichment scores
Christine Smyth
2012-03-01
Full Text Available The Collaboratory for the Study of Earthquake Predictability (CSEP is a global project aimed at testing earthquake forecast models in a fair environment. Various metrics are currently used to evaluate the submitted forecasts. However, the CSEP still lacks easily understandable metrics with which to rank the universal performance of the forecast models. In this research, we modify a well-known and respected metric from another statistical field, bioinformatics, to make it suitable for evaluating earthquake forecasts, such as those submitted to the CSEP initiative. The metric, originally called a gene-set enrichment score, is based on a Kolmogorov-Smirnov statistic. Our modified metric assesses if, over a certain time period, the forecast values at locations where earthquakes have occurred are significantly increased compared to the values for all locations where earthquakes did not occur. Permutation testing allows for a significance value to be placed upon the score. Unlike the metrics currently employed by the CSEP, the score places no assumption on the distribution of earthquake occurrence nor requires an arbitrary reference forecast. In this research, we apply the modified metric to simulated data and real forecast data to show it is a powerful and robust technique, capable of ranking competing earthquake forecasts.
Earthquake forecasting and its verification
J. R. Holliday
2005-01-01
Full Text Available No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months. However, it is possible to make probabilistic hazard assessments for earthquake risk. In this paper we discuss a new approach to earthquake forecasting based on a pattern informatics (PI method which quantifies temporal variations in seismicity. The output, which is based on an association of small earthquakes with future large earthquakes, is a map of areas in a seismogenic region ('hotspots'' where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. Because a sharp decision threshold is used, these forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative (or receiver operating characteristic (ROC diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI forecast based on the hypothesis that future large earthquakes will occur where most smaller earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.
Statistical earthquake focal mechanism forecasts
Kagan, Yan Y
2013-01-01
Forecasts of the focal mechanisms of future earthquakes are important for seismic hazard estimates and Coulomb stress and other models of earthquake occurrence. Here we report on a high-resolution global forecast of earthquake rate density as a function of location, magnitude, and focal mechanism. In previous publications we reported forecasts of 0.5 degree spatial resolution, covering the latitude range magnitude, and focal mechanism. In previous publications we reported forecasts of 0.5 degree spatial resolution, covering the latitude range from -75 to +75 degrees, based on the Global Central Moment Tensor earthquake catalog. In the new forecasts we've improved the spatial resolution to 0.1 degree and the latitude range from pole to pole. Our focal mechanism estimates require distance-weighted combinations of observed focal mechanisms within 1000 km of each grid point. Simultaneously we calculate an average rotation angle between the forecasted mechanism and all the surrounding mechanisms, using the method ...
Aybige Akinci
2010-11-01
Full Text Available We present a five-year, time-independent, earthquake-forecast model for earthquake magnitudes of 5.0 and greater in Italy using spatially smoothed seismicity data. The model is called HAZGRIDX, and it was developed based on the assumption that future earthquakes will occur near locations of historical earthquakes; it does not take into account any information from tectonic, geological, or geodetic data. Thus HAZGRIDX is based on observed earthquake occurrence from seismicity data, without considering any physical model. In the present study, we calculate earthquake rates on a spatial grid platform using two declustered catalogs: 1 the Parametric catalog of Italian earthquakes (Catalogo Parametrico dei Terremoti Italiani, CPTI04 that contains the larger earthquakes from MW 7.0 since 1100; and 2 the Italian seismicity catalogue (Catalogo della Sismicità Italiana, CSI 1.1 that contains the small earthquakes down to ML 1.0, with a maximum of ML 5.9, over the past 22 years (1981-2003. The model assumes that earthquake magnitudes follow the Gutenberg-Richter law, with a uniform b-value. The forecast rates are presented in terms of the expected numbers of ML>5.0 events per year for each grid cell of about 10 km × 10 km. The final map is derived by averaging the earthquake potentials that come from these two different catalogs: CPTI04 and CSI 1.1. We also describe the earthquake occurrences in terms of probabilities of occurrence of one event within a specified magnitude bin, DM0.1, in a five year time period. HAZGRIDX is one of several forecasting models, scaled to five and ten years, that have been submitted to the Collaboratory for the Study of Earthquake Probability (CSEP forecasting center in ETH, Zurich, to be tested for Italy.
Earthquake number forecasts testing
Kagan, Yan Y.
2017-10-01
and kurtosis both tend to zero for large earthquake rates: for the Gaussian law, these values are identically zero. A calculation of the NBD skewness and kurtosis levels based on the values of the first two statistical moments of the distribution, shows rapid increase of these upper moments levels. However, the observed catalogue values of skewness and kurtosis are rising even faster. This means that for small time intervals, the earthquake number distribution is even more heavy-tailed than the NBD predicts. Therefore for small time intervals, we propose using empirical number distributions appropriately smoothed for testing forecasted earthquake numbers.
Discussion on Earthquake Forecasting and Early Warning
Zhang Xiaodong; Jiang Haikun; Li Mingxiao
2008-01-01
Through analysis of natural and social attributes of earthquake forecasting,the relationship between the natural and social attributes of earthquake forecasting (early warning) has been discussed.Regarding the natural attributes of earthquake forecasting,it only attempts to forecast the magnitude,location and occurrence time of future earthquake based on the aualysis of observational data and relevant theories and taking into consideration the present understanding of seismogeny and earthquake generation.It need not consider the consequences an earthquake forecast involves,and its purpose is to check out the level of scientific understanding of earthquakes.In respect of the social aspect of earthquake forecasting,people also focus on the consequence that the forecasting involves,in addition to its natural aspect,such as the uncertainty of earthquake prediction itself,the impact of earthquake prediction,and the earthquake resistant capability of structures (buildings),lifeline works,etc.In a word,it highlights the risk of earthquake forecasting and tries to mitigate the earthquake hazard as much as possible.In this paper,the authors also discuss the scientific and social challenges faced in earthquake prediction and analyze preliminarily the meanings and content of earthquake early warning.
Interevent times in a new alarm-based earthquake forecasting model
Talbi, Abdelhak; Nanjo, Kazuyoshi; Zhuang, Jiancang; Satake, Kenji; Hamdache, Mohamed
2013-09-01
This study introduces a new earthquake forecasting model that uses the moment ratio (MR) of the first to second order moments of earthquake interevent times as a precursory alarm index to forecast large earthquake events. This MR model is based on the idea that the MR is associated with anomalous long-term changes in background seismicity prior to large earthquake events. In a given region, the MR statistic is defined as the inverse of the index of dispersion or Fano factor, with MR values (or scores) providing a biased estimate of the relative regional frequency of background events, here termed the background fraction. To test the forecasting performance of this proposed MR model, a composite Japan-wide earthquake catalogue for the years between 679 and 2012 was compiled using the Japan Meteorological Agency catalogue for the period between 1923 and 2012, and the Utsu historical seismicity records between 679 and 1922. MR values were estimated by sampling interevent times from events with magnitude M ≥ 6 using an earthquake random sampling (ERS) algorithm developed during previous research. Three retrospective tests of M ≥ 7 target earthquakes were undertaken to evaluate the long-, intermediate- and short-term performance of MR forecasting, using mainly Molchan diagrams and optimal spatial maps obtained by minimizing forecasting error defined by miss and alarm rate addition. This testing indicates that the MR forecasting technique performs well at long-, intermediate- and short-term. The MR maps produced during long-term testing indicate significant alarm levels before 15 of the 18 shallow earthquakes within the testing region during the past two decades, with an alarm region covering about 20 per cent (alarm rate) of the testing region. The number of shallow events missed by forecasting was reduced by about 60 per cent after using the MR method instead of the relative intensity (RI) forecasting method. At short term, our model succeeded in forecasting the
M. J. Werner
2011-02-01
Full Text Available Data assimilation is routinely employed in meteorology, engineering and computer sciences to optimally combine noisy observations with prior model information for obtaining better estimates of a state, and thus better forecasts, than achieved by ignoring data uncertainties. Earthquake forecasting, too, suffers from measurement errors and partial model information and may thus gain significantly from data assimilation. We present perhaps the first fully implementable data assimilation method for earthquake forecasts generated by a point-process model of seismicity. We test the method on a synthetic and pedagogical example of a renewal process observed in noise, which is relevant for the seismic gap hypothesis, models of characteristic earthquakes and recurrence statistics of large quakes inferred from paleoseismic data records. To address the non-Gaussian statistics of earthquakes, we use sequential Monte Carlo methods, a set of flexible simulation-based methods for recursively estimating arbitrary posterior distributions. We perform extensive numerical simulations to demonstrate the feasibility and benefits of forecasting earthquakes based on data assimilation.
Fault-based Earthquake Rupture Forecasts for Western Gulf of Corinth, Greece
Ganas, A.; Parsons, T.; Segkou, M.
2014-12-01
The western Gulf of Corinth has not experienced a strong earthquake since 1995 (the Ms=6.2 event of Aigion on 15 June 1995; Bernard et al., 1997), although the Gulf is extending fast (over 12 mm/yr of N-S extension from continuous GPS data spanning a period of 9+ years) and its seismic history since 1769 exhibits twelve (12) shallow events with M>6.0. We undertook an analysis of rupture forecasts along the active faults in this area of central Greece, using most updated datasets (active fault maps, fault geometry, fault slip rates, trenching data on past earthquakes, historical and instrumental seismicity, strain) and models for earthquake budget extrapolated from observed seismicity, magnitude-frequency distributions and calculated earthquake rates vs. magnitude for individual faults. We present a unified rupture forecast model that comprises a time-independent (Poisson-process) earthquake rate model, and a time-dependent earthquake-probability model, based on recent earthquake rates and stress-renewal statistics conditioned on the date of last event. The resulting rupture rate maps may be used to update building codes and promote mitigation efforts.
Earthquake forecasting: Statistics and Information
Gertsik, V; Krichevets, A
2013-01-01
We present an axiomatic approach to earthquake forecasting in terms of multi-component random fields on a lattice. This approach provides a method for constructing point estimates and confidence intervals for conditional probabilities of strong earthquakes under conditions on the levels of precursors. Also, it provides an approach for setting multilevel alarm system and hypothesis testing for binary alarms. We use a method of comparison for different earthquake forecasts in terms of the increase of Shannon information. 'Forecasting' and 'prediction' of earthquakes are equivalent in this approach.
Operational earthquake forecasting can enhance earthquake preparedness
Jordan, T.H.; Marzocchi, W.; Michael, A.J.; Gerstenberger, M.C.
2014-01-01
We cannot yet predict large earthquakes in the short term with much reliability and skill, but the strong clustering exhibited in seismic sequences tells us that earthquake probabilities are not constant in time; they generally rise and fall over periods of days to years in correlation with nearby seismic activity. Operational earthquake forecasting (OEF) is the dissemination of authoritative information about these time‐dependent probabilities to help communities prepare for potentially destructive earthquakes. The goal of OEF is to inform the decisions that people and organizations must continually make to mitigate seismic risk and prepare for potentially destructive earthquakes on time scales from days to decades. To fulfill this role, OEF must provide a complete description of the seismic hazard—ground‐motion exceedance probabilities as well as short‐term rupture probabilities—in concert with the long‐term forecasts of probabilistic seismic‐hazard analysis (PSHA).
Earthquake forecasting: statistics and information
Vladimir Gertsik
2016-01-01
Full Text Available The paper presents a decision rule forming a mathematical basis of earthquake forecasting problem. We develop an axiomatic approach to earthquake forecasting in terms of multicomponent random fields on a lattice. This approach provides a method for constructing point estimates and confidence intervals for conditional probabilities of strong earthquakes under conditions on the levels of precursors. Also, it provides an approach for setting a multilevel alarm system and hypothesis testing for binary alarms. We use a method of comparison for different algorithms of earthquake forecasts in terms of the increase of Shannon information. ‘Forecasting’ (the calculation of the probabilities and ‘prediction’ (the alarm declaring of earthquakes are equivalent in this approach.
Chunan Tang; Tianhui Ma; Xiaoli Ding
2009-01-01
Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR), used for monitoring crust deformation, are found to be very promising in earthquake prediction subject to stress-forecasting. However, it is rec-ognized that unless we can give reasonable explanations of these curious precursory phenomena that continue to be seren-dipitously observed fi'om time to time, such high technology of GPS or InSAR is difficult to be efficiently used. Therefore, a proper model revealing the relation between earthquake evolution and stress variation, such as the phenomena of stress buildup, stress shadow and stress transfer (SSS), is crucial to the GPS or lnSAR based earthquake prediction. Here we ad-dress this question through a numerical approach of earthquake development using an intuitive physical model with a map-like configuration of discontinuous fault system. The simulation provides a physical basis for the principle of stress-forecasting of earthquakes based on SSS and for the application of GPS or InSAR in earthquake prediction. The ob-served SSS associated phenomena with images of stress distribution during the failure process can be continuously simulated. It is shown that the SSS are better indicators of earthquake precursors than that of seismic foreshocks, suggesting a predict-ability of earthquakes based on stress-forecasting strategy.
Retrospective validation of renewal-based, medium-term earthquake forecasts
Rotondi, R.
2013-10-01
In this paper, some methods for scoring the performances of an earthquake forecasting probability model are applied retrospectively for different goals. The time-dependent occurrence probabilities of a renewal process are tested against earthquakes of Mw ≥ 5.3 recorded in Italy according to decades of the past century. An aim was to check the capability of the model to reproduce the data by which the model was calibrated. The scoring procedures used can be distinguished on the basis of the requirement (or absence) of a reference model and of probability thresholds. Overall, a rank-based score, information gain, gambling scores, indices used in binary predictions and their loss functions are considered. The definition of various probability thresholds as percentages of the hazard functions allows proposals of the values associated with the best forecasting performance as alarm level in procedures for seismic risk mitigation. Some improvements are then made to the input data concerning the completeness of the historical catalogue and the consistency of the composite seismogenic sources with the hypotheses of the probability model. Another purpose of this study was thus to obtain hints on what is the most influential factor and on the suitability of adopting the consequent changes of the data sets. This is achieved by repeating the estimation procedure of the occurrence probabilities and the retrospective validation of the forecasts obtained under the new assumptions. According to the rank-based score, the completeness appears to be the most influential factor, while there are no clear indications of the usefulness of the decomposition of some composite sources, although in some cases, it has led to improvements of the forecast.
Short- and Long-Term Earthquake Forecasts Based on Statistical Models
Console, Rodolfo; Taroni, Matteo; Murru, Maura; Falcone, Giuseppe; Marzocchi, Warner
2017-04-01
The epidemic-type aftershock sequences (ETAS) models have been experimentally used to forecast the space-time earthquake occurrence rate during the sequence that followed the 2009 L'Aquila earthquake and for the 2012 Emilia earthquake sequence. These forecasts represented the two first pioneering attempts to check the feasibility of providing operational earthquake forecasting (OEF) in Italy. After the 2009 L'Aquila earthquake the Italian Department of Civil Protection nominated an International Commission on Earthquake Forecasting (ICEF) for the development of the first official OEF in Italy that was implemented for testing purposes by the newly established "Centro di Pericolosità Sismica" (CPS, the seismic Hazard Center) at the Istituto Nazionale di Geofisica e Vulcanologia (INGV). According to the ICEF guidelines, the system is open, transparent, reproducible and testable. The scientific information delivered by OEF-Italy is shaped in different formats according to the interested stakeholders, such as scientists, national and regional authorities, and the general public. The communication to people is certainly the most challenging issue, and careful pilot tests are necessary to check the effectiveness of the communication strategy, before opening the information to the public. With regard to long-term time-dependent earthquake forecast, the application of a newly developed simulation algorithm to Calabria region provided typical features in time, space and magnitude behaviour of the seismicity, which can be compared with those of the real observations. These features include long-term pseudo-periodicity and clustering of strong earthquakes, and a realistic earthquake magnitude distribution departing from the Gutenberg-Richter distribution in the moderate and higher magnitude range.
Earthquake forecast via neutrino tomography
WANG Bin; CHEN Ya-Zheng; LI Xue-Qian
2011-01-01
We discuss the possibility of forecasting earthquakes by means of (anti)neutrino tomography. An- tineutrinos emitted from reactors are used as a probe. As the antineutrinos traverse through a region prone to earthquakes, observable variations in the matter effect on the antineutrino oscillation would provide a tomog- raphy of the vicinity of the region. In this preliminary work, we adopt a simplified model for the geometrical profile and matter density in a fault zone. We calculate the survival probability of electron antineutrinos for cases without and with an anomalous accumulation of electrons which can be considered as a clear signal of the coming earthquake, at the geological region with a fault zone, and find that the variation may reach as much as 3% for ν emitted from a reactor. The case for a ν beam from a neutrino factory is also investigated, and it is noted that, because of the typically high energy associated with such neutrinos, the oscillation length is too large and the resultant variation is not practically observable. Our conclusion is that with the present reactor facilities and detection techniques, it is still a difficult task to make an earthquake forecast using such a scheme, though it seems to be possible from a theoretical point of view while ignoring some uncertainties. However, with the development of the geology, especially the knowledge about the fault zone, and with the improvement of the detection techniques, etc., there is hope that a medium-term earthquake forecast would be feasible.
Earthquakes - Volcanoes (Causes and Forecast)
Tsiapas, E.
2009-04-01
EARTHQUAKES - VOLCANOES (CAUSES AND FORECAST) ELIAS TSIAPAS RESEARCHER NEA STYRA, EVIA,GREECE TEL.0302224041057 tsiapas@hol.gr The earthquakes are caused by large quantities of liquids (e.g. H2O, H2S, SO2, ect.) moving through lithosphere and pyrosphere (MOHO discontinuity) till they meet projections (mountains negative projections or projections coming from sinking lithosphere). The liquids are moved from West Eastward carried away by the pyrosphere because of differential speed of rotation of the pyrosphere by the lithosphere. With starting point an earthquake which was noticed at an area and from statistical studies, we know when, where and what rate an earthquake may be, which earthquake is caused by the same quantity of liquids, at the next east region. The forecast of an earthquake ceases to be valid if these components meet a crack in the lithosphere (e.g. limits of lithosphere plates) or a volcano crater. In this case the liquids come out into the atmosphere by the form of gasses carrying small quantities of lava with them (volcano explosion).
Short-term earthquake forecasting based on an epidemic clustering model
Console, Rodolfo; Murru, Maura; Falcone, Giuseppe
2016-04-01
The application of rigorous statistical tools, with the aim of verifying any prediction method, requires a univocal definition of the hypothesis, or the model, characterizing the concerned anomaly or precursor, so as it can be objectively recognized in any circumstance and by any observer. This is mandatory to build up on the old-fashion approach consisting only of the retrospective anecdotic study of past cases. A rigorous definition of an earthquake forecasting hypothesis should lead to the objective identification of particular sub-volumes (usually named alarm volumes) of the total time-space volume within which the probability of occurrence of strong earthquakes is higher than the usual. The test of a similar hypothesis needs the observation of a sufficient number of past cases upon which a statistical analysis is possible. This analysis should be aimed to determine the rate at which the precursor has been followed (success rate) or not followed (false alarm rate) by the target seismic event, or the rate at which a target event has been preceded (alarm rate) or not preceded (failure rate) by the precursor. The binary table obtained from this kind of analysis leads to the definition of the parameters of the model that achieve the maximum number of successes and the minimum number of false alarms for a specific class of precursors. The mathematical tools suitable for this purpose may include the definition of Probability Gain or the R-Score, as well as the application of popular plots such as the Molchan error-diagram and the ROC diagram. Another tool for evaluating the validity of a forecasting method is the concept of the likelihood ratio (also named performance factor) of occurrence and non-occurrence of seismic events under different hypotheses. Whatever is the method chosen for building up a new hypothesis, usually based on retrospective data, the final assessment of its validity should be carried out by a test on a new and independent set of observations
Stress-based aftershock forecasting: the 2008 M=7.9 Wenchuan, and 2013 M=6.6 Lushan earthquakes
Parsons, T.
2013-12-01
Immediately after the 12 May 2008 M=7.9 Wenchuan earthquake, static stress change calculations were made on the on major faults surrounding the rupture zone. The purpose was two-fold: (1) to identify the most likely locations (stress increases) of dangerous aftershocks, and (2) to conduct a prospective test of stress mapping as a rapid-response forecast tool. The occurrence of the 20 April M=6.6 Lushan earthquake in the Longmen fault zone near Ya'an was consistent with the static stress forecast, but a formal evaluation of the post Wenchuan forecast performance was not favorable because the anticipated aftershock distribution was violated, with clear seismicity rate increases in stress shadow zones. Here I look at reconciling these results and ask the question, are static stress change calculations more applicable to larger aftershocks? A single case such as the Wenchuan-Lushan pairing could readily be a coincidence, so I look at additional large continental earthquakes and their aftershock magnitude relations. Results show (1) the most probable place that high magnitude aftershocks will occur is in areas with the highest aftershock activity, (2) high magnitude aftershocks are most likely to happen where stress change calculations are greatest, and (3) high magnitude aftershocks are most likely to happen on well developed fault zones. All three of these points are fairly obvious, but a conclusion that can be drawn from the 2008 M=7.9 Wenchuan and 2013 M=6.6 Lushon pair is that all three are necessary considerations. The location of the 2013 M=6.6 Lushon earthquake was consistent with stress change calculations, although there was virtually no precursory activity in the immediate vicinity. Therefore a forecast based only on elevated activity rates would not have anticipated its location.
Liu Jie; Guo Tieshuan; Yang Liming; Su Youjin; Li Gang
2009-01-01
The reason for the failure to forecast the Wenchuan Ms8.0 earthquake is under study, based on the systematically collection of the seismicity anomalies and their analysis results from annual earthquake tendency forecasts between the 2001 Western Kuulun Mountains Pass Ms8.1 earthquake and the 2008 Wenchuan Ms8.0 earthquake. The results show that the earthquake tendency estimation of Chinese Mainland is for strong earthquakes to occur in the active stage, and that there is still potential for the occurrence of a Ms8.0 large earthquake in Chinese Mainland after the 2001 Western Kunlun Mountains Pass earthquake. However the phenomena that many large earthquakes occurred around Chinese Mainland, and the 6-year long quietude of Ms7.0 earthquake and an obvious quietude of Ms5.0 and Ms6.0 earthquakes during 2002 ～2007 led to the distinctly lower forecast estimation of earthquake tendency in Chinese Mainland after 2006. The middle part in the north-south seismic belt has been designated a seismic risk area of strong earthquake in recent years, but, the estimation of the risk degree in Southwestern China is insufficient after the Ning'er Ms6.4 earthquake in Yunnan in 2007. There are no records of earthquakes with Ms≥7.0 in the Longmenshan fault, which is one of reasons that this fault was not considered a seismic risk area of strong earthquakes in recent years.
An interdisciplinary approach for earthquake modelling and forecasting
Han, P.; Zhuang, J.; Hattori, K.; Ogata, Y.
2016-12-01
Earthquake is one of the most serious disasters, which may cause heavy casualties and economic losses. Especially in the past two decades, huge/mega earthquakes have hit many countries. Effective earthquake forecasting (including time, location, and magnitude) becomes extremely important and urgent. To date, various heuristically derived algorithms have been developed for forecasting earthquakes. Generally, they can be classified into two types: catalog-based approaches and non-catalog-based approaches. Thanks to the rapid development of statistical seismology in the past 30 years, now we are able to evaluate the performances of these earthquake forecast approaches quantitatively. Although a certain amount of precursory information is available in both earthquake catalogs and non-catalog observations, the earthquake forecast is still far from satisfactory. In most case, the precursory phenomena were studied individually. An earthquake model that combines self-exciting and mutually exciting elements was developed by Ogata and Utsu from the Hawkes process. The core idea of this combined model is that the status of the event at present is controlled by the event itself (self-exciting) and all the external factors (mutually exciting) in the past. In essence, the conditional intensity function is a time-varying Poisson process with rate λ(t), which is composed of the background rate, the self-exciting term (the information from past seismic events), and the external excitation term (the information from past non-seismic observations). This model shows us a way to integrate the catalog-based forecast and non-catalog-based forecast. Against this background, we are trying to develop a new earthquake forecast model which combines catalog-based and non-catalog-based approaches.
Forecasting characteristic earthquakes in a minimalist model
Vázquez-Prada, M.; Pacheco, A.; González, Á.
2003-01-01
Using error diagrams, we quantify the forecasting of characteristic-earthquake occurence in a recently introduced minimalist model. Initially we connect the earthquake alarm at a fixed time after the occurence of a characteristic event. The evaluation of this strategy leads to a one-dimensional n...
Segou, Margarita
2016-01-01
I perform a retrospective forecast experiment in the most rapid extensive continental rift worldwide, the western Corinth Gulf (wCG, Greece), aiming to predict shallow seismicity (depth ETAS) statistics, four physics-based (CRS) models, combining static stress change estimations and the rate-and-state laboratory law and one hybrid model. For the latter models, I incorporate the stress changes imparted from 31 earthquakes with magnitude M ≥ 4.5 at the extended area of wCG. Special attention is given on the 3-D representation of active faults, acting as potential receiver planes for the estimation of static stress changes. I use reference seismicity between 1990 and 1995, corresponding to the learning phase of physics-based models, and I evaluate the forecasts for six months following the 1995 M = 6.4 Aigio earthquake using log-likelihood performance metrics. For the ETAS realizations, I use seismic events with magnitude M ≥ 2.5 within daily update intervals to enhance their predictive power. For assessing the role of background seismicity, I implement a stochastic reconstruction (aka declustering) aiming to answer whether M > 4.5 earthquakes correspond to spontaneous events and identify, if possible, different triggering characteristics between aftershock sequences and swarm-type seismicity periods. I find that: (1) ETAS models outperform CRS models in most time intervals achieving very low rejection ratio RN = 6 per cent, when I test their efficiency to forecast the total number of events inside the study area, (2) the best rejection ratio for CRS models reaches RN = 17 per cent, when I use varying target depths and receiver plane geometry, (3) 75 per cent of the 1995 Aigio aftershocks that occurred within the first month can be explained by static stress changes, (4) highly variable performance on behalf of both statistical and physical models is suggested by large confidence intervals of information gain per earthquake and (5) generic ETAS models can
García, Alicia; De la Cruz-Reyna, Servando; Marrero, José M.; Ortiz, Ramón
2016-05-01
Under certain conditions, volcano-tectonic (VT) earthquakes may pose significant hazards to people living in or near active volcanic regions, especially on volcanic islands; however, hazard arising from VT activity caused by localized volcanic sources is rarely addressed in the literature. The evolution of VT earthquakes resulting from a magmatic intrusion shows some orderly behaviour that may allow the occurrence and magnitude of major events to be forecast. Thus governmental decision makers can be supplied with warnings of the increased probability of larger-magnitude earthquakes on the short-term timescale. We present here a methodology for forecasting the occurrence of large-magnitude VT events during volcanic crises; it is based on a mean recurrence time (MRT) algorithm that translates the Gutenberg-Richter distribution parameter fluctuations into time windows of increased probability of a major VT earthquake. The MRT forecasting algorithm was developed after observing a repetitive pattern in the seismic swarm episodes occurring between July and November 2011 at El Hierro (Canary Islands). From then on, this methodology has been applied to the consecutive seismic crises registered at El Hierro, achieving a high success rate in the real-time forecasting, within 10-day time windows, of volcano-tectonic earthquakes.
Earthquake rate and magnitude distributions of great earthquakes for use in global forecasts
Kagan, Yan Y.; Jackson, David D.
2016-07-01
We have obtained new results in the statistical analysis of global earthquake catalogues with special attention to the largest earthquakes, and we examined the statistical behaviour of earthquake rate variations. These results can serve as an input for updating our recent earthquake forecast, known as the `Global Earthquake Activity Rate 1' model (GEAR1), which is based on past earthquakes and geodetic strain rates. The GEAR1 forecast is expressed as the rate density of all earthquakes above magnitude 5.8 within 70 km of sea level everywhere on earth at 0.1 × 0.1 degree resolution, and it is currently being tested by the Collaboratory for Study of Earthquake Predictability. The seismic component of the present model is based on a smoothed version of the Global Centroid Moment Tensor (GCMT) catalogue from 1977 through 2013. The tectonic component is based on the Global Strain Rate Map, a `General Earthquake Model' (GEM) product. The forecast was optimized to fit the GCMT data from 2005 through 2012, but it also fit well the earthquake locations from 1918 to 1976 reported in the International Seismological Centre-Global Earthquake Model (ISC-GEM) global catalogue of instrumental and pre-instrumental magnitude determinations. We have improved the recent forecast by optimizing the treatment of larger magnitudes and including a longer duration (1918-2011) ISC-GEM catalogue of large earthquakes to estimate smoothed seismicity. We revised our estimates of upper magnitude limits, described as corner magnitudes, based on the massive earthquakes since 2004 and the seismic moment conservation principle. The new corner magnitude estimates are somewhat larger than but consistent with our previous estimates. For major subduction zones we find the best estimates of corner magnitude to be in the range 8.9 to 9.6 and consistent with a uniform average of 9.35. Statistical estimates tend to grow with time as larger earthquakes occur. However, by using the moment conservation
OPERATIONAL EARTHQUAKE FORECASTING. State of Knowledge and Guidelines for Utilization
Koshun Yamaoka
2011-08-01
earthquake forecasting as the principle means for gathering and disseminating authoritative information about time-dependent seismic hazards to help communities prepare for potentially destructive earthquakes. On short time scales of days and weeks, earthquake sequences show clustering in space and time, as indicated by the aftershocks triggered by large events. Statistical descriptions of clustering explain many features observed in seismicity catalogs, and they can be used to construct forecasts that indicate how earthquake probabilities change over the short term. Properly applied, short-term forecasts have operational utility; for example, in anticipating aftershocks that follow large earthquakes. Although the value of long-term forecasts for ensuring seismic safety is clear, the interpretation of short-term forecasts is problematic, because earthquake probabilities may vary over orders of magnitude but typically remain low in an absolute sense (< 1% per day. Translating such low-probability forecasts into effective decision-making is a difficult challenge. Reports on the current utilization operational forecasting in earthquake risk management were compiled for six countries with high seismic risk: China, Greece, Italy, Japan, Russia, United States. Long-term models are currently the most important forecasting tools for civil protection against earthquake damage, because they guide earthquake safety provisions of building codes, performance-based seismic design, and other risk-reducing engineering practices, such as retrofitting to correct design flaws in older buildings. Short-term forecasting of aftershocks is practiced by several countries among those surveyed, but operational earthquake forecasting has not been fully implemented (i.e., regularly updated and on a national scale in any of them. Based on the experience accumulated in seismically active regions, the ICEF has provided to DPC a set of recommendations on the utilization of operational forecasting in Italy
Time-Dependent Earthquake Forecasts on a Global Scale
Rundle, J. B.; Holliday, J. R.; Turcotte, D. L.; Graves, W. R.
2014-12-01
We develop and implement a new type of global earthquake forecast. Our forecast is a perturbation on a smoothed seismicity (Relative Intensity) spatial forecast combined with a temporal time-averaged ("Poisson") forecast. A variety of statistical and fault-system models have been discussed for use in computing forecast probabilities. An example is the Working Group on California Earthquake Probabilities, which has been using fault-based models to compute conditional probabilities in California since 1988. An example of a forecast is the Epidemic-Type Aftershock Sequence (ETAS), which is based on the Gutenberg-Richter (GR) magnitude-frequency law, the Omori aftershock law, and Poisson statistics. The method discussed in this talk is based on the observation that GR statistics characterize seismicity for all space and time. Small magnitude event counts (quake counts) are used as "markers" for the approach of large events. More specifically, if the GR b-value = 1, then for every 1000 M>3 earthquakes, one expects 1 M>6 earthquake. So if ~1000 M>3 events have occurred in a spatial region since the last M>6 earthquake, another M>6 earthquake should be expected soon. In physics, event count models have been called natural time models, since counts of small events represent a physical or natural time scale characterizing the system dynamics. In a previous research, we used conditional Weibull statistics to convert event counts into a temporal probability for a given fixed region. In the present paper, we move belyond a fixed region, and develop a method to compute these Natural Time Weibull (NTW) forecasts on a global scale, using an internally consistent method, in regions of arbitrary shape and size. We develop and implement these methods on a modern web-service computing platform, which can be found at www.openhazards.com and www.quakesim.org. We also discuss constraints on the User Interface (UI) that follow from practical considerations of site usability.
Adaptively smoothed seismicity earthquake forecasts for Italy
Yan Y. Kagan
2010-11-01
Full Text Available We present a model for estimation of the probabilities of future earthquakes of magnitudes m ≥ 4.95 in Italy. This model is a modified version of that proposed for California, USA, by Helmstetter et al. [2007] and Werner et al. [2010a], and it approximates seismicity using a spatially heterogeneous, temporally homogeneous Poisson point process. The temporal, spatial and magnitude dimensions are entirely decoupled. Magnitudes are independently and identically distributed according to a tapered Gutenberg-Richter magnitude distribution. We have estimated the spatial distribution of future seismicity by smoothing the locations of past earthquakes listed in two Italian catalogs: a short instrumental catalog, and a longer instrumental and historic catalog. The bandwidth of the adaptive spatial kernel is estimated by optimizing the predictive power of the kernel estimate of the spatial earthquake density in retrospective forecasts. When available and reliable, we used small earthquakes of m ≥ 2.95 to reveal active fault structures and 29 probable future epicenters. By calibrating the model with these two catalogs of different durations to create two forecasts, we intend to quantify the loss (or gain of predictability incurred when only a short, but recent, data record is available. Both forecasts were scaled to five and ten years, and have been submitted to the Italian prospective forecasting experiment of the global Collaboratory for the Study of Earthquake Predictability (CSEP. An earlier forecast from the model was submitted by Helmstetter et al. [2007] to the Regional Earthquake Likelihood Model (RELM experiment in California, and with more than half of the five-year experimental period over, the forecast has performed better than the others.
Gambling scores for earthquake predictions and forecasts
Zhuang, Jiancang
2010-04-01
This paper presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points betted by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. We also calculate the upper bound of the gambling score when the true model is a renewal process, the stress release model or the ETAS model and when the reference model is the Poisson model.
Werner, M. J.; Marzocchi, W.; Taroni, M.; Zechar, J. D.; Gerstenberger, M.; Liukis, M.; Rhoades, D. A.; Cattania, C.; Christophersen, A.; Hainzl, S.; Helmstetter, A.; Jimenez, A.; Steacy, S.; Jordan, T. H.
2014-12-01
The M7.1 Darfield, New Zealand (NZ), earthquake triggered a complex earthquake cascade that provides a wealth of new scientific data to study earthquake triggering and the predictive skill of statistical and physics-based forecasting models. To this end, the Collaboratory for the Study of Earthquake Predictability (CSEP) is conducting a retrospective evaluation of over a dozen short-term forecasting models that were developed by groups in New Zealand, Europe and the US. The statistical model group includes variants of the Epidemic-Type Aftershock Sequence (ETAS) model, non-parametric kernel smoothing models, and the Short-Term Earthquake Probabilities (STEP) model. The physics-based model group includes variants of the Coulomb stress triggering hypothesis, which are embedded either in Dieterich's (1994) rate-state formulation or in statistical Omori-Utsu clustering formulations (hybrid models). The goals of the CSEP evaluation are to improve our understanding of the physical mechanisms governing earthquake triggering, to improve short-term earthquake forecasting models and time-dependent hazard assessment for the Canterbury area, and to understand the influence of poor-quality, real-time data on the skill of operational (real-time) forecasts. To assess the latter, we use the earthquake catalog data that the NZ CSEP Testing Center archived in near real-time during the earthquake sequence and compare the predictive skill of models using the archived data as input with the skill attained using the best available data today. We present results of the retrospective model comparison and discuss implications for operational earthquake forecasting.
Operational Earthquake Forecasting: Proposed Guidelines for Implementation (Invited)
Jordan, T. H.
2010-12-01
The goal of operational earthquake forecasting (OEF) is to provide the public with authoritative information about how seismic hazards are changing with time. During periods of high seismic activity, short-term earthquake forecasts based on empirical statistical models can attain nominal probability gains in excess of 100 relative to the long-term forecasts used in probabilistic seismic hazard analysis (PSHA). Prospective experiments are underway by the Collaboratory for the Study of Earthquake Predictability (CSEP) to evaluate the reliability and skill of these seismicity-based forecasts in a variety of tectonic environments. How such information should be used for civil protection is by no means clear, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing formal procedures for OEF in this sort of “low-probability environment.” Nevertheless, the need to move more quickly towards OEF has been underscored by recent experiences, such as the 2009 L’Aquila earthquake sequence and other seismic crises in which an anxious public has been confused by informal, inconsistent earthquake forecasts. Whether scientists like it or not, rising public expectations for real-time information, accelerated by the use of social media, will require civil protection agencies to develop sources of authoritative information about the short-term earthquake probabilities. In this presentation, I will discuss guidelines for the implementation of OEF informed by my experience on the California Earthquake Prediction Evaluation Council, convened by CalEMA, and the International Commission on Earthquake Forecasting, convened by the Italian government following the L’Aquila disaster. (a) Public sources of information on short-term probabilities should be authoritative, scientific, open, and
The Value, Protocols, and Scientific Ethics of Earthquake Forecasting
Jordan, Thomas H.
2013-04-01
Earthquakes are different from other common natural hazards because precursory signals diagnostic of the magnitude, location, and time of impending seismic events have not yet been found. Consequently, the short-term, localized prediction of large earthquakes at high probabilities with low error rates (false alarms and failures-to-predict) is not yet feasible. An alternative is short-term probabilistic forecasting based on empirical statistical models of seismic clustering. During periods of high seismic activity, short-term earthquake forecasts can attain prospective probability gains up to 1000 relative to long-term forecasts. The value of such information is by no means clear, however, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing operational forecasting protocols in this sort of "low-probability environment." This paper will explore the complex interrelations among the valuation of low-probability earthquake forecasting, which must account for social intangibles; the protocols of operational forecasting, which must factor in large uncertainties; and the ethics that guide scientists as participants in the forecasting process, who must honor scientific principles without doing harm. Earthquake forecasts possess no intrinsic societal value; rather, they acquire value through their ability to influence decisions made by users seeking to mitigate seismic risk and improve community resilience to earthquake disasters. According to the recommendations of the International Commission on Earthquake Forecasting (www.annalsofgeophysics.eu/index.php/annals/article/view/5350), operational forecasting systems should appropriately separate the hazard-estimation role of scientists from the decision-making role of civil protection authorities and individuals. They should
Retrospective stress-forecasting of earthquakes
Gao, Yuan; Crampin, Stuart
2015-04-01
Observations of changes in azimuthally varying shear-wave splitting (SWS) above swarms of small earthquakes monitor stress-induced changes to the stress-aligned vertical microcracks pervading the upper crust, lower crust, and uppermost ~400km of the mantle. (The microcracks are intergranular films of hydrolysed melt in the mantle.) Earthquakes release stress, and an appropriate amount of stress for the relevant magnitude must accumulate before each event. Iceland is on an extension of the Mid-Atlantic Ridge, where two transform zones, uniquely run onshore. These onshore transform zones provide semi-continuous swarms of small earthquakes, which are the only place worldwide where SWS can be routinely monitored. Elsewhere SWS must be monitored above temporally-active occasional swarms of small earthquakes, or in infrequent SKS and other teleseismic reflections from the mantle. Observations of changes in SWS time-delays are attributed to stress-induced changes in crack aspect-ratios allowing stress-accumulation and stress-relaxation to be identified. Monitoring SWS in SW Iceland in 1988, stress-accumulation before an impending earthquake was recognised and emails were exchanged between the University of Edinburgh (EU) and the Iceland Meteorological Office (IMO). On 10th November 1988, EU emailed IMO that a M5 earthquake could occur soon on a seismically-active fault plane where seismicity was still continuing following a M5.1 earthquake six-months earlier. Three-days later, IMO emailed EU that a M5 earthquake had just occurred on the specified fault-plane. We suggest this is a successful earthquake stress-forecast, where we refer to the procedure as stress-forecasting earthquakes as opposed to predicting or forecasting to emphasise the different formalism. Lack of funds has prevented us monitoring SWS on Iceland seismograms, however, we have identified similar characteristic behaviour of SWS time-delays above swarms of small earthquakes which have enabled us to
International Aftershock Forecasting: Lessons from the Gorkha Earthquake
Michael, A. J.; Blanpied, M. L.; Brady, S. R.; van der Elst, N.; Hardebeck, J.; Mayberry, G. C.; Page, M. T.; Smoczyk, G. M.; Wein, A. M.
2015-12-01
Following the M7.8 Gorhka, Nepal, earthquake of April 25, 2015 the USGS issued a series of aftershock forecasts. The initial impetus for these forecasts was a request from the USAID Office of US Foreign Disaster Assistance to support their Disaster Assistance Response Team (DART) which coordinated US Government disaster response, including search and rescue, with the Government of Nepal. Because of the possible utility of the forecasts to people in the region and other response teams, the USGS released these forecasts publicly through the USGS Earthquake Program web site. The initial forecast used the Reasenberg and Jones (Science, 1989) model with generic parameters developed for active deep continental regions based on the Garcia et al. (BSSA, 2012) tectonic regionalization. These were then updated to reflect a lower productivity and higher decay rate based on the observed aftershocks, although relying on teleseismic observations, with a high magnitude-of-completeness, limited the amount of data. After the 12 May M7.3 aftershock, the forecasts used an Epidemic Type Aftershock Sequence model to better characterize the multiple sources of earthquake clustering. This model provided better estimates of aftershock uncertainty. These forecast messages were crafted based on lessons learned from the Christchurch earthquake along with input from the U.S. Embassy staff in Kathmandu. Challenges included how to balance simple messaging with forecasts over a variety of time periods (week, month, and year), whether to characterize probabilities with words such as those suggested by the IPCC (IPCC, 2010), how to word the messages in a way that would translate accurately into Nepali and not alarm the public, and how to present the probabilities of unlikely but possible large and potentially damaging aftershocks, such as the M7.3 event, which had an estimated probability of only 1-in-200 for the week in which it occurred.
EARTHQUAKES - VOLCANOES (Causes - Forecast - Counteraction)
Tsiapas, Elias
2014-05-01
Earthquakes and volcanoes are caused by: 1)Various liquid elements (e.g. H20, H2S, S02) which emerge from the pyrosphere and are trapped in the space between the solid crust and the pyrosphere (Moho discontinuity). 2)Protrusions of the solid crust at the Moho discontinuity (mountain range roots, sinking of the lithosphere's plates). 3)The differential movement of crust and pyrosphere. The crust misses one full rotation for approximately every 100 pyrosphere rotations, mostly because of the lunar pull. The above mentioned elements can be found in small quantities all over the Moho discontinuity, and they are constantly causing minor earthquakes and small volcanic eruptions. When large quantities of these elements (H20, H2S, SO2, etc) concentrate, they are carried away by the pyrosphere, moving from west to east under the crust. When this movement takes place under flat surfaces of the solid crust, it does not cause earthquakes. But when these elements come along a protrusion (a mountain root) they concentrate on its western side, displacing the pyrosphere until they fill the space created. Due to the differential movement of pyrosphere and solid crust, a vacuum is created on the eastern side of these protrusions and when the aforementioned liquids overfill this space, they explode, escaping to the east. At the point of their escape, these liquids are vaporized and compressed, their flow accelerates, their temperature rises due to fluid friction and they are ionized. On the Earth's surface, a powerful rumbling sound and electrical discharges in the atmosphere, caused by the movement of the gasses, are noticeable. When these elements escape, the space on the west side of the protrusion is violently taken up by the pyrosphere, which collides with the protrusion, causing a major earthquake, attenuation of the protrusions, cracks on the solid crust and damages to structures on the Earth's surface. It is easy to foresee when an earthquake will occur and how big it is
Adaptively Smoothed Seismicity Earthquake Forecasts for Italy
Werner, M J; Jackson, D D; Kagan, Y Y; Wiemer, S
2010-01-01
We present a model for estimating the probabilities of future earthquakes of magnitudes m > 4.95 in Italy. The model, a slightly modified version of the one proposed for California by Helmstetter et al. (2007) and Werner et al. (2010), approximates seismicity by a spatially heterogeneous, temporally homogeneous Poisson point process. The temporal, spatial and magnitude dimensions are entirely decoupled. Magnitudes are independently and identically distributed according to a tapered Gutenberg-Richter magnitude distribution. We estimated the spatial distribution of future seismicity by smoothing the locations of past earthquakes listed in two Italian catalogs: a short instrumental catalog and a longer instrumental and historical catalog. The bandwidth of the adaptive spatial kernel is estimated by optimizing the predictive power of the kernel estimate of the spatial earthquake density in retrospective forecasts. When available and trustworthy, we used small earthquakes m>2.95 to illuminate active fault structur...
The potential uses of operational earthquake forecasting
Field, Ned; Jordan, Thomas; Jones, Lucille; Michael, Andrew; Blanpied, Michael L.
2016-01-01
This article reports on a workshop held to explore the potential uses of operational earthquake forecasting (OEF). We discuss the current status of OEF in the United States and elsewhere, the types of products that could be generated, the various potential users and uses of OEF, and the need for carefully crafted communication protocols. Although operationalization challenges remain, there was clear consensus among the stakeholders at the workshop that OEF could be useful.
The Effects of Static Coulomb Stress Change on Southern California Earthquake Forecasting
Strader, Anne Elizabeth
I investigate how inclusion of static Coulomb stress changes, caused by tectonic loading and previous seismicity, contributes to the effectiveness and reliability of prospective earthquake forecasts. Several studies have shown that positive static Coulomb stress changes are associated with increased seismicity, relative to stress shadows. However, it is difficult to avoid bias when the learning and testing intervals are chosen retrospectively. I hypothesize that earthquake forecasts based on static Coulomb stress fields may improve upon existing earthquake forecasts based on historical seismicity. Within southern California, I have confirmed the aforementioned relationship between earthquake location and Coulomb stress change, but found no identifiable triggering threshold based on static Coulomb stress history at individual earthquake locations. I have also converted static Coulomb stress changes into spatially-varying earthquake rates by optimizing an index function and calculating probabilities of cells containing at least one earthquake based on Coulomb stress ranges. Inclusion of Coulomb stress effects gives an improvement in earthquake forecasts that is significant with 95% confidence, compared to smoothed seismicity null forecasts. Because of large uncertainties in Coulomb stress calculations near faults (and aftershock distributions), I combine static Coulomb stress and smoothed seismicity into a hybrid earthquake forecast. Evaluating such forecasts against those in which only Coulomb stress or smoothed seismicity determines earthquake rates indicates that Coulomb stress is more effective in the far field, whereas statistical seismology outperforms Coulomb stress near faults. Additionally, I test effects of receiver plane orientation, stress type (normal and shear components), and declustering receiver earthquakes. While static Coulomb stress shows significant potential in a prospective earthquake forecast, simplifying assumptions compromise its
Prospective and retrospective evaluation of five-year earthquake forecast models for California
Strader, Anne; Schneider, Max; Schorlemmer, Danijel
2017-10-01
The Collaboratory for the Study of Earthquake Predictability was developed to prospectively test earthquake forecasts through reproducible and transparent experiments within a controlled environment. From January 2006 to December 2010, the Regional Earthquake Likelihood Models (RELM) Working Group developed and evaluated thirteen time-invariant prospective earthquake mainshock forecasts. The number, spatial and magnitude components of the forecasts were compared to the observed seismicity distribution using a set of likelihood-based consistency tests. In this RELM experiment update, we assess the long-term forecasting potential of the RELM forecasts. Additionally, we evaluate RELM forecast performance against the Uniform California Earthquake Rupture Forecast (UCERF2) and the National Seismic Hazard Mapping Project (NSHMP) forecasts, which are used for seismic hazard analysis for California. To test each forecast's long-term stability, we also evaluate each forecast from January 2006 to December 2015, which contains both five-year testing periods, and the 40-year period from January 1967 to December 2006. Multiple RELM forecasts, which passed the N-test during the retrospective (January 2006 to December 2010) period, overestimate the number of events from January 2011 to December 2015, although their forecasted spatial distributions are consistent with observed earthquakes. Both the UCERF2 and NSHMP forecasts pass all consistency tests for the two five-year periods; however, they tend to underestimate the number of observed earthquakes over the 40-year testing period. The smoothed seismicity model Helmstetter-et-al.Mainshock outperforms both United States Geological Survey (USGS) models during the second five-year experiment, and contains higher forecasted seismicity rates than the USGS models at multiple observed earthquake locations.
Parsons, Thomas E.; Segou, Margaret; Sevilgen, Volkan; Milner, Kevin; Field, Ned; Toda, Shinji; Stein, Ross S.
2014-01-01
We calculate stress changes resulting from the M= 6.0 West Napa earthquake on north San Francisco Bay area faults. The earthquake ruptured within a series of long faults that pose significant hazard to the Bay area, and we are thus concerned with potential increases in the probability of a large earthquake through stress transfer. We conduct this exercise as a prospective test because the skill of stress-based aftershock forecasting methodology is inconclusive. We apply three methods: (1) generalized mapping of regional Coulomb stress change, (2) stress changes resolved on Uniform California Earthquake Rupture Forecast faults, and (3) a mapped rate/state aftershock forecast. All calculations were completed within 24 h after the main shock and were made without benefit of known aftershocks, which will be used to evaluative the prospective forecast. All methods suggest that we should expect heightened seismicity on parts of the southern Rodgers Creek, northern Hayward, and Green Valley faults.
Parsons, Tom; Segou, Margaret; Sevilgen, Volkan; Milner, Kevin; Field, Edward; Toda, Shinji; Stein, Ross S.
2014-12-01
We calculate stress changes resulting from the M = 6.0 West Napa earthquake on north San Francisco Bay area faults. The earthquake ruptured within a series of long faults that pose significant hazard to the Bay area, and we are thus concerned with potential increases in the probability of a large earthquake through stress transfer. We conduct this exercise as a prospective test because the skill of stress-based aftershock forecasting methodology is inconclusive. We apply three methods: (1) generalized mapping of regional Coulomb stress change, (2) stress changes resolved on Uniform California Earthquake Rupture Forecast faults, and (3) a mapped rate/state aftershock forecast. All calculations were completed within 24 h after the main shock and were made without benefit of known aftershocks, which will be used to evaluative the prospective forecast. All methods suggest that we should expect heightened seismicity on parts of the southern Rodgers Creek, northern Hayward, and Green Valley faults.
Earthquake Forecasting as a System-Science Problem
Jordan, T. H.
2012-12-01
The increasing exposure of society to natural hazards has made the forecasting of extreme events a pressing scientific concern. No aspect of this problem has been more vexing than earthquake prediction. The century-long failure to identify observable precursory signals diagnostic of impending events has led to an alternative approach, in which a variety of constraints on earthquake location, magnitude, and long-term frequency are synthesized into probabilistic seismic hazard models, such as those produced by the USGS National Seismic Hazard Mapping Project. This presentation will describe how recent progress in earthquake system science is improving hazard and risk forecasting. These system-level problems can be partitioned according to causal sequences described in terms of conditional probabilities. For example, the exceedance probabilities of shaking intensities at geographically distributed sites conditional on a particular fault rupture (a ground motion prediction model or GMPM) can be combined with the probabilities of different ruptures (an earthquake rupture forecast or ERF) to create a seismic hazard map. Deterministic simulations of ground motions from very large suites (millions) of ruptures, now feasible through high-performance computational platforms such as SCEC's CyberShake, are allowing seismologists to replace empirical GMPMs with physics-based models that more accurately represent wave propagation through heterogeneous geologic structures, such as sedimentary basins that amplify seismic shaking. A notable advance is the development of ERFs conditioned on preceding seismic activity, such as the Uniform California Earthquake Rupture Forecasts produced by the Working Groups on California Earthquake Probabilities. These time-dependent probability models account for the stress-renewal processes of elastic rebound, and they are beginning to capture aftershock triggering. However, they have not fully reconciled the long-term phase modulation of stress
1/f and the Earthquake Problem: Scaling constraints to facilitate operational earthquake forecasting
Yoder, M. R.; Rundle, J. B.; Glasscoe, M. T.
2013-12-01
The difficulty of forecasting earthquakes can fundamentally be attributed to the self-similar, or '1/f', nature of seismic sequences. Specifically, the rate of occurrence of earthquakes is inversely proportional to their magnitude m, or more accurately to their scalar moment M. With respect to this '1/f problem,' it can be argued that catalog selection (or equivalently, determining catalog constraints) constitutes the most significant challenge to seismicity based earthquake forecasting. Here, we address and introduce a potential solution to this most daunting problem. Specifically, we introduce a framework to constrain, or partition, an earthquake catalog (a study region) in order to resolve local seismicity. In particular, we combine Gutenberg-Richter (GR), rupture length, and Omori scaling with various empirical measurements to relate the size (spatial and temporal extents) of a study area (or bins within a study area), in combination with a metric to quantify rate trends in local seismicity, to the local earthquake magnitude potential - the magnitudes of earthquakes the region is expected to experience. From this, we introduce a new type of time dependent hazard map for which the tuning parameter space is nearly fully constrained. In a similar fashion, by combining various scaling relations and also by incorporating finite extents (rupture length, area, and duration) as constraints, we develop a method to estimate the Omori (temporal) and spatial aftershock decay parameters as a function of the parent earthquake's magnitude m. From this formulation, we develop an ETAS type model that overcomes many point-source limitations of contemporary ETAS. These models demonstrate promise with respect to earthquake forecasting applications. Moreover, the methods employed suggest a general framework whereby earthquake and other complex-system, 1/f type, problems can be constrained from scaling relations and finite extents.
Earthquake forecasting during the complex Amatrice-Norcia seismic sequence
Marzocchi, Warner; Taroni, Matteo; Falcone, Giuseppe
2017-01-01
Earthquake forecasting is the ultimate challenge for seismologists, because it condenses the scientific knowledge about the earthquake occurrence process, and it is an essential component of any sound risk mitigation planning. It is commonly assumed that, in the short term, trustworthy earthquake forecasts are possible only for typical aftershock sequences, where the largest shock is followed by many smaller earthquakes that decay with time according to the Omori power law. We show that the current Italian operational earthquake forecasting system issued statistically reliable and skillful space-time-magnitude forecasts of the largest earthquakes during the complex 2016–2017 Amatrice-Norcia sequence, which is characterized by several bursts of seismicity and a significant deviation from the Omori law. This capability to deliver statistically reliable forecasts is an essential component of any program to assist public decision-makers and citizens in the challenging risk management of complex seismic sequences.
Jiancang Zhuang
2012-07-01
Full Text Available Based on the ETAS (epidemic-type aftershock sequence model, which is used for describing the features of short-term clustering of earthquake occurrence, this paper presents some theories and techniques related to evaluating the probability distribution of the maximum magnitude in a given space-time window, where the Gutenberg-Richter law for earthquake magnitude distribution cannot be directly applied. It is seen that the distribution of the maximum magnitude in a given space-time volume is determined in the longterm by the background seismicity rate and the magnitude distribution of the largest events in each earthquake cluster. The techniques introduced were applied to the seismicity in the Japan region in the period from 1926 to 2009. It was found that the regions most likely to have big earthquakes are along the Tohoku (northeastern Japan Arc and the Kuril Arc, both with much higher probabilities than the offshore Nankai and Tokai regions.
Uniform California earthquake rupture forecast, version 2 (UCERF 2)
Field, E.H.; Dawson, T.E.; Felzer, K.R.; Frankel, A.D.; Gupta, V.; Jordan, T.H.; Parsons, T.; Petersen, M.D.; Stein, R.S.; Weldon, R.J.; Wills, C.J.
2009-01-01
The 2007 Working Group on California Earthquake Probabilities (WGCEP, 2007) presents the Uniform California Earthquake Rupture Forecast, Version 2 (UCERF 2). This model comprises a time-independent (Poisson-process) earthquake rate model, developed jointly with the National Seismic Hazard Mapping Program and a time-dependent earthquake-probability model, based on recent earthquake rates and stress-renewal statistics conditioned on the date of last event. The models were developed from updated statewide earthquake catalogs and fault deformation databases using a uniform methodology across all regions and implemented in the modular, extensible Open Seismic Hazard Analysis framework. The rate model satisfies integrating measures of deformation across the plate-boundary zone and is consistent with historical seismicity data. An overprediction of earthquake rates found at intermediate magnitudes (6.5 ??? M ???7.0) in previous models has been reduced to within the 95% confidence bounds of the historical earthquake catalog. A logic tree with 480 branches represents the epistemic uncertainties of the full time-dependent model. The mean UCERF 2 time-dependent probability of one or more M ???6.7 earthquakes in the California region during the next 30 yr is 99.7%; this probability decreases to 46% for M ???7.5 and to 4.5% for M ???8.0. These probabilities do not include the Cascadia subduction zone, largely north of California, for which the estimated 30 yr, M ???8.0 time-dependent probability is 10%. The M ???6.7 probabilities on major strike-slip faults are consistent with the WGCEP (2003) study in the San Francisco Bay Area and the WGCEP (1995) study in southern California, except for significantly lower estimates along the San Jacinto and Elsinore faults, owing to provisions for larger multisegment ruptures. Important model limitations are discussed.
Portals for Real-Time Earthquake Data and Forecasting: Challenge and Promise (Invited)
Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Feltstykket, R.; Donnellan, A.; Glasscoe, M. T.
2013-12-01
Earthquake forecasts have been computed by a variety of countries world-wide for over two decades. For the most part, forecasts have been computed for insurance, reinsurance and underwriters of catastrophe bonds. However, recent events clearly demonstrate that mitigating personal risk is becoming the responsibility of individual members of the public. Open access to a variety of web-based forecasts, tools, utilities and information is therefore required. Portals for data and forecasts present particular challenges, and require the development of both apps and the client/server architecture to deliver the basic information in real time. The basic forecast model we consider is the Natural Time Weibull (NTW) method (JBR et al., Phys. Rev. E, 86, 021106, 2012). This model uses small earthquakes (';seismicity-based models') to forecast the occurrence of large earthquakes, via data-mining algorithms combined with the ANSS earthquake catalog. This method computes large earthquake probabilities using the number of small earthquakes that have occurred in a region since the last large earthquake. Localizing these forecasts in space so that global forecasts can be computed in real time presents special algorithmic challenges, which we describe in this talk. Using 25 years of data from the ANSS California-Nevada catalog of earthquakes, we compute real-time global forecasts at a grid scale of 0.1o. We analyze and monitor the performance of these models using the standard tests, which include the Reliability/Attributes and Receiver Operating Characteristic (ROC) tests. It is clear from much of the analysis that data quality is a major limitation on the accurate computation of earthquake probabilities. We discuss the challenges of serving up these datasets over the web on web-based platforms such as those at www.quakesim.org , www.e-decider.org , and www.openhazards.com.
Applications of the gambling score in evaluating earthquake predictions and forecasts
Zhuang, Jiancang; Zechar, Jeremy D.; Jiang, Changsheng; Console, Rodolfo; Murru, Maura; Falcone, Giuseppe
2010-05-01
This study presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points bet by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. For discrete predictions, we apply this method to evaluate performance of Shebalin's predictions made by using the Reverse Tracing of Precursors (RTP) algorithm and of the outputs of the predictions from the Annual Consultation Meeting on Earthquake Tendency held by China Earthquake Administration. For the continuous case, we use it to compare the probability forecasts of seismicity in the Abruzzo region before and after the L'aquila earthquake based on the ETAS model and the PPE model.
2008-01-01
Pattern informatics (PI) model is one of the recently developed predictive models of earthquake phys- ics based on the statistical mechanics of complex systems. In this paper, retrospective forecast test of the PI model was conducted for the earthquakes in Sichuan-Yunnan region since 1988, exploring the possibility to apply this model to the estimation of time-dependent seismic hazard in continental China. Regional earthquake catalogue down to ML3.0 from 1970 to 2007 was used. The ‘target magnitude’ for the forecast test was MS5.5. Fifteen-year long ‘sliding time window’ was used in the PI calculation, with ‘anomaly training time window’ being 5 years and ‘forecast time window’ being 5 years, respectively. Receiver operating characteristic (ROC) test was conducted for the evaluation of the forecast result, showing that the PI forecast outperforms not only random guess but also the simple number counting approach based on the clustering hypothesis of earthquakes (the RI forecast). If the ‘forecast time window’ was shortened to 3 years and 1 year, respectively, the forecast capability of the PI model de- creased significantly, albeit outperformed random forecast. For the one year ‘forecast time window’, the PI result was almost comparable to the RI result, indicating that clustering properties play a more important role at this time scale.
Lee, Ya-Ting; Turcotte, Donald L; Holliday, James R; Sachs, Michael K; Rundle, John B; Chen, Chien-Chih; Tiampo, Kristy F
2011-10-04
The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M ≥ 4.95 earthquakes in 0.1° × 0.1° cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M ≥ 4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M = 7.2, April 4, 2010, El Mayor-Cucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most "successful" in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts.
Using earthquake intensities to forecast earthquake occurrence times
J. R. Holliday
2006-01-01
Full Text Available It is well known that earthquakes do not occur randomly in space and time. Foreshocks, aftershocks, precursory activation, and quiescence are just some of the patterns recognized by seismologists. Using the Pattern Informatics technique along with relative intensity analysis, we create a scoring method based on time dependent relative operating characteristic diagrams and show that the occurrences of large earthquakes in California correlate with time intervals where fluctuations in small earthquakes are suppressed relative to the long term average. We estimate a probability of less than 1% that this coincidence is due to random clustering. Furthermore, we show that the methods used to obtain these results may be applicable to other parts of the world.
Pattern Informatics and Its Application for Optimal Forecasting of Large Earthquakes in Japan
Nanjo, K Z; Holliday, J R; Turcotte, D L
2005-01-01
Pattern informatics (PI) technique can be used to detect precursory seismic activation or quiescence and make earthquake forecast. Here we apply the PI method for optimal forecasting of large earthquakes in Japan, using the data catalogue maintained by the Japan Meteorological Agency. The PI method is tested to forecast large (magnitude m >= 5) earthquakes for the time period 1995-2004 in the Kobe region. Visual inspection and statistical testing show that the optimized PI method has forecasting skill, relative to the seismic intensity data often used as a standard null hypothesis. Moreover, we find a retrospective forecast that the 1995 Kobe earthquake (m = 7.2) falls in a seismically anomalous area. Another approach to test the forecasting algorithm is to create a future potential map for large (m >= 5) earthquake events. This is illustrated using the Kobe and Tokyo regions for the forecast period 2000-2009. Based on the resulting Kobe map we point out several forecasted areas: the epicentral area of the 19...
Research on strong earthquake type division and forecast method for subsequent strong earthquakes
无
2000-01-01
The relationships between energy, amplitude and frequency of earthquake are correlative with the property of the seismic source. And the grade of the correlativity can be used as an index to distinguish the types of strong earthquakes. Primarily the strong earthquake can be divided into three types of main-after earthquakes, double-main earthquakes and swarm of strong earthquake. There are similarity and a certain repeatability at the quantificational indexes of hypocenter property between the same type of strong earthquakes, which supply basis for the forecast of subsequent strong shocks. The reference indexes of after strong shock forecast which are valuable for the applications of the method of type-divided forecast come from the analysis about more than fifty strong shock wide-band (BPZ wave) recording data of CDSN from 1988 to 1997.
Implications of the Regional Earthquake Likelihood Models test of earthquake forecasts in California
Michael Karl Sachs
2012-09-01
Full Text Available The Regional Earthquake Likelihood Models (RELM test was the first competitive comparison of prospective earthquake forecasts. The test was carried out over 5 years from 1 January 2006 to 31 December 2010 over a region that included all of California. The test area was divided into 7682 0.1°x0.1° spatial cells. Each submitted forecast gave the predicted numbers of earthquakes Nemi larger than M=4.95 in 0.1 magnitude bins for each cell. In this paper we present a method that separates the forecast of the number of test earthquakes from the forecast of their locations. We first obtain the number Nem of forecast earthquakes in magnitude bin m. We then determine the conditional probability λemi=Nemi/Nem that an earthquake in magnitude bin m will occur in cell i. The summation of λemi over all 7682 cells is unity. A random (no skill forecast gives equal values of λemi for all spatial cells and magnitude bins. The skill of a forecast, in terms of the location of the earthquakes, is measured by the success in assigning large values of λemi to the cells in which earthquakes occur and low values of λemi to the cells where earthquakes do not occur. Thirty-one test earthquakes occurred in 27 different combinations of spatial cells i and magnitude bins m, we had the highest value of λemi for that mi cell. We evaluate the performance of eleven submitted forecasts in two ways. First, we determine the number of mi cells for which the forecast λemi was the largest, the best forecast is the one with the highest number. Second, we determine the mean value of λemi for the 27 mi cells for each forecast. The best forecast has the highest mean value of λemi. The success of a forecast during the test period is dependent on the allocation of the probabilities λemi between the mi cells, since the sum over the mi cells is unity. We illustrate the forecast distributions of λemi and discuss their differences. We conclude that the RELM test was successful in
A note on the ranking of earthquake forecasts
Molchan, G
2016-01-01
The ranking problem of earthquake forecasts is considered. We formulate simple statistical requirements to forecasting quality measure R and analyze some R-ranking methods on this basis, in particular, the pari-mutuel gambling method by Zechar&Zhuang (2014).
Rate-and-State Southern California Earthquake Forecasts: Resolving Stress Singularities
Strader, A. E.; Jackson, D. D.
2014-12-01
In previous studies, we pseudo-prospectively evaluated time-dependent Coulomb stress earthquake forecasts, based on rate-and-state friction (Toda and Enescu, 2011 and Dieterich, 1996), against an ETAS null hypothesis (Zhuang et al., 2002). At the 95% confidence interval, we found that the stress-based forecast failed to outperform the ETAS forecast during the first eight weeks following the 10/16/1999 Hector Mine earthquake, in both earthquake number and spatial distribution. The rate-and-state forecast was most effective in forecasting far-field events (earthquakes occurring at least 50km away from modeled active faults). Near active faults, where most aftershocks occurred, stress singularities arising from modeled fault section boundaries obscured the Coulomb stress field. In addition to yielding physically unrealistic stress quantities, the stress singularities arising from the slip model often failed to indicate potential fault asperity locations inferred from aftershock distributions. Here, we test the effects of these stress singularities on the rate-and-state forecast's effectiveness, as well as mitigate stress uncertainties near active faults. We decrease the area significantly impacted by stress singularities by increasing the number of fault patches and introducing tapered slip at fault section boundaries, representing displacement as a high-resolution step function. Using recent seismicity distributions to relocate fault asperities, we also invert seismicity for a fault displacement model with higher resolution than the original slip distribution, where areas of positive static Coulomb stress change coincide with earthquake locations.
Statistical physics approach to earthquake occurrence and forecasting
Arcangelis, Lucilla de [Department of Industrial and Information Engineering, Second University of Naples, Aversa (CE) (Italy); Godano, Cataldo [Department of Mathematics and Physics, Second University of Naples, Caserta (Italy); Grasso, Jean Robert [ISTerre, IRD-CNRS-OSUG, University of Grenoble, Saint Martin d’Héres (France); Lippiello, Eugenio, E-mail: eugenio.lippiello@unina2.it [Department of Mathematics and Physics, Second University of Naples, Caserta (Italy)
2016-04-25
There is striking evidence that the dynamics of the Earth crust is controlled by a wide variety of mutually dependent mechanisms acting at different spatial and temporal scales. The interplay of these mechanisms produces instabilities in the stress field, leading to abrupt energy releases, i.e., earthquakes. As a consequence, the evolution towards instability before a single event is very difficult to monitor. On the other hand, collective behavior in stress transfer and relaxation within the Earth crust leads to emergent properties described by stable phenomenological laws for a population of many earthquakes in size, time and space domains. This observation has stimulated a statistical mechanics approach to earthquake occurrence, applying ideas and methods as scaling laws, universality, fractal dimension, renormalization group, to characterize the physics of earthquakes. In this review we first present a description of the phenomenological laws of earthquake occurrence which represent the frame of reference for a variety of statistical mechanical models, ranging from the spring-block to more complex fault models. Next, we discuss the problem of seismic forecasting in the general framework of stochastic processes, where seismic occurrence can be described as a branching process implementing space–time-energy correlations between earthquakes. In this context we show how correlations originate from dynamical scaling relations between time and energy, able to account for universality and provide a unifying description for the phenomenological power laws. Then we discuss how branching models can be implemented to forecast the temporal evolution of the earthquake occurrence probability and allow to discriminate among different physical mechanisms responsible for earthquake triggering. In particular, the forecasting problem will be presented in a rigorous mathematical framework, discussing the relevance of the processes acting at different temporal scales for
Statistical physics approach to earthquake occurrence and forecasting
de Arcangelis, Lucilla; Godano, Cataldo; Grasso, Jean Robert; Lippiello, Eugenio
2016-04-01
There is striking evidence that the dynamics of the Earth crust is controlled by a wide variety of mutually dependent mechanisms acting at different spatial and temporal scales. The interplay of these mechanisms produces instabilities in the stress field, leading to abrupt energy releases, i.e., earthquakes. As a consequence, the evolution towards instability before a single event is very difficult to monitor. On the other hand, collective behavior in stress transfer and relaxation within the Earth crust leads to emergent properties described by stable phenomenological laws for a population of many earthquakes in size, time and space domains. This observation has stimulated a statistical mechanics approach to earthquake occurrence, applying ideas and methods as scaling laws, universality, fractal dimension, renormalization group, to characterize the physics of earthquakes. In this review we first present a description of the phenomenological laws of earthquake occurrence which represent the frame of reference for a variety of statistical mechanical models, ranging from the spring-block to more complex fault models. Next, we discuss the problem of seismic forecasting in the general framework of stochastic processes, where seismic occurrence can be described as a branching process implementing space-time-energy correlations between earthquakes. In this context we show how correlations originate from dynamical scaling relations between time and energy, able to account for universality and provide a unifying description for the phenomenological power laws. Then we discuss how branching models can be implemented to forecast the temporal evolution of the earthquake occurrence probability and allow to discriminate among different physical mechanisms responsible for earthquake triggering. In particular, the forecasting problem will be presented in a rigorous mathematical framework, discussing the relevance of the processes acting at different temporal scales for different
Varenna workshop report. Operational earthquake forecasting and decision making
Warner Marzocchi
2015-09-01
Full Text Available A workshop on Operational earthquake forecasting and decision making was convened in Varenna, Italy, on June 8-11, 2014, under the sponsorship of the EU FP 7 REAKT (Strategies and tools for Real-time EArthquake risK reducTion project, the Seismic Hazard Center at the Istituto Nazionale di Geofisica e Vulcanologia (INGV, and the Southern California Earthquake Center (SCEC. The main goal was to survey the interdisciplinary issues of operational earthquake forecasting (OEF, including the problems that OEF raises for decision making and risk communication. The workshop was attended by 64 researchers from universities, research centers, and governmental institutions in 11 countries. Participants and the workshop agenda are listed in the appendix.The workshop comprised six topical sessions structured around three main themes: the science of operational earthquake forecasting, decision making in a low-probability environment, and communicating hazard and risk. Each topic was introduced by a moderator and surveyed by a few invited speakers, who were then empaneled for an open discussion. The presentations were followed by poster sessions. During a wrap-up session on the last day, the reporters for each topical session summarized the main points that they had gleaned from the talks and open discussions. This report attempts to distill this workshop record into a brief overview of the workshop themes and to describe the range of opinions expressed during the discussions.
High Resolution Long- and Short-Term Earthquake Forecasts for California
Werner, M J; Jackson, D D; Kagan, Y Y
2009-01-01
We present two models for estimating the probabilities of future earthquakes in California, to be tested in the Collaboratory for the Study of Earthquake Predictability (CSEP). The first, time-independent model, modified from Helmstetter et al. (2007), provides five-year forecasts for magnitudes m > 4.95. We show that large quakes occur on average near the locations of small m > 2 events, so that a high-resolution estimate of the spatial distribution of future large quakes is obtained from the locations of the numerous small events. We employ an adaptive spatial kernel of optimized bandwidth and assume a universal, tapered Gutenberg-Richter distribution. In retrospective tests, we show that no Poisson forecast could capture the observed variability. We therefore also test forecasts using a negative binomial distribution for the number of events. We modify existing likelihood-based tests to better evaluate the spatial forecast. Our time-dependent model, an Epidemic Type Aftershock Sequence (ETAS) model modifie...
Operational Earthquake Forecasting and Decision-Making in a Low-Probability Environment
Jordan, T. H.; the International Commission on Earthquake ForecastingCivil Protection
2011-12-01
Operational earthquake forecasting (OEF) is the dissemination of authoritative information about the time dependence of seismic hazards to help communities prepare for potentially destructive earthquakes. Most previous work on the public utility of OEF has anticipated that forecasts would deliver high probabilities of large earthquakes; i.e., deterministic predictions with low error rates (false alarms and failures-to-predict) would be possible. This expectation has not been realized. An alternative to deterministic prediction is probabilistic forecasting based on empirical statistical models of aftershock triggering and seismic clustering. During periods of high seismic activity, short-term earthquake forecasts can attain prospective probability gains in excess of 100 relative to long-term forecasts. The utility of such information is by no means clear, however, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing OEF in this sort of "low-probability environment." The need to move more quickly has been underscored by recent seismic crises, such as the 2009 L'Aquila earthquake sequence, in which an anxious public was confused by informal and inaccurate earthquake predictions. After the L'Aquila earthquake, the Italian Department of Civil Protection appointed an International Commission on Earthquake Forecasting (ICEF), which I chaired, to recommend guidelines for OEF utilization. Our report (Ann. Geophys., 54, 4, 2011; doi: 10.4401/ag-5350) concludes: (a) Public sources of information on short-term probabilities should be authoritative, scientific, open, and timely, and need to convey epistemic uncertainties. (b) Earthquake probabilities should be based on operationally qualified, regularly updated forecasting systems. (c) All operational models should be evaluated
Fractals and Forecasting in Earthquakes and Finance
Rundle, J. B.; Holliday, J. R.; Turcotte, D. L.
2011-12-01
It is now recognized that Benoit Mandelbrot's fractals play a critical role in describing a vast range of physical and social phenomena. Here we focus on two systems, earthquakes and finance. Since 1942, earthquakes have been characterized by the Gutenberg-Richter magnitude-frequency relation, which in more recent times is often written as a moment-frequency power law. A similar relation can be shown to hold for financial markets. Moreover, a recent New York Times article, titled "A Richter Scale for the Markets" [1] summarized the emerging viewpoint that stock market crashes can be described with similar ideas as large and great earthquakes. The idea that stock market crashes can be related in any way to earthquake phenomena has its roots in Mandelbrot's 1963 work on speculative prices in commodities markets such as cotton [2]. He pointed out that Gaussian statistics did not account for the excessive number of booms and busts that characterize such markets. Here we show that both earthquakes and financial crashes can both be described by a common Landau-Ginzburg-type free energy model, involving the presence of a classical limit of stability, or spinodal. These metastable systems are characterized by fractal statistics near the spinodal. For earthquakes, the independent ("order") parameter is the slip deficit along a fault, whereas for the financial markets, it is financial leverage in place. For financial markets, asset values play the role of a free energy. In both systems, a common set of techniques can be used to compute the probabilities of future earthquakes or crashes. In the case of financial models, the probabilities are closely related to implied volatility, an important component of Black-Scholes models for stock valuations. [2] B. Mandelbrot, The variation of certain speculative prices, J. Business, 36, 294 (1963)
A synoptic view of the Third Uniform California Earthquake Rupture Forecast (UCERF3)
Field, Ned; Jordan, Thomas H.; Page, Morgan T.; Milner, Kevin R.; Shaw, Bruce E.; Dawson, Timothy E.; Biasi, Glenn; Parsons, Thomas E.; Hardebeck, Jeanne L.; Michael, Andrew J.; Weldon, Ray; Powers, Peter; Johnson, Kaj M.; Zeng, Yuehua; Bird, Peter; Felzer, Karen; van der Elst, Nicholas; Madden, Christopher; Arrowsmith, Ramon; Werner, Maximillan J.; Thatcher, Wayne R.
2017-01-01
Probabilistic forecasting of earthquake‐producing fault ruptures informs all major decisions aimed at reducing seismic risk and improving earthquake resilience. Earthquake forecasting models rely on two scales of hazard evolution: long‐term (decades to centuries) probabilities of fault rupture, constrained by stress renewal statistics, and short‐term (hours to years) probabilities of distributed seismicity, constrained by earthquake‐clustering statistics. Comprehensive datasets on both hazard scales have been integrated into the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3). UCERF3 is the first model to provide self‐consistent rupture probabilities over forecasting intervals from less than an hour to more than a century, and it is the first capable of evaluating the short‐term hazards that result from multievent sequences of complex faulting. This article gives an overview of UCERF3, illustrates the short‐term probabilities with aftershock scenarios, and draws some valuable scientific conclusions from the modeling results. In particular, seismic, geologic, and geodetic data, when combined in the UCERF3 framework, reject two types of fault‐based models: long‐term forecasts constrained to have local Gutenberg–Richter scaling, and short‐term forecasts that lack stress relaxation by elastic rebound.
Earthquake forecasting: a possible solution considering the GPS ionospheric delay
M. De Agostino
2011-12-01
Full Text Available The recent earthquakes in L'Aquila (Italy and in Japan have dramatically emphasized the problem of natural disasters and their correct forecasting. One of the aims of the research community is to find a possible and reliable forecasting method, considering all the available technologies and tools. Starting from the recently developed research concerning this topic and considering that the number of GPS reference stations around the world is continuously increasing, this study is an attempt to investigate whether it is possible to use GPS data in order to enhance earthquake forecasting. In some cases, ionospheric activity level increases just before to an earthquake event and shows a different behaviour 5–10 days before the event, when the seismic event has a magnitude greater than 4–4.5 degrees. Considering the GPS data from the reference stations located around the L'Aquila area (Italy, an analysis of the daily variations of the ionospheric signal delay has been carried out in order to evaluate a possible correlation between seismic events and unexpected variations of ionospheric activities. Many different scenarios have been tested, in particular considering the elevation angles, the visibility lengths and the time of day (morning, afternoon or night of the satellites. In this paper, the contribution of the ionospheric impact has been shown: a realistic correlation between ionospheric delay and earthquake can be seen about one week before the seismic event.
Earthquake forecasting studies using radon time series data in Taiwan
Walia, Vivek; Kumar, Arvind; Fu, Ching-Chou; Lin, Shih-Jung; Chou, Kuang-Wu; Wen, Kuo-Liang; Chen, Cheng-Hong
2017-04-01
For few decades, growing number of studies have shown usefulness of data in the field of seismogeochemistry interpreted as geochemical precursory signals for impending earthquakes and radon is idendified to be as one of the most reliable geochemical precursor. Radon is recognized as short-term precursor and is being monitored in many countries. This study is aimed at developing an effective earthquake forecasting system by inspecting long term radon time series data. The data is obtained from a network of radon monitoring stations eastblished along different faults of Taiwan. The continuous time series radon data for earthquake studies have been recorded and some significant variations associated with strong earthquakes have been observed. The data is also examined to evaluate earthquake precursory signals against environmental factors. An automated real-time database operating system has been developed recently to improve the data processing for earthquake precursory studies. In addition, the study is aimed at the appraisal and filtrations of these environmental parameters, in order to create a real-time database that helps our earthquake precursory study. In recent years, automatic operating real-time database has been developed using R, an open source programming language, to carry out statistical computation on the data. To integrate our data with our working procedure, we use the popular and famous open source web application solution, AMP (Apache, MySQL, and PHP), creating a website that could effectively show and help us manage the real-time database.
Lessons of L'Aquila for Operational Earthquake Forecasting
Jordan, T. H.
2012-12-01
The L'Aquila earthquake of 6 Apr 2009 (magnitude 6.3) killed 309 people and left tens of thousands homeless. The mainshock was preceded by a vigorous seismic sequence that prompted informal earthquake predictions and evacuations. In an attempt to calm the population, the Italian Department of Civil Protection (DPC) convened its Commission on the Forecasting and Prevention of Major Risk (MRC) in L'Aquila on 31 March 2009 and issued statements about the hazard that were widely received as an "anti-alarm"; i.e., a deterministic prediction that there would not be a major earthquake. On October 23, 2012, a court in L'Aquila convicted the vice-director of DPC and six scientists and engineers who attended the MRC meeting on charges of criminal manslaughter, and it sentenced each to six years in prison. A few weeks after the L'Aquila disaster, the Italian government convened an International Commission on Earthquake Forecasting for Civil Protection (ICEF) with the mandate to assess the status of short-term forecasting methods and to recommend how they should be used in civil protection. The ICEF, which I chaired, issued its findings and recommendations on 2 Oct 2009 and published its final report, "Operational Earthquake Forecasting: Status of Knowledge and Guidelines for Implementation," in Aug 2011 (www.annalsofgeophysics.eu/index.php/annals/article/view/5350). As defined by the Commission, operational earthquake forecasting (OEF) involves two key activities: the continual updating of authoritative information about the future occurrence of potentially damaging earthquakes, and the officially sanctioned dissemination of this information to enhance earthquake preparedness in threatened communities. Among the main lessons of L'Aquila is the need to separate the role of science advisors, whose job is to provide objective information about natural hazards, from that of civil decision-makers who must weigh the benefits of protective actions against the costs of false alarms
Field, Ned; Milner, Kevin R.; Hardebeck, Jeanne L.; Page, Morgan T.; van der Elst, Nicholas; Jordan, Thomas H.; Michael, Andrew J.; Shaw, Bruce E.; Werner, Maximillan J.
2017-01-01
We, the ongoing Working Group on California Earthquake Probabilities, present a spatiotemporal clustering model for the Third Uniform California Earthquake Rupture Forecast (UCERF3), with the goal being to represent aftershocks, induced seismicity, and otherwise triggered events as a potential basis for operational earthquake forecasting (OEF). Specifically, we add an epidemic‐type aftershock sequence (ETAS) component to the previously published time‐independent and long‐term time‐dependent forecasts. This combined model, referred to as UCERF3‐ETAS, collectively represents a relaxation of segmentation assumptions, the inclusion of multifault ruptures, an elastic‐rebound model for fault‐based ruptures, and a state‐of‐the‐art spatiotemporal clustering component. It also represents an attempt to merge fault‐based forecasts with statistical seismology models, such that information on fault proximity, activity rate, and time since last event are considered in OEF. We describe several unanticipated challenges that were encountered, including a need for elastic rebound and characteristic magnitude–frequency distributions (MFDs) on faults, both of which are required to get realistic triggering behavior. UCERF3‐ETAS produces synthetic catalogs of M≥2.5 events, conditioned on any prior M≥2.5 events that are input to the model. We evaluate results with respect to both long‐term (1000 year) simulations as well as for 10‐year time periods following a variety of hypothetical scenario mainshocks. Although the results are very plausible, they are not always consistent with the simple notion that triggering probabilities should be greater if a mainshock is located near a fault. Important factors include whether the MFD near faults includes a significant characteristic earthquake component, as well as whether large triggered events can nucleate from within the rupture zone of the mainshock. Because UCERF3‐ETAS has many sources of uncertainty, as
The Establishment of an Operational Earthquake Forecasting System in Italy
Marzocchi, Warner; Lombardi, Anna Maria; Casarotti, Emanuele
2014-05-01
Just after the Mw 6.2 earthquake that hit L'Aquila, on April 6 2009, the Civil Protection nominated an International Commission on Earthquake Forecasting (ICEF) that paved the way to the development of the Operational Earthquake Forecasting (OEF), defined as the "procedures for gathering and disseminating authoritative information about the time dependence of seismic hazards to help communities prepare for potentially destructive earthquakes". In this paper we introduce the first official OEF system in Italy that has been developed by the new-born Centro di Pericolosità Sismica at the Istituto Nazionale di Geofisica e Vulcanologia. The system provides every day an update of the weekly probabilities of ground shaking over the whole Italian territory. In this presentation, we describe in detail the philosophy behind the system, the scientific details, and the output format that has been preliminary defined in agreement with Civil Protection. To our knowledge, this is the first operational system that fully satisfies the ICEF guidelines. Probably, the most sensitive issue is related to the communication of such a kind of message to the population. Acknowledging this inherent difficulty, in agreement with Civil Protection we are planning pilot tests to be carried out in few selected areas in Italy; the purpose of such tests is to check the effectiveness of the message and to receive feedbacks.
Ohta, Yusaku; Kobayashi, Tatsuya; Tsushima, Hiroaki; Miura, Satoshi; Hino, Ryota; Takasu, Tomoji; Fujimoto, Hiromi; Iinuma, Takeshi; Tachibana, Kenji; Demachi, Tomotsugu; Sato, Toshiya; Ohzono, Mako; Umino, Norihito
2012-02-01
Real-time crustal deformation monitoring is extremely important for achieving rapid understanding of actual earthquake scales, because the measured permanent displacement directly gives the true earthquake size (seismic moment, Mw) information, which in turn, provides tsunami forecasting. We have developed an algorithm to detect/estimate static ground displacements due to earthquake faulting from real-time kinematic GPS (RTK-GPS) time series. The new algorithm identifies permanent displacements by monitoring the difference of a short-term average (STA) to a long-term average (LTA) of the GPS time series. We assessed the noise property and precision of the RTK-GPS time series with various baseline length conditions and orbits and discerned that the real-time ephemerides based on the International GNSS Service (IGS) are sufficient for crustal deformation monitoring with long baselines up to ˜1,000 km. We applied the algorithm to data obtained in the 2011 off the Pacific coast of Tohoku earthquake (Mw 9.0) to test the possibility of coseismic displacement detections, and further, we inverted the obtained displacement fields for a fault model; the inversion estimated a fault model with Mw 8.7, which is close to the actual Mw of 9.0, within five minutes from the origin time. Once the fault model is estimated, tsunami waveforms can be immediately synthesized using pre-computed tsunami Green's functions. The calculated waveforms showed good agreement with the actual tsunami observations both in arrival times and wave heights, suggesting that the RTK-GPS data by our algorithm can provide reliable rapid tsunami forecasting that can complement existing tsunami forecasting systems based on seismic observations.
Ramin Sadeghian
2010-06-01
Full Text Available Earthquakes are natural phenomena that can be viewed in three dimensions: time, space and magnitude. Earthquakes can be investigated not only physically, but also mathematically. In this study, semi-Markov models are applied, which can be considered as useful methods to analyze and forecast the occurrence of future earthquakes based on previous earthquake data. In the present study, the target region, Iran, is divided into zones, and each zone is examined as one of the semi-Markov model states. Several methods to determine the levels of forecasting error are then introduced and applied to the target area. The results of the application of these semi-Markov models to investigate and forecast the occurrence of future earthquakes are obtained and analyzed mathematically. A new zoning method is developed and compared with that of Karakaisis, through the proposed forecasting method. Moreover, the effects of the type of zoning and the number of zones on the forecasting error of the next earthquake occurrences are investigated using several algorithms.
Goltz, J. D.
2016-12-01
Although variants of both earthquake early warning and short-term operational earthquake forecasting systems have been implemented or are now being implemented in some regions and nations, they have been slow to gain acceptance within the disciplines that produced them as well as among those for whom they were intended to assist. To accelerate the development and implementation of these technologies will require the cooperation and collaboration of multiple disciplines, some inside and others outside of academia. Seismologists, social scientists, emergency managers, elected officials and key opinion leaders from the media and public must be the participants in this process. Representatives of these groups come from both inside and outside of academia and represent very different organizational cultures, backgrounds and expectations for these systems, sometimes leading to serious disagreements and impediments to further development and implementation. This presentation will focus on examples of the emergence of earthquake early warning and operational earthquake forecasting systems in California, Japan and other regions and document the challenges confronted in the ongoing effort to improve seismic safety.
Support vector machine method for forecasting future strong earthquakes in Chinese mainland
无
2006-01-01
Statistical learning theory is for small-sample statistics. And support vector machine is a new machine learning method based on the statistical learning theory. The support vector machine not only has solved certain problems in many learning methods, such as small sample, over fitting, high dimension and local minimum, but also has a higher generalization (forecasting) ability than that of artificial neural networks. The strong earthquakes in Chinese mainland are related to a certain extent to the intensive seismicity along the main plate boundaries in the world,however, the relation is nonlinear. In the paper, we have studied this unclear relation by the support vector machine method for the purpose of forecasting strong earthquakes in Chinese mainland.
Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre
Filgueira, Rosa; Atkinson, Malcolm; Bell, Andrew; Main, Ian; Boon, Steven; Meredith, Philip
2013-04-01
Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in
Retrospective evaluation of the five-year and ten-year CSEP-Italy earthquake forecasts
Stefan Wiemer
2010-11-01
Full Text Available On August 1, 2009, the global Collaboratory for the Study of Earthquake Predictability (CSEP launched a prospective and comparative earthquake predictability experiment in Italy. The goal of this CSEP-Italy experiment is to test earthquake occurrence hypotheses that have been formalized as probabilistic earthquake forecasts over temporal scales that range from days to years. In the first round of forecast submissions, members of the CSEP-Italy Working Group presented 18 five-year and ten-year earthquake forecasts to the European CSEP Testing Center at ETH Zurich. We have considered here the twelve time-independent earthquake forecasts among this set, and evaluated them with respect to past seismicity data from two Italian earthquake catalogs. We present the results of the tests that measure the consistencies of the forecasts according to past observations. As well as being an evaluation of the time-independent forecasts submitted, this exercise provides insight into a number of important issues in predictability experiments with regard to the specification of the forecasts, the performance of the tests, and the trade-off between robustness of results and experiment duration. We conclude with suggestions for the design of future earthquake predictability experiments.
Long- and Short-Term Earthquake Forecasts during the Tohoku Sequence
Kagan, Yan Y
2012-01-01
We consider two issues related to the 2011 Tohoku mega-earthquake: (1) what is the repeat time for the largest earthquakes in this area, and (2) what are the possibilities of numerical short-term forecasts during the 2011 earthquake sequence in the Tohoku area. Starting in 1999 we have carried out long- and short-term forecasts for Japan and the surrounding areas using the GCMT catalog. The forecasts predict the earthquake rate per area, time, magnitude unit and earthquake focal mechanisms. Long-term forecasts indicate that the repeat time for the m9 earthquake in the Tohoku area is of the order of 350 years. We have archived several forecasts made before and after the Tohoku earthquake. The long-term rate estimates indicate that, as expected, the forecasted rate changed only by a few percent after the Tohoku earthquake, whereas due to the foreshocks, the short-term rate increased by a factor of more than 100 before the mainshock event as compared to the long-term rate. After the Tohoku mega-earthquake the ra...
Scientific and non-scientific challenges for Operational Earthquake Forecasting
Marzocchi, W.
2015-12-01
Tracking the time evolution of seismic hazard in time windows shorter than the usual 50-years of long-term hazard models may offer additional opportunities to reduce the seismic risk. This is the target of operational earthquake forecasting (OEF). During the OEF development in Italy we identify several challenges that range from pure science to the more practical interface of science with society. From a scientific point of view, although earthquake clustering is the clearest empirical evidence about earthquake occurrence, and OEF clustering models are the most (successfully) tested hazard models in seismology, we note that some seismologists are still reluctant to accept their scientific reliability. After exploring the motivations of these scientific doubts, we also look into an issue that is often overlooked in this discussion, i.e., in any kind of hazard analysis, we do not use a model because it is the true one, but because it is the better than anything else we can think of. The non-scientific aspects are mostly related to the fact that OEF usually provides weekly probabilities of large eartquakes smaller than 1%. These probabilities are considered by some seismologists too small to be of interest or useful. However, in a recent collaboration with engineers we show that such earthquake probabilities may lead to intolerable individual risk of death. Interestingly, this debate calls for a better definition of the still fuzzy boundaries among the different expertise required for the whole risk mitigation process. The last and probably more pressing challenge is related to the communication to the public. In fact, a wrong message could be useless or even counterproductive. Here we show some progresses that we have made in this field working with communication experts in Italy.
Chen, Kejie; Babeyko, Andrey; Hoechner, Andreas; Ge, Maorong
2016-04-01
Real-time GPS is nowadays considered as a valuable component of next generation near-field tsunami early warning systems able to provide fast and reliable source parameters. Looking for optimal methodologies and assessing corresponding uncertainties becomes an important task. We take the opportunity and consider the 2014 Pisagua event as a case study to explore tsunami forecast uncertainty related to the GPS-based source inversion. We intentionally neglect all other sources of uncertainty (observation set, signal processing, wave simulation, etc.) and exclusively assess the effect of inversion technique. In particular, we compare three end-member methods: (1) point-source fastCMT (centroid moment tensor), (2) distributed slip along predefined plate interface, and (3) unconstrained inversion into a single uniform slip finite fault. The three methods provide significantly different far-field tsunami forecast but show surprisingly similar tsunami predictions in the near field.
UCERF3: A new earthquake forecast for California's complex fault system
Field, Edward H.; ,
2015-01-01
With innovations, fresh data, and lessons learned from recent earthquakes, scientists have developed a new earthquake forecast model for California, a region under constant threat from potentially damaging events. The new model, referred to as the third Uniform California Earthquake Rupture Forecast, or "UCERF" (http://www.WGCEP.org/UCERF3), provides authoritative estimates of the magnitude, location, and likelihood of earthquake fault rupture throughout the state. Overall the results confirm previous findings, but with some significant changes because of model improvements. For example, compared to the previous forecast (Uniform California Earthquake Rupture Forecast 2), the likelihood of moderate-sized earthquakes (magnitude 6.5 to 7.5) is lower, whereas that of larger events is higher. This is because of the inclusion of multifault ruptures, where earthquakes are no longer confined to separate, individual faults, but can occasionally rupture multiple faults simultaneously. The public-safety implications of this and other model improvements depend on several factors, including site location and type of structure (for example, family dwelling compared to a long-span bridge). Building codes, earthquake insurance products, emergency plans, and other risk-mitigation efforts will be updated accordingly. This model also serves as a reminder that damaging earthquakes are inevitable for California. Fortunately, there are many simple steps residents can take to protect lives and property.
Segou, Margaret; Parsons, Thomas E.
2016-01-01
When a major earthquake strikes, the resulting devastation can be compounded or even exceeded by the subsequent cascade of triggered seismicity. As the Nepalese recover from the 25 April 2015 shock, knowledge of what comes next is essential. We calculate the redistribution of crustal stresses and implied earthquake probabilities for different periods, from daily to 30 years into the future. An initial forecast was completed before an M 7.3 earthquake struck on 12 May 2015 that enables a preliminary assessment; postforecast seismicity has so far occurred within a zone of fivefold probability gain. Evaluation of the forecast performance, using two months of seismic data, reveals that stress‐based approaches present improved skill in higher‐magnitude triggered seismicity. Our results suggest that considering the total stress field, rather than only the coseismic one, improves the spatial performance of the model based on the estimation of a wide range of potential triggered faults following a mainshock.
Trendafiloski, G.; Gaspa Rebull, O.; Ewing, C.; Podlaha, A.; Magee, B.
2012-04-01
Calibration and validation are crucial steps in the production of the catastrophe models for the insurance industry in order to assure the model's reliability and to quantify its uncertainty. Calibration is needed in all components of model development including hazard and vulnerability. Validation is required to ensure that the losses calculated by the model match those observed in past events and which could happen in future. Impact Forecasting, the catastrophe modelling development centre of excellence within Aon Benfield, has recently launched its earthquake model for Algeria as a part of the earthquake model for the Maghreb region. The earthquake model went through a detailed calibration process including: (1) the seismic intensity attenuation model by use of macroseismic observations and maps from past earthquakes in Algeria; (2) calculation of the country-specific vulnerability modifiers by use of past damage observations in the country. The use of Benouar, 1994 ground motion prediction relationship was proven as the most appropriate for our model. Calculation of the regional vulnerability modifiers for the country led to 10% to 40% larger vulnerability indexes for different building types compared to average European indexes. The country specific damage models also included aggregate damage models for residential, commercial and industrial properties considering the description of the buildings stock given by World Housing Encyclopaedia and the local rebuilding cost factors equal to 10% for damage grade 1, 20% for damage grade 2, 35% for damage grade 3, 75% for damage grade 4 and 100% for damage grade 5. The damage grades comply with the European Macroseismic Scale (EMS-1998). The model was validated by use of "as-if" historical scenario simulations of three past earthquake events in Algeria M6.8 2003 Boumerdes, M7.3 1980 El-Asnam and M7.3 1856 Djidjelli earthquake. The calculated return periods of the losses for client market portfolio align with the
Clements, Robert Alan; Schorlemmer, Danijel; 10.1214/11-AOAS487
2012-01-01
Modern, powerful techniques for the residual analysis of spatial-temporal point process models are reviewed and compared. These methods are applied to California earthquake forecast models used in the Collaboratory for the Study of Earthquake Predictability (CSEP). Assessments of these earthquake forecasting models have previously been performed using simple, low-power means such as the L-test and N-test. We instead propose residual methods based on rescaling, thinning, superposition, weighted K-functions and deviance residuals. Rescaled residuals can be useful for assessing the overall fit of a model, but as with thinning and superposition, rescaling is generally impractical when the conditional intensity $\\lambda$ is volatile. While residual thinning and superposition may be useful for identifying spatial locations where a model fits poorly, these methods have limited power when the modeled conditional intensity assumes extremely low or high values somewhere in the observation region, and this is commonly t...
Problem and Improvement of R-values Applied to Assessment of Earthquake Forecast
Wang Xiaoqing
2001-01-01
The researches on the assessment of earthquake forecast are reviewed, then the R-value assessment is further developed theoretically in the paper. The results include the arithmetic of the R-values of earthquake occurrence under the condition that "anomaly" occurred or no "anomaly" occurred respectively, and the relation between the values. The distribution of Rvalue of a forecast method, corresponding to multi-status anomalies being independent each other, is also developed in the paper. The appropriate methods to estimate the R-values and extrapolate the occurrence probability of future earthquakes are also given in the paper.
Earthquake Forecasts for Gorkha Immediately Following the 25th April, M=7.8 Mainshock
Segkou, M.; Parsons, T.
2015-12-01
The M-7.8 Gorkha (Nepal) earthquake on the 25th April, 2015 has shaken the central Himalayan front and immediately raised concerns for the severity of future triggered earthquakes. Here, we implement standard and innovative forecast models to predict the spatio-temporal distribution of triggered events. Key challenges addressed are: 1) the limited information on early aftershocks, 2) the low-productivity aftershock sequence in the near-source area, 3) the off-fault (>250 km) triggered events exemplified by the M=5.4 Xegar event, 3 hrs after the mainshock. We apply short-term empirical/statistical ETAS and physical forecast models, the latter based on the combination of rate/state friction law and Coulomb stresses. Within the physics-based model implementation we seek to evaluate the uncertainty related with the rupture style of triggered events by considering: 1) the geometry of active structures, 2) optimally oriented for failure faults and 3) all-potential faults described by the total stress field. The latter is represented by the full stress tensor before and after the mainshock and our analysis suggests that the preseismic stress magnitudes are still sufficient to cause earthquakes even after modification by the mainshock. The above remark reveals that there are no "stress shadows" affecting the spatial distribution of near-field aftershocks. It is also noted that the method allows for an a-priori determination of the rupture plan of the M=7.3 event, within the limit of uncertainty (20˚). The results show that: (1) ETAS models underestimate the number of observed events, since they heavily base their good performance in small magnitude earthquakes, not available in the first few weeks after the mainshock, (2) far field triggered events are captured only by physics-based forecasts, and (3) the total stress method improves the predictability of larger magnitude events. We conclude that frontier regions benefit from the implementation of physics-based models
Ogata, Y.
2014-12-01
In our previous papers (Ogata et al., 1995, 1996, 2012; GJI), we characterized foreshock activity in Japan, and then presented a model that forecasts the probability that one or more earthquakes form a foreshock sequence; then we tested prospectively foreshock probabilities in the JMA catalog. In this talk, I compare the empirical results with results for synthetic catalogs in order to clarify whether or not these results are consistent with the description of the seismicity by a superposition of background activity and epidemic-type aftershock sequences (ETAS models). This question is important, because it is still controversially discussed whether the nucleation process of large earthquakes is driven by seismically cascading (ETAS-type) or by aseismic accelerating processes. To explore the foreshock characteristics, I firstly applied the same clustering algorithms to real and synthetic catalogs and analyzed the temporal, spatial and magnitude distributions of the selected foreshocks, to find significant differences particularly in the temporal acceleration and magnitude dependence. Finally, I calculated forecast scores based on a single-link cluster algorithm which could be appropriate for real-time applications. I find that the JMA catalog yields higher scores than all synthetic catalogs and that the ETAS models having the same magnitude sequence as the original catalog performs significantly better (more close to the reality) than ETAS-models with randomly picked magnitudes.
Francesco Visini
2010-11-01
Full Text Available The Collaboratory for the Study of Earthquake Predictability (CSEP selected Italy as a testing region for probabilistic earthquake forecast models in October, 2008. The model we have submitted for the two medium-term forecast periods of 5 and 10 years (from 2009 is a time-dependent, geologically based earthquake rupture forecast that is defined for central Italy only (11-15˚ E; 41-45˚ N. The model took into account three separate layers of seismogenic sources: background seismicity; seismotectonic provinces; and individual faults that can produce major earthquakes (seismogenic boxes. For CSEP testing purposes, the background seismicity layer covered a range of magnitudes from 5.0 to 5.3 and the seismicity rates were obtained by truncated Gutenberg-Richter relationships for cells centered on the CSEP grid. Then the seismotectonic provinces layer returned the expected rates of medium-to-large earthquakes following a traditional Cornell-type approach. Finally, for the seismogenic boxes layer, the rates were based on the geometry and kinematics of the faults that different earthquake recurrence models have been assigned to, ranging from pure Gutenberg-Richter behavior to characteristic events, with the intermediate behavior named as the hybrid model. The results for different magnitude ranges highlight the contribution of each of the three layers to the total computation. The expected rates for M >6.0 on April 1, 2009 (thus computed before the L'Aquila, 2009, MW= 6.3 earthquake are of particular interest. They showed local maxima in the two seismogenic-box sources of Paganica and Sulmona, one of which was activated by the L'Aquila earthquake of April 6, 2009. Earthquake rates as of August 1, 2009, (now under test also showed a maximum close to the Sulmona source for MW ~6.5; significant seismicity rates (10-4 to 10-3 in 5 years for destructive events (magnitude up to 7.0 were located in other individual sources identified as being capable of such
Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast
Jinyin Ye; Yuehong Shao; Zhijia Li
2016-01-01
TIGGE (THORPEX International Grand Global Ensemble) was a major part of the THORPEX (Observing System Research and Predictability Experiment). It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability fo...
Chance of damage from an earthquake in 2016 based on peak ground acceleration
U.S. Geological Survey, Department of the Interior — A one-year seismic hazard forecast for the Central and Eastern United States, based on induced and natural earthquakes, has been produced by the U.S. Geological...
Forecast of Large Earthquakes Through Semi-periodicity Analysis of Labeled Point Processes
Quinteros Cartaya, C. B.; Nava Pichardo, F. A.; Glowacka, E.; Gómez Treviño, E.; Dmowska, R.
2016-08-01
Large earthquakes have semi-periodic behavior as a result of critically self-organized processes of stress accumulation and release in seismogenic regions. Hence, large earthquakes in a given region constitute semi-periodic sequences with recurrence times varying slightly from periodicity. In previous papers, it has been shown that it is possible to identify these sequences through Fourier analysis of the occurrence time series of large earthquakes from a given region, by realizing that not all earthquakes in the region need belong to the same sequence, since there can be more than one process of stress accumulation and release in the region. Sequence identification can be used to forecast earthquake occurrence with well determined confidence bounds. This paper presents improvements on the above mentioned sequence identification and forecasting method: the influence of earthquake size on the spectral analysis, and its importance in semi-periodic events identification are considered, which means that earthquake occurrence times are treated as a labeled point process; a revised estimation of non-randomness probability is used; a better estimation of appropriate upper limit uncertainties to use in forecasts is introduced; and the use of Bayesian analysis to evaluate the posterior forecast performance is applied. This improved method was successfully tested on synthetic data and subsequently applied to real data from some specific regions. As an example of application, we show the analysis of data from the northeastern Japan Arc region, in which one semi-periodic sequence of four earthquakes with M ≥ 8.0, having high non-randomness probability was identified. We compare the results of this analysis with those of the unlabeled point process analysis.
Parsons, T.
2009-12-01
After a large earthquake, our concern immediately moves to the likelihood that another large shock could be triggered, threatening an already weakened building stock. A key question is whether it is best to map out Coulomb stress change calculations shortly after mainshocks to potentially highlight the most likely aftershock locations, or whether it is more prudent to wait until the best information is available. It has been shown repeatedly that spatial aftershock patterns can be matched with Coulomb stress change calculations a year or more after mainshocks. However, with the onset of rapid source slip model determinations, the method has produced encouraging results like the M=8.7 earthquake that was forecast using stress change calculations from 2004 great Sumatra earthquake by McCloskey et al. [2005]. Here, I look back at two additional prospective calculations published shortly after the 2005 M=7.6 Kashmir and 2008 M=8.0 Wenchuan earthquakes. With the benefit of 1.5-4 years of additional seismicity, it is possible to assess the performance of rapid Coulomb stress change calculations. In the second part of the talk, within the context of the ongoing Working Group on California Earthquake Probabilities (WGCEP) assessments, uncertainties associated with time-dependent probability calculations are convolved with uncertainties inherent to Coulomb stress change calculations to assess the strength of signal necessary for a physics-based calculation to merit consideration into a formal earthquake forecast. Conclusions are as follows: (1) subsequent aftershock occurrence shows that prospective static stress change calculations both for Kashmir and Wenchuan examples failed to adequately predict the spatial post-mainshock earthquake distributions. (2) For a San Andreas fault example with relatively well-understood recurrence, a static stress change on the order of 30 to 40 times the annual stressing rate would be required to cause a significant (90%) perturbation to the
Neural Network based Consumption Forecasting
Madsen, Per Printz
2016-01-01
This paper describe a Neural Network based method for consumption forecasting. This work has been financed by the The ENCOURAGE project. The aims of The ENCOURAGE project is to develop embedded intelligence and integration technologies that will directly optimize energy use in buildings and enable...
Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast
Jinyin Ye
2016-01-01
Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.
Wavelet-based Evapotranspiration Forecasts
Bachour, R.; Maslova, I.; Ticlavilca, A. M.; McKee, M.; Walker, W.
2012-12-01
Providing a reliable short-term forecast of evapotranspiration (ET) could be a valuable element for improving the efficiency of irrigation water delivery systems. In the last decade, wavelet transform has become a useful technique for analyzing the frequency domain of hydrological time series. This study shows how wavelet transform can be used to access statistical properties of evapotranspiration. The objective of the research reported here is to use wavelet-based techniques to forecast ET up to 16 days ahead, which corresponds to the LANDSAT 7 overpass cycle. The properties of the ET time series, both physical and statistical, are examined in the time and frequency domains. We use the information about the energy decomposition in the wavelet domain to extract meaningful components that are used as inputs for ET forecasting models. Seasonal autoregressive integrated moving average (SARIMA) and multivariate relevance vector machine (MVRVM) models are coupled with the wavelet-based multiresolution analysis (MRA) results and used to generate short-term ET forecasts. Accuracy of the models is estimated and model robustness is evaluated using the bootstrap approach.
A way to synchronize models with seismic faults for earthquake forecasting
González, Á.; Gómez, J.B.; Vázquez-Prada, M.
2006-01-01
Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual f...
Bayesian forecasting of recurrent earthquakes and predictive performance for a small sample size
Nomura, S.; Ogata, Y.; Komaki, F.; Toda, S.
2011-04-01
This paper presents a Bayesian method of probability forecasting for a renewal of earthquakes. When only limited records of characteristic earthquakes on a fault are available, relevant prior distributions for renewal model parameters are essential to computing unbiased, stable time-dependent earthquake probabilities. We also use event slip and geological slip rate data combined with historical earthquake records to improve our forecast model. We apply the Brownian Passage Time (BPT) model and make use of the best fit prior distribution for its coefficient of variation (the shape parameter, alpha) relative to the mean recurrence time because the Earthquake Research Committee (ERC) of Japan uses the BPT model for long-term forecasting. Currently, more than 110 active faults have been evaluated by the ERC, but most include very few paleoseismic events. We objectively select the prior distribution with the Akaike Bayesian Information Criterion using all available recurrence data including the ERC datasets. These data also include mean recurrence times estimated from slip per event divided by long-term slip rate. By comparing the goodness of fit to the historical record and simulated data, we show that the proposed predictor provides more stable performance than plug-in predictors, such as maximum likelihood estimates and the predictor currently adopted by the ERC.
Study on medium-short term earthquake forecast in Yunnan Province by precursory events
秦嘉政; 钱晓东
2004-01-01
The medium-short term forecast for a certain kinds of main earthquake events might be possible with the time-to-failure method presented by Varnes (1989), Bufe and Varnes (1993), which is to simulate an accelerative releasing model of precursory earthquake energy. By fitting the observed data with the theoretical formula, a medium-short term forecast technique for the main shock events could be established, by which the location, time and magnitude of the main shock could be determined. The data used in the paper are obtained from the earthquake catalogue recorded by Yunnan Regional Seismological Network with a time coverage of 1965～2002. The statistical analyses for the past 37 years show that the data of M≥2.5 earthquakes were fairly complete. In the present paper,30 main shocks occurred in Yunnan region were simulated. For 25 of them, the forecasting time and magnitude from the simulation of precursory sequence are very close to the actual values with the precision of about ±0.57(magnitude unit). Suppose that the last event of the precursory sequence is known, then the time error for the forecasting main shock is about ±0.64 year. For the other 5 main shocks, the simulation cannot be made due to the insufficient precursory events for the full determination of energy accelerating curve or disturbance to the energy-release curve. The results in the paper indicate that there is no obviously linear relation in the optimal searching radius for the main shock and the precursory events because Yunnan is an active region with damage earthquakes and moderate and small earthquakes. However, there is a strong correlation between the main shock moment and the coefficient k/m. The "optimal fitting range" for the forecasting time and magnitude can be further reduced using the relation between the main shock moment lgM0 and the coefficient lgk/m and the value range of the restricting index m, by which the forecast precision of the simulated main shock can be improved
Long-Term Probabilistic Forecast for M ≥ 5.0 Earthquakes in Iran
Talebi, Mohammad; Zare, Mehdi; Peresan, Antonella; Ansari, Anooshiravan
2017-03-01
In this study, a long-term forecasting model is proposed to evaluate the probabilities of forthcoming M ≥ 5.0 earthquakes on a 0.2° grid for an area including the Iranian plateau. The model is built basically from smoothing the locations of preceding events, assuming a spatially heterogeneous and temporally homogeneous Poisson point process for seismicity. In order to calculate the expectations, the space distribution, from adaptively smoothed seismicity, has been scaled in time and magnitude by average number of events over a 5-year forecasting horizon and a tapered magnitude distribution, respectively. The model has been adjusted and applied considering two earthquake datasets: a regional unified catalog (MB14) and a global catalog (ISC). Only the events with M ≥ 4.5 have been retained from the datasets, based on preliminary completeness data analysis. A set of experiments has been carried out, testing different options in the model application, and the average probability gains for target earthquakes have been estimated. By optimizing the model parameters, which leads to increase of the predictive power of the model, it is shown that a declustered catalog has an advantage over a non-declustered one, and a low-magnitude threshold of a learning catalog can be preferred to a larger one. In order to examine the significance of the model results at 95% confidence level, a set of retrospective tests, namely, the L test, the N test, the R test, and the error diagram test, has been performed considering 13 target time windows. The error diagram test shows that the forecast results, obtained for both the two input catalogs, mostly fall outside the 5% critical region that is related to results from a random guess. The L test and the N test could not reject the model for most of the time intervals (i.e. 85 and 62% of times for the ISC and MB14 forecasts, respectively). Furthermore, after backwards extending the time span of the learning catalogs and repeating the L
Field, E. H.; Arrowsmith, R.; Biasi, G. P.; Bird, P.; Dawson, T. E.; Felzer, K. R.; Jackson, D. D.; Johnson, K. M.; Jordan, T. H.; Madugo, C. M.; Michael, A. J.; Milner, K. R.; Page, M. T.; Parsons, T.; Powers, P.; Shaw, B. E.; Thatcher, W. R.; Weldon, R. J.; Zeng, Y.
2013-12-01
We present the time-independent component of the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), where the primary achievements have been to relax fault segmentation and include multi-fault ruptures, both limitations of UCERF2. The rates of all earthquakes are solved for simultaneously, and from a broader range of data, using a system-level 'grand inversion' that is both conceptually simple and extensible. The inverse problem is large and underdetermined, so a range of models is sampled using an efficient simulated annealing algorithm. The approach is more derivative than prescriptive (e.g., magnitude-frequency distributions are no longer assumed), so new analysis tools were developed for exploring solutions. Epistemic uncertainties were also accounted for using 1440 alternative logic tree branches, necessitating access to supercomputers. The most influential uncertainties include alternative deformation models (fault slip rates), a new smoothed seismicity algorithm, alternative values for the total rate of M≥5 events, and different scaling relationships, virtually all of which are new. As a notable first, three deformation models are based on kinematically consistent inversions of geodetic and geologic data, also providing slip-rate constraints on faults previously excluded due to lack of geologic data. The grand inversion constitutes a system-level framework for testing hypotheses and balancing the influence of different experts. For example, we demonstrate serious challenges with the Gutenberg-Richter hypothesis for individual faults. UCERF3 is still an approximation of the system, however, and the range of models is limited (e.g., constrained to stay close to UCERF2). Nevertheless, UCERF3 removes the apparent UCERF2 over-prediction of M6.5-7 earthquake rates, and also includes types of multi-fault ruptures seen in nature. While UCERF3 fits the data better than UCERF2 overall, there may be areas that warrant further site
Effect of tectonic setting on the fit and performance of a long-range earthquake forecasting model
David Alan Rhoades
2012-02-01
Full Text Available The Every Earthquake a Precursor According to Scale (EEPAS long-range earthquake forecasting model has been shown to be informative in several seismically active regions, including New Zealand, California and Japan. In previous applications of the model, the tectonic setting of earthquakes has been ignored. Here we distinguish crustal, plate interface, and slab earthquakes and apply the model to earthquakes with magnitude M≥4 in the Japan region from 1926 onwards. The target magnitude range is M≥ 6; the fitting period is 1966-1995; and the testing period is 1996-2005. In forecasting major slab earthquakes, it is optimal to use only slab and interface events as precursors. In forecasting major interface events, it is optimal to use only interface events as precursors. In forecasting major crustal events, it is optimal to use only crustal events as precursors. For the smoothed-seismicity component of the EEPAS model, it is optimal to use slab and interface events for earthquakes in the slab, interface events only for earthquakes on the interface, and crustal and interface events for crustal earthquakes. The optimal model parameters indicate that the precursor areas for slab earthquakes are relatively small compared to those for earthquakes in other tectonic categories, and that the precursor times and precursory earthquake magnitudes for crustal earthquakes are relatively large. The optimal models fit the learning data sets better than the raw EEPAS model, with an average information gain per earthquake of about 0.4. The average information gain is similar in the testing period, although it is higher for crustal earthquakes and lower for slab and interface earthquakes than in the learning period. These results show that earthquake interactions are stronger between earthquakes of similar tectonic types and that distinguishing tectonic types improves forecasts by enhancing the depth resolution where tectonic categories of earthquakes are
Earthquake Forecasted on DeepComp 6800 with LURR Theory
无
2005-01-01
@@ With a supercomputer at CAS and a model developed by Chinese scientists, a research group led by Prof. Yin Xiangchu, a guest researcher at the State Key Lab of Nonlinear Mechanics affiliated to the CAS Institute of Mechanics,correctly predicted an earthquake in south China on Sept. 11, six days before it occurred.
Petersen, M. D.; Mueller, C. S.; Moschetti, M. P.; Hoover, S. M.; Llenos, A. L.; Ellsworth, W. L.; Michael, A. J.; Rubinstein, J. L.; McGarr, A.; Rukstales, K. S.
2016-12-01
The U.S. Geological Survey released a 2016 one-year forecast for seismic hazard in the central and eastern U.S., which included the influence from both induced and natural earthquakes. This forecast was primarily based on 2015 declustered seismicity rates but also included longer-term rates, 10- and 20- km smoothing distances, earthquakes between Mw 4.7 and maximum magnitudes of 6.0 or 7.1, and 9 alternative ground motion models. Results indicate that areas in Oklahoma, Kansas, Colorado, New Mexico, Arkansas, Texas, and the New Madrid Seismic Zone have a significant chance for damaging ground shaking levels in 2016 (greater than 1% chance of exceeding 0.12 PGA and MMI VI). We evaluate this one-year forecast by considering the earthquakes and ground shaking levels that occurred during the first half of 2016 (earthquakes not included in the forecast). During this period the full catalog records hundreds of events with M ≥ 3.0, but the declustered catalog eliminates most of these dependent earthquakes and results in much lower numbers of earthquakes. The declustered catalog based on USGS COMCAT indicates a M 5.1 earthquake occurred in the zone of highest hazard on the map. Two additional earthquakes of M ≥ 4.0 occurred in Oklahoma, and about 82 earthquakes of M ≥ 3.0 occurred with 77 in Oklahoma and Kansas, 4 in Raton Basin Colorado/New Mexico, and 1 near Cogdell Texas. In addition, 72 earthquakes occurred outside the zones of induced seismicity with more than half in New Madrid and eastern Tennessee. The catalog rates in the first half of 2016 and the corresponding seismic hazard were generally lower than in 2015. For example, the zones for Irving, Venus, and Fashing, Texas; Sun City, Kansas; and north-central Arkansas did not experience any earthquakes with M≥ 2.7 during this period. The full catalog rates were lower by about 30% in Raton Basin and the Oklahoma-Kansas zones but the declustered catalog rates did not drop as much. This decrease in earthquake
Petersen, Mark D.; Mueller, Charles; Moschetti, Morgan P.; Hoover, Susan M.; Shumway, Allison; McNamara, Daniel E.; Williams, Robert; Llenos, Andrea L.; Ellsworth, William L; Rubinstein, Justin L.; McGarr, Arthur F.; Rukstales, Kenneth S.
2017-01-01
We produce a one‐year 2017 seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes that updates the 2016 one‐year forecast; this map is intended to provide information to the public and to facilitate the development of induced seismicity forecasting models, methods, and data. The 2017 hazard model applies the same methodology and input logic tree as the 2016 forecast, but with an updated earthquake catalog. We also evaluate the 2016 seismic‐hazard forecast to improve future assessments. The 2016 forecast indicated high seismic hazard (greater than 1% probability of potentially damaging ground shaking in one year) in five focus areas: Oklahoma–Kansas, the Raton basin (Colorado/New Mexico border), north Texas, north Arkansas, and the New Madrid Seismic Zone. During 2016, several damaging induced earthquakes occurred in Oklahoma within the highest hazard region of the 2016 forecast; all of the 21 moment magnitude (M) ≥4 and 3 M≥5 earthquakes occurred within the highest hazard area in the 2016 forecast. Outside the Oklahoma–Kansas focus area, two earthquakes with M≥4 occurred near Trinidad, Colorado (in the Raton basin focus area), but no earthquakes with M≥2.7 were observed in the north Texas or north Arkansas focus areas. Several observations of damaging ground‐shaking levels were also recorded in the highest hazard region of Oklahoma. The 2017 forecasted seismic rates are lower in regions of induced activity due to lower rates of earthquakes in 2016 compared with 2015, which may be related to decreased wastewater injection caused by regulatory actions or by a decrease in unconventional oil and gas production. Nevertheless, the 2017 forecasted hazard is still significantly elevated in Oklahoma compared to the hazard calculated from seismicity before 2009.
Petersen, Mark D.; Mueller, Charles; Moschetti, Morgan P.; Hoover, Susan M.; Shumway, Allison; McNamara, Daniel E.; Williams, Robert A.; Llenos, Andrea L.; Ellsworth, William L.; Michael, Andrew J.; Rubinstein, Justin L.; McGarr, Arthur F.; Rukstales, Kenneth S.
2017-01-01
We produce the 2017 one-year seismic hazard forecast for the central and eastern United States from induced and natural earthquakes that updates the 2016 one-year forecast; this map is intended to provide information to the public and to facilitate the development of induced seismicity forecasting models, methods, and data. The 2017 hazard model applies the same methodology and input logic tree as the 2016 forecast, but with an updated earthquake catalog. We also evaluate the 2016 seismic hazard forecast to improve future assessments. The 2016 forecast indicated high seismic hazard (greater than 1% probability of potentially damaging ground shaking in one-year) in five focus areas: Oklahoma-Kansas, the Raton Basin (Colorado/New Mexico border), north Texas, north Arkansas, and the New Madrid Seismic Zone. During 2016, several damaging induced earthquakes occurred in Oklahoma within the highest hazard region of the 2016 forecast; all of the 21 magnitude (M) ≥ 4 and three M ≥ 5 earthquakes occurred within the highest hazard area in the 2016 forecast. Outside the Oklahoma-Kansas focus area two earthquakes with M ≥ 4 occurred near Trinidad, Colorado (in the Raton Basin focus area), but no earthquakes with M ≥ 2.7 were observed in the north Texas or north Arkansas focus areas. Several observations of damaging ground shaking levels were also recorded in the highest hazard region of Oklahoma. The 2017 forecasted seismic rates are lower in regions of induced activity due to lower rates of earthquakes in 2016 compared to 2015, which may be related to decreased wastewater injection, caused by regulatory actions or by a decrease in unconventional oil and gas production. Nevertheless, the 2017 forecasted hazard is still significantly elevated in Oklahoma compared to the hazard calculated from seismicity before 2009.
Smartphone-Based Earthquake and Tsunami Early Warning in Chile
Brooks, B. A.; Baez, J. C.; Ericksen, T.; Barrientos, S. E.; Minson, S. E.; Duncan, C.; Guillemot, C.; Smith, D.; Boese, M.; Cochran, E. S.; Murray, J. R.; Langbein, J. O.; Glennie, C. L.; Dueitt, J.; Parra, H.
2016-12-01
Many locations around the world face high seismic hazard, but do not have the resources required to establish traditional earthquake and tsunami warning systems (E/TEW) that utilize scientific grade seismological sensors. MEMs accelerometers and GPS chips embedded in, or added inexpensively to, smartphones are sensitive enough to provide robust E/TEW if they are deployed in sufficient numbers. We report on a pilot project in Chile, one of the most productive earthquake regions world-wide. There, magnitude 7.5+ earthquakes occurring roughly every 1.5 years and larger tsunamigenic events pose significant local and trans-Pacific hazard. The smartphone-based network described here is being deployed in parallel to the build-out of a scientific-grade network for E/TEW. Our sensor package comprises a smartphone with internal MEMS and an external GPS chipset that provides satellite-based augmented positioning and phase-smoothing. Each station is independent of local infrastructure, they are solar-powered and rely on cellular SIM cards for communications. An Android app performs initial onboard processing and transmits both accelerometer and GPS data to a server employing the FinDer-BEFORES algorithm to detect earthquakes, producing an acceleration-based line source model for smaller magnitude earthquakes or a joint seismic-geodetic finite-fault distributed slip model for sufficiently large magnitude earthquakes. Either source model provides accurate ground shaking forecasts, while distributed slip models for larger offshore earthquakes can be used to infer seafloor deformation for local tsunami warning. The network will comprise 50 stations by Sept. 2016 and 100 stations by Dec. 2016. Since Nov. 2015, batch processing has detected, located, and estimated the magnitude for Mw>5 earthquakes. Operational since June, 2016, we have successfully detected two earthquakes > M5 (M5.5, M5.1) that occurred within 100km of our network while producing zero false alarms.
Uniform California earthquake rupture forecast, version 3 (UCERF3): the time-independent model
Field, Edward H.; Biasi, Glenn P.; Bird, Peter; Dawson, Timothy E.; Felzer, Karen R.; Jackson, David D.; Johnson, Kaj M.; Jordan, Thomas H.; Madden, Christopher; Michael, Andrew J.; Milner, Kevin R.; Page, Morgan T.; Parsons, Thomas; Powers, Peter M.; Shaw, Bruce E.; Thatcher, Wayne R.; Weldon, Ray J.; Zeng, Yuehua; ,
2013-01-01
In this report we present the time-independent component of the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), which provides authoritative estimates of the magnitude, location, and time-averaged frequency of potentially damaging earthquakes in California. The primary achievements have been to relax fault segmentation assumptions and to include multifault ruptures, both limitations of the previous model (UCERF2). The rates of all earthquakes are solved for simultaneously, and from a broader range of data, using a system-level "grand inversion" that is both conceptually simple and extensible. The inverse problem is large and underdetermined, so a range of models is sampled using an efficient simulated annealing algorithm. The approach is more derivative than prescriptive (for example, magnitude-frequency distributions are no longer assumed), so new analysis tools were developed for exploring solutions. Epistemic uncertainties were also accounted for using 1,440 alternative logic tree branches, necessitating access to supercomputers. The most influential uncertainties include alternative deformation models (fault slip rates), a new smoothed seismicity algorithm, alternative values for the total rate of M≥5 events, and different scaling relationships, virtually all of which are new. As a notable first, three deformation models are based on kinematically consistent inversions of geodetic and geologic data, also providing slip-rate constraints on faults previously excluded because of lack of geologic data. The grand inversion constitutes a system-level framework for testing hypotheses and balancing the influence of different experts. For example, we demonstrate serious challenges with the Gutenberg-Richter hypothesis for individual faults. UCERF3 is still an approximation of the system, however, and the range of models is limited (for example, constrained to stay close to UCERF2). Nevertheless, UCERF3 removes the apparent UCERF2 overprediction of
W. Marzocchi
2008-06-01
Full Text Available The main goal of this work is to review the scientific researches carried out before and after the Umbria-Marche sequence related to the earthquake forecasting/prediction in Italy. In particular, I focus the attention on models that aim addressing three main practical questions: was (is Umbria-Marche a region with high probability of occurrence of a destructive earthquake? Was a precursory activity recorded before the mainshock(s? What was our capability to model the spatio-temporal-magnitude evolution of that seismic sequence? The models are reviewed pointing out what we have learned after the Umbria-Marche earthquakes, in terms of physical understanding of earthquake occurrence process, and of improving our capability to forecast earthquakes and to track in real-time seismic sequences.
Kuo, T., E-mail: mctkuobe@mail.ncku.edu.t [Department of Mineral and Petroleum Engineering, National Cheng Kung University, Tainan 701, Taiwan (China); Su, C. [Department of Mineral and Petroleum Engineering, National Cheng Kung University, Tainan 701, Taiwan (China); Chang, C. [Central Weather Bureau, Taipei, Taiwan (China); Lin, C.; Cheng, W.; Liang, H.; Lewis, C. [Department of Mineral and Petroleum Engineering, National Cheng Kung University, Tainan 701, Taiwan (China); Chiang, C. [Central Geological Survey, Ministry of Economic Affairs, Taipei, Taiwan (China)
2010-10-15
Radon anomalies in groundwater were recorded prior to three major earthquakes - (1) 2003 M{sub w} = 6.8 Chengkung, (2) 2006 M{sub w} = 6.1 Taitung, and (3) 2008 M{sub w} = 5.4 Antung. The epicenters were located 24 km, 52 km, and 13 km, respectively, from the Antung radon-monitoring station. Prior to the three major earthquakes, radon decreased from background levels of 29.3 {+-} 1.7, 28.2 {+-} 2.1, and 27.2 {+-} 1.8 Bq dm{sup -3} to minima of 12.1 {+-} 0.3, 13.7 {+-} 0.3, and 17.8 {+-} 1.6 Bq dm{sup -3}, respectively. Based on the radon precursory data, this paper correlates the observed radon minima with earthquake magnitude and precursory time. The correlations provide a possible means for forecasting local disastrous earthquakes in the southern segment of coastal range and longitudinal valley of eastern Taiwan.
Chan, Chung-Han
2016-09-01
This study provides some new insights into earthquake forecasting models that are applied to regions with subduction systems, including the depth component for forecasting grids and time-dependent factors. To demonstrate the importance of depth component, a forecasting approach, which incorporates three-dimensional grids, is compared with an approach with two-dimensional cells. Through application to the two subduction regions, Ryukyu and Kanto, it is shown that the approaches with three-dimensional grids always demonstrate a better forecasting ability. I thus confirm the importance of depth dependency for forecasting, especially for applications to a subduction environment or a region with non-vertical seismogenic structures. In addition, this study discusses the role of time-dependent factors for forecasting models and concludes that time dependency only becomes crucial during the period with significant seismicity rate change that follows a large earthquake.
U.S. Geological Survey, Department of the Interior — A one-year seismic hazard forecast for the Central and Eastern United States, based on induced and natural earthquakes, has been produced by the U.S. Geological...
The effect of solar cycle's activities on earthquake:a conceptual idea for forecasting
Nikouravan, Bijan; Pirasteh, Saied; Somayeh, Mollaee
2013-04-01
It has been seen that the period of solar activity and its cycle has a decrease of seismic activity in the compression zone of the Earth. In addition, at the same time there is an increase of the activity in the tension zones of the Earth. This study has been emphasized on last 45 years (i.e. 1960-2005) cyclic data in term of the number of seismic and sunspots activities. The high correlation between sunspot and earthquakes shows that there is long-term forecast for the earthquakes mainly in Iran, Japan and USA in and after 2010. The next maximum of seismic activities and earthquakes with very high amplitude for the tension zones on the Earth has forecasted for the period 2012-2013 mainly in the night. This has been alarming for the countries that fall within the seismic region such as Iran, Japan, USA etc. and if the Governments do not make the infrastructure strengthen, there may be more disasters and loss of life and properties in future.
Earthquake Focal Mechanism Forecasts and Applications in the PSHA in Italy
Roselli, P.; Marzocchi, W.; Montone, P.; Mariucci, M. T.
2015-12-01
The reduction of uncertainties is a primary goal in Probabilistic Seismic Hazard Analysis (PSHA). One of the main sources of uncertainty is associated with the use of the Ground Motion Prediction Equations (GMPEs). Part of the GMPE uncertainties can be reduced improving the forecasts of the focal mechanisms and style of faulting related to the future earthquakes; in other words, it is expected that GMPEs forecasting performances are more accurate and precise if the style of faulting of the next earthquakes is known. In this study, we propose and apply to the Italian territory a procedure to compute, for each spatial cell, the probability to observe in the future a Normal, Reverse, and Strike-Slip event, and the average distribution of the P, T and N axes for each of these types of earthquake. For this purpose we use a significant focal mechanism catalog and the latest present-day stress field data release for Italy. The method is a modification of the Cumulative Moment Tensor introduced by Kostrov (1974), where all data are weighted according to their spatial distance from the cell. This method is applied to the Italian territory that is characterized by a complex tectonic setting because of the N-S convergence of Africa and Eurasian plates and of NE-SW extension, perpendicular to the Apenninic belt, coexistence. The final goal is to provide information that might be helpful for the ongoing activities related to the preparation of the next seismic hazard model for Italy.
Daily earthquake forecasts during the May-June 2012 Emilia earthquake sequence (northern Italy
Warner Marzocchi
2012-10-01
Full Text Available On May 20, 2012, at 02:03 UTC, a magnitude Ml 5.9 earthquake hit part of the Po Plain area (latitude, 44.89 ˚N; longitude, 11.23 ˚E close to the village of Finale-Emilia in the Emilia-Romagna region (northern Italy. This caused a number of human losses and significant economic damage to buildings, and to local farms and industry. This earthquake was preceded by an increase in the seismicity the day before, with the largest shock of Ml 4.1 at 23:13 UTC (latitude, 44.90 ˚N; longitude, 11.26 ˚E. It was then followed by six other Ml 5.0 or greater events in the following weeks. The largest of these six earthquakes occurred on May 29, 2012, at 07:00 UTC (Ml 5.8, and was located 12 km southwest of the May 20, 2012, main event (latitude, 44.85 ˚N; longitude, 11.09 ˚E, resulting in the collapse of many buildings that had already been weakened, a greater number of victims, and most of the economic damage (see Figure 1. This sequence took place in one of the Italian regions that is considered to be at small-to-moderate seismic hazard [Gruppo di Lavoro MPS 2004]. Earthquakes of the M6 class have occurred in the past in this zone [Gruppo di Lavoro CPTI 2004], but with a much smaller time frequency with respect to the most seismically hazardous parts of Italy. […
无
2000-01-01
The principle of middle and long-term earthquake forecast model of spatial and temporal synthesized probability gain and the evaluation of forecast efficiency (R-values) of various forecast methods are introduced in this paper. The R-value method, developed by Xu (1989), is further developed here, and can be applied to more complicated cases. Probability gains in spatial and/or temporal domains and the R-values for different forecast methods are estimated in North China. The synthesized probability gain is then estimated as an example.
Petersen, Mark D.; Mueller, Charles S.; Moschetti, Morgan P.; Hoover, Susan M.; Llenos, Andrea L.; Ellsworth, William L.; Michael, Andrew J.; Rubinstein, Justin L.; McGarr, Arthur F.; Rukstales, Kenneth S.
2016-03-28
The U.S. Geological Survey (USGS) has produced a 1-year seismic hazard forecast for 2016 for the Central and Eastern United States (CEUS) that includes contributions from both induced and natural earthquakes. The model assumes that earthquake rates calculated from several different time windows will remain relatively stationary and can be used to forecast earthquake hazard and damage intensity for the year 2016. This assessment is the first step in developing an operational earthquake forecast for the CEUS, and the analysis could be revised with updated seismicity and model parameters. Consensus input models consider alternative earthquake catalog durations, smoothing parameters, maximum magnitudes, and ground motion estimates, and represent uncertainties in earthquake occurrence and diversity of opinion in the science community. Ground shaking seismic hazard for 1-percent probability of exceedance in 1 year reaches 0.6 g (as a fraction of standard gravity [g]) in northern Oklahoma and southern Kansas, and about 0.2 g in the Raton Basin of Colorado and New Mexico, in central Arkansas, and in north-central Texas near Dallas. Near some areas of active induced earthquakes, hazard is higher than in the 2014 USGS National Seismic Hazard Model (NHSM) by more than a factor of 3; the 2014 NHSM did not consider induced earthquakes. In some areas, previously observed induced earthquakes have stopped, so the seismic hazard reverts back to the 2014 NSHM. Increased seismic activity, whether defined as induced or natural, produces high hazard. Conversion of ground shaking to seismic intensity indicates that some places in Oklahoma, Kansas, Colorado, New Mexico, Texas, and Arkansas may experience damage if the induced seismicity continues unabated. The chance of having Modified Mercalli Intensity (MMI) VI or greater (damaging earthquake shaking) is 5–12 percent per year in north-central Oklahoma and southern Kansas, similar to the chance of damage caused by natural earthquakes
Jumping over the hurdles to effectively communicate the Operational Earthquake Forecast
McBride, S.; Wein, A. M.; Becker, J.; Potter, S.; Tilley, E. N.; Gerstenberger, M.; Orchiston, C.; Johnston, D. M.
2016-12-01
Probabilities, uncertainties, statistics, science, and threats are notoriously difficult topics to communicate with members of the public. The Operational Earthquake Forecast (OEF) is designed to provide an understanding of potential numbers and sizes of earthquakes and the communication of it must address all of those challenges. Furthermore, there are other barriers to effective communication of the OEF. These barriers include the erosion of trust in scientists and experts, oversaturation of messages, fear and threat messages magnified by the sensalisation of the media, fractured media environments and online echo chambers. Given the complexities and challenges of the OEF, how can we overcome barriers to effective communication? Crisis and risk communication research can inform the development of communication strategies to increase the public understanding and use of the OEF, when applied to the opportunities and challenges of practice. We explore ongoing research regarding how the OEF can be more effectively communicated - including the channels, tools and message composition to engage with a variety of publics. We also draw on past experience and a study of OEF communication during the Canterbury Earthquake Sequence (CES). We demonstrate how research and experience has guided OEF communications during subsequent events in New Zealand, including the M5.7 Valentine's Day earthquake in 2016 (CES), M6.0 Wilberforce earthquake in 2015, and the Cook Strait/Lake Grassmere earthquakes in 2013. We identify the successes and lessons learned of the practical communication of the OEF. Finally, we present future projects and directions in the communication of OEF, informed by both practice and research.
Suggestion of EFS-small satellite system for impending earthquake forecast
无
2000-01-01
In the IAF Congress '92 a multiple small satellite Earth observation system was put forward with sensors of visible and infrared spectrums. The system could shorten the revisiting period so that any place on the world could be observed twice a day. Now we extend the idea to the microwave remote sensing satellite system. The main purpose of the system is the impending forecast of earthquakes. According to the theory and long-time concrete practice of Qiang Zuji through the observation of temperature increase of the low layer of atmosphere and its moving trend caused by some sorts of radiation and gases released from Earth interior, an impending strong earthquake could be predicted in time. As the temperature increase is detected by thermo-infrared spectrum sensors on the meteorological satellites, the observation may be sometimes obstructed by cloud or rain. In the suggested system, mm-wave radiometers are used and those obstructions could be generally overcome.
Wu, Zhongliang; Jiang, Changsheng; Zhang, Shengfeng
2016-08-01
The approach in China since the last 1.5 decade for using apparent stress in time-dependent seismic hazard assessment or earthquake forecast is summarized. Retrospective case studies observe that apparent stress exhibits short-term increase, with time scale of several months, before moderate to strong earthquakes in a large area surrounding the `target earthquake'. Apparent stress is also used to estimate the tendency of aftershock activity. The concept relating apparent stress indirectly to stress level is used to understand the properties of some `precursory' anomalies. Meanwhile, different opinions were reported. Problems in the calculation also existed for some cases. Moreover, retrospective studies have the limitation in their significance as compared to forward forecast test. Nevertheless, this approach, seemingly uniquely carried out in a large scale in mainland China, provides the earthquake catalogs for the predictive analysis of seismicity with an additional degree of freedom, deserving a systematic review and reflection.
González, A; Gómez, J B; Pacheco, A F; Gonzalez, Alvaro; Vazquez-Prada, Miguel; Gomez, Javier B.; Pacheco, Amalio F.
2005-01-01
Numerical models of seismic faults are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual fault or fault network it simulates (just as, for example, meteorologists synchronize their models with the atmosphere by incorporating current atmospheric data in them). However, lithospheric dynamics is largely unobservable: important parameters cannot (or can rarely) be measured in Nature. Earthquakes, though, provide indirect but measurable clues of the stress and strain status in the lithosphere, which should be helpful for the accurate synchronization of the models. The rupture area is one of the measurable parameters of actual earthquakes. Here we explore how this can be used to at least synchronize fault models between themselves and forecast synthetic earthquakes. Our purpose here is to forec...
Acoustic wave-equation-based earthquake location
Tong, Ping; Yang, Dinghui; Liu, Qinya; Yang, Xu; Harris, Jerry
2016-04-01
We present a novel earthquake location method using acoustic wave-equation-based traveltime inversion. The linear relationship between the location perturbation (δt0, δxs) and the resulting traveltime residual δt of a particular seismic phase, represented by the traveltime sensitivity kernel K(t0, xs) with respect to the earthquake location (t0, xs), is theoretically derived based on the adjoint method. Traveltime sensitivity kernel K(t0, xs) is formulated as a convolution between the forward and adjoint wavefields, which are calculated by numerically solving two acoustic wave equations. The advantage of this newly derived traveltime kernel is that it not only takes into account the earthquake-receiver geometry but also accurately honours the complexity of the velocity model. The earthquake location is obtained by solving a regularized least-squares problem. In 3-D realistic applications, it is computationally expensive to conduct full wave simulations. Therefore, we propose a 2.5-D approach which assumes the forward and adjoint wave simulations within a 2-D vertical plane passing through the earthquake and receiver. Various synthetic examples show the accuracy of this acoustic wave-equation-based earthquake location method. The accuracy and efficiency of the 2.5-D approach for 3-D earthquake location are further verified by its application to the 2004 Big Bear earthquake in Southern California.
Probability Forecast of Regional Landslide Based on Numerical Weather Forecast
GAO Kechang; WEI Fangqiang; CUI Peng; HU Kaiheng; XU Jing; ZHANG Guoping; BI Baogui
2006-01-01
The regional forecast of landslide is one of the key points of hazard mitigation. It is also a hot and difficult point in research field. To solve this problem has become urgent task along with Chinese economy fast development. This paper analyzes the principle of regional landslide forecast and the factors for forecasting. The method of a combination of Information Value Model and Extension Model has been put forward to be as the forecast model. Using new result of Numerical Weather Forecast Research and that combination model, we discuss the implementation feasibility of regional landslide forecast. Finally, with the help of Geographic Information System, an operation system for southwest of China landslide forecast has been developed. It can carry out regional landslide forecast daily and has been pilot run in NMC. Since this is the first time linking theoretical research with meteorological service, further works are needed to enhance it.
Fung, D. C. N.; Wang, J. P.; Chang, S. H.; Chang, S. C.
2014-12-01
Using a revised statistical model built on past seismic probability models, the probability of different magnitude earthquakes occurring within variable timespans can be estimated. The revised model is based on Poisson distribution and includes the use of best-estimate values of the probability distribution of different magnitude earthquakes recurring from a fault from literature sources. Our study aims to apply this model to the Taipei metropolitan area with a population of 7 million, which lies in the Taipei Basin and is bounded by two normal faults: the Sanchaio and Taipei faults. The Sanchaio fault is suggested to be responsible for previous large magnitude earthquakes, such as the 1694 magnitude 7 earthquake in northwestern Taipei (Cheng et. al., 2010). Based on a magnitude 7 earthquake return period of 543 years, the model predicts the occurrence of a magnitude 7 earthquake within 20 years at 1.81%, within 79 years at 6.77% and within 300 years at 21.22%. These estimates increase significantly when considering a magnitude 6 earthquake; the chance of one occurring within the next 20 years is estimated to be 3.61%, 79 years at 13.54% and 300 years at 42.45%. The 79 year period represents the average lifespan of the Taiwan population. In contrast, based on data from 2013, the probability of Taiwan residents experiencing heart disease or malignant neoplasm is 11.5% and 29%. The inference of this study is that the calculated risk that the Taipei population is at from a potentially damaging magnitude 6 or greater earthquake occurring within their lifetime is just as great as of suffering from a heart attack or other health ailments.
Maura Murru
2015-03-01
Full Text Available In this paper, we compare the forecasting performance of several statistical models, which are used to describe the occurrence process of earthquakes in forecasting the short-term earthquake probabilities during the L’Aquila earthquake sequence in central Italy in 2009. These models include the Proximity to Past Earthquakes (PPE model and two versions of the Epidemic Type Aftershock Sequence (ETAS model. We used the information gains corresponding to the Poisson and binomial scores to evaluate the performance of these models. It is shown that both ETAS models work better than the PPE model. However, in comparing the two types of ETAS models, the one with the same fixed exponent coefficient (alpha = 2.3 for both the productivity function and the scaling factor in the spatial response function (ETAS I, performs better in forecasting the active aftershock sequence than the model with different exponent coefficients (ETAS II, when the Poisson score is adopted. ETAS II performs better when a lower magnitude threshold of 2.0 and the binomial score are used. The reason is found to be that the catalog does not have an event of similar magnitude to the L’Aquila mainshock (Mw 6.3 in the training period (April 16, 2005 to March 15, 2009, and the (alpha-value is underestimated, thus the forecast seismicity is underestimated when the productivity function is extrapolated to high magnitudes. We also investigate the effect of the inclusion of small events in forecasting larger events. These results suggest that the training catalog used for estimating the model parameters should include earthquakes of magnitudes similar to the mainshock when forecasting seismicity during an aftershock sequence.
Lianhui Li
2015-12-01
Full Text Available Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.
U.S. Geological Survey, Department of the Interior — A one-year seismic hazard forecast for the Central and Eastern United States, based on induced and natural earthquakes, has been produced by the U.S. Geological...
U.S. Geological Survey, Department of the Interior — A one-year seismic hazard forecast for the Central and Eastern United States, based on induced and natural earthquakes, has been produced by the U.S. Geological...
Wave-equation Based Earthquake Location
Tong, P.; Yang, D.; Yang, X.; Chen, J.; Harris, J.
2014-12-01
Precisely locating earthquakes is fundamentally important for studying earthquake physics, fault orientations and Earth's deformation. In industry, accurately determining hypocenters of microseismic events triggered in the course of a hydraulic fracturing treatment can help improve the production of oil and gas from unconventional reservoirs. We develop a novel earthquake location method based on solving full wave equations to accurately locate earthquakes (including microseismic earthquakes) in complex and heterogeneous structures. Traveltime residuals or differential traveltime measurements with the waveform cross-correlation technique are iteratively inverted to obtain the locations of earthquakes. The inversion process involves the computation of the Fréchet derivative with respect to the source (earthquake) location via the interaction between a forward wavefield emitting from the source to the receiver and an adjoint wavefield reversely propagating from the receiver to the source. When there is a source perturbation, the Fréchet derivative not only measures the influence of source location but also the effects of heterogeneity, anisotropy and attenuation of the subsurface structure on the arrival of seismic wave at the receiver. This is essential for the accuracy of earthquake location in complex media. In addition, to reduce the computational cost, we can first assume that seismic wave only propagates in a vertical plane passing through the source and the receiver. The forward wavefield, adjoint wavefield and Fréchet derivative with respect to the source location are all computed in a 2D vertical plane. By transferring the Fréchet derivative along the horizontal direction of the 2D plane into the ones along Latitude and Longitude coordinates or local 3D Cartesian coordinates, the source location can be updated in a 3D geometry. The earthquake location obtained with this combined 2D-3D approach can then be used as the initial location for a true 3D wave
Nomura, S.; Ogata, Y.
2010-12-01
This study is concerned with the probability forecast by the Brownian Passage Time (BPT) model especially in case where only a few records of recurrent earthquakes from an active fault are available. We adopt the Bayesian predictive distribution that takes the relevant prior information and all possibilities for model parameters into account. We utilize the size of single-event displacements U and the slip rate V across the segment to calculate the mean recurrence time T=U/V that the past recurrence intervals are distributed around as Figure 1. We then make use of the best fitted prior distribution for the BPT variation coefficient (the shape parameter, α) selected by the Akaike Bayesian information criterion (ABIC), while the ERC uses the same common estimate α=0.24. Applying this prior distribution, we can see that α takes various values among the faults but has some locational tendencies from Figure 2. For example, α values tend to be higher in the center of Honshu island where the faults are densely populated. We compare the goodness of fit and probability forecasts between the conventional models and our proposed model by historical or simulated datasets. The Bayesian predictor shows very stable and superior performance for small samples or variant recurrence times. Figure 1: The relation between mean recurrence time from slip data and past recurrence intervals with error bars. Figure 2: The map of active faults in land and subduction-zones in Japan, whose colors show the Bayes estimates of variation coefficient α.
Assessment of GNSS-based height data of multiple ships for measuring and forecasting great tsunamis
Inazu, Daisuke; Waseda, Takuji; Hibiya, Toshiyuki; Ohta, Yusaku
2016-12-01
Ship height positioning by the Global Navigation Satellite System (GNSS) was investigated for measuring and forecasting great tsunamis. We first examined GNSS height-positioning data of a navigating vessel. If we use the kinematic precise point positioning (PPP) method, tsunamis greater than 10-1 m will be detected by ship height positioning. Based on Automatic Identification System (AIS) data, we found that tens of cargo ships and tankers are usually identified to navigate over the Nankai Trough, southwest Japan. We assumed that a future Nankai Trough great earthquake tsunami will be observed by the kinematic PPP height positioning of an AIS-derived ship distribution, and examined the tsunami forecast capability of the offshore tsunami measurements based on the PPP-based ship height. A method to estimate the initial tsunami height distribution using offshore tsunami observations was used for forecasting. Tsunami forecast tests were carried out using simulated tsunami data by the PPP-based ship height of 92 cargo ships/tankers, and by currently operating deep-sea pressure and Global Positioning System (GPS) buoy observations at 71 stations over the Nankai Trough. The forecast capability using the PPP-based height of the 92 ships was shown to be comparable to or better than that using the operating offshore observatories at the 71 stations. We suppose that, immediately after the occurrence of a great earthquake, stations receiving successive ship information (AIS data) along certain areas of the coast would fail to acquire ship data due to strong ground shaking, especially near the epicenter. Such a situation would significantly deteriorate the tsunami-forecast capability using ship data. On the other hand, operational real-time analysis of seismic/geodetic data would be carried out for estimating a tsunamigenic fault model. Incorporating the seismic/geodetic fault model estimation into the tsunami forecast above possibly compensates for the deteriorated forecast
Distal Volcano-Tectonic Earthquakes (DVT's): Diagnosis and use in Eruption Forecasting
White, R. A.; Power, J. A.
2001-12-01
Volcano-tectonic earthquake swarms occurred 5-6 Km from the summit months prior to the catastrophic eruptions of Mt. Pinatubo (1991) and Nevado del Ruiz (1985). Similar earthquake swarms probably occurred beneath distal portions of Mt. St. Helens (1980), El Chichon (1982), and Soufriere Hills (1995-98) months to years prior the eruptions there. Thus these Distal Volcano-Tectonic (DVT) earthquakes were probably the longest-term precursors to those eruptions. Based on close correlation with observed volcanic activity, we show that DVT's result from magma intrusion. Although DVT's are brittle-failure earthquakes along faults, they are generally distinguishable from tectonic sequences by clustering features, most notably a slowly increasing to roughly constant moment release rate. Total seismic moments for DVT swarms appear constrained by magma viscosity, with the largest moments associated with basalts. DVT swarms occur from 30 Km from summits of volcanoes. Maximum depths increase roughly as the distance out to 10 km then gradually level off, as do depths to the brittle-ductile transition near active volcanoes. We interpret DVT's as resulting from injection of magmatic fluids into closed aquifers near the base of the brittle zone, over-pressurizing the aquifers out several to many kilometers horizontally. The over-pressure may trigger faulting in areas where the intruding magma increased the static stress. We show that the DVT moment rate is proportional to the fluid injection rate and is apparently delayed by only minutes to tens of minutes depending on distance, owing to the rapid hydraulic transmission of pore-pressures. Thus DVT earthquake swarms can provide early warning for major eruptions while possibly providing constraints in near-real time on magma viscosity, depth and ascent rate during intrusion.
Forecasting earthquake-induced landslides at the territorial scale by means of PBEE approaches
Berni, N.; Fanelli, G.; Ponziani, F.; Salciarini, D.; Stelluti, M.; Tamagnini, C.
2012-04-01
Models for predicting earthquake-induced landslide susceptibility on a regional scale are the main tools used by the Civil Protection Agencies to issue warning alarms after seismic events and to evaluate possible seismic hazard conditions for different earthquake scenarios. We present a model for susceptibility analysis based on a deterministic approach that subdivides the study area in a finite number of cells, assumes for each cell a simplified infinite slope model and considers the earthquake shaking as the landslide triggering factor. In this case, the stability conditions of the slopes are related both to the slope features (in terms of mechanical properties, geometrical and topographical settings and pore pressure regime) and to the earthquake characteristics (in terms of intensity, duration and frequency). Therefore, for a territorial analysis, the proposed method determines the limit conditions of the slope, given the seismic input, soil strength parameters, slope and depth of slip surface, and groundwater conditions for every cell in the study area. The procedure is ideally suited for the implementation on a GIS platform, in which the relevant information are stored for each cell. The seismic response of the slopes is analyzed by means of the Newmark's permanent displacement method. In Newmark's approach, seismic slope stability is measured in terms of the ratio of accumulated permanent displacement during the earthquake and the maximum allowable one, depending - in principle - on the definition of tolerable damage level. The computed permanent displacement depends critically on the actual slope stability conditions, quantified by the critical acceleration, i.e., the seismic acceleration bringing the slope to a state of (instantaneous) limit equilibrium. This methodology is applied in a study of shallow earthquake-induced landslides in central Italy. The triggering seismic input is defined in terms of synthetic accelerograms, constructed from the response
Nomura, Shunichi; Ogata, Yosihiko
2016-04-01
We propose a Bayesian method of probability forecasting for recurrent earthquakes of inland active faults in Japan. Renewal processes with the Brownian Passage Time (BPT) distribution are applied for over a half of active faults in Japan by the Headquarters for Earthquake Research Promotion (HERP) of Japan. Long-term forecast with the BPT distribution needs two parameters; the mean and coefficient of variation (COV) for recurrence intervals. The HERP applies a common COV parameter for all of these faults because most of them have very few specified paleoseismic events, which is not enough to estimate reliable COV values for respective faults. However, different COV estimates are proposed for the same paleoseismic catalog by some related works. It can make critical difference in forecast to apply different COV estimates and so COV should be carefully selected for individual faults. Recurrence intervals on a fault are, on the average, determined by the long-term slip rate caused by the tectonic motion but fluctuated by nearby seismicities which influence surrounding stress field. The COVs of recurrence intervals depend on such stress perturbation and so have spatial trends due to the heterogeneity of tectonic motion and seismicity. Thus we introduce a spatial structure on its COV parameter by Bayesian modeling with a Gaussian process prior. The COVs on active faults are correlated and take similar values for closely located faults. It is found that the spatial trends in the estimated COV values coincide with the density of active faults in Japan. We also show Bayesian forecasts by the proposed model using Markov chain Monte Carlo method. Our forecasts are different from HERP's forecast especially on the active faults where HERP's forecasts are very high or low.
Christophersen, Annemarie; Rhoades, David A.; Colella, Harmony V.
2017-03-01
The well-established earthquake forecasting model 'Every Earthquake a Precursor According to Scale' (EEPAS) is based on the observation that the magnitude and rate of minor earthquakes increases prior to large earthquakes. The precursor time is measured between this increase and the mainshock and is in the order of months to decades. Fitting the EEPAS model to different regional earthquake catalogues has indicated that the precursor time is longer in more slowly deforming tectonic environments. Examples from the stable continental region of Australia confirm this. To overcome the challenge of limited earthquake records in the analysis of the precursor time for areas with low strain rate, we use the physics-based earthquake simulator, RSQSim to generate a series of synthetic earthquake catalogues. A fault network with realistic complexity, is employed, based on the Wellington, New Zealand, fault network. The slip rates on faults are systematically reduced by five successive factors of 1/4. Fitting the EEPAS model to these synthetic catalogues shows that the precursor time is inversely proportional to the reduction in slip rate. Results suggest that the expected precursor times for large earthquakes in stable continental regions far exceed the length of available catalogues. The expected precursor time for the 2010 M7.1 Darfield, New Zealand, earthquake, which apparently had no precursory seismicity in the instrumental catalogue, also exceeds the length of the available catalogue. Therefore, applying the EEPAS model to physics-based simulators allows us to start understanding the phenomenon of precursory seismicity.
Mavrodiev, S Cht
2016-01-01
This research presents one possible way for imminent prediction of earthquake magnitude, depth and epicenter coordinates by solving the inverse problem using a data acquisition network system for monitoring, archiving and complex analysis of geophysical variables precursors. Among many possible precursors the most reliable are the geoelectromagnetic field, the boreholes water level, the radon surface concentration, the local heat flow, the ionosphere variables, the low frequency atmosphere and Earth core waves. In this study only geomagnetic data are used. Within the framework of geomagnetic quake approach it is possible to perform an imminent regional seismic activity forecasting on the basis of simple analysis of geomagnetic data which use a new variable Schtm with dimension surface density of energy. Such analysis of Memambetsu, Kakioka, Kanoya (Japan, INTERMAGNET) stations and NEIC earthquakes data, the hypothesis that the predicted earthquake is this with bigest value of the variable Schtm permit to form...
Bellier Joseph
2016-01-01
Full Text Available Hydrological ensemble forecasting performances are analysed over 5 basins up to 2000 km2 in the French Upper Rhone region. Streamflow forecasts are issued at an hourly time step from lumped ARX rainfall-runoff models forced by different precipitation forecasts. Ensemble meteorological forecasts from ECMWF and NCEP are considered, as well as analogue-based forecasts fed by their corresponding control forecast. Analogue forecasts are rearranged using an adaptation of the Schaake-Shuffle method in order to ensure the temporal coherence. A new evaluation approach is proposed, separating forecasting performances on peak amplitudes and peak timings for high flow events. Evaluation is conducted against both simulated and observed streamflow (so that relative meteorological and hydrological uncertainties can be assessed, by means of CRPS and rank histograms, over the 2007-2014 period. Results show a general agreement of the forecasting performances when averaged over the 5 basins. However, ensemble-based and analogue-based streamflow forecasts produce a different signature on peak events in terms of bias, spread and reliability. Strengths and weaknesses of both approaches are discussed as well as potential improvements, notably towards their merging.
Ebrahimian, Hossein; Jalayer, Fatemeh
2017-08-29
In the immediate aftermath of a strong earthquake and in the presence of an ongoing aftershock sequence, scientific advisories in terms of seismicity forecasts play quite a crucial role in emergency decision-making and risk mitigation. Epidemic Type Aftershock Sequence (ETAS) models are frequently used for forecasting the spatio-temporal evolution of seismicity in the short-term. We propose robust forecasting of seismicity based on ETAS model, by exploiting the link between Bayesian inference and Markov Chain Monte Carlo Simulation. The methodology considers the uncertainty not only in the model parameters, conditioned on the available catalogue of events occurred before the forecasting interval, but also the uncertainty in the sequence of events that are going to happen during the forecasting interval. We demonstrate the methodology by retrospective early forecasting of seismicity associated with the 2016 Amatrice seismic sequence activities in central Italy. We provide robust spatio-temporal short-term seismicity forecasts with various time intervals in the first few days elapsed after each of the three main events within the sequence, which can predict the seismicity within plus/minus two standard deviations from the mean estimate within the few hours elapsed after the main event.
Earthquake forecast for the Wasatch Front region of the Intermountain West
DuRoss, Christopher B.
2016-04-18
The Working Group on Utah Earthquake Probabilities has assessed the probability of large earthquakes in the Wasatch Front region. There is a 43 percent probability of one or more magnitude 6.75 or greater earthquakes and a 57 percent probability of one or more magnitude 6.0 or greater earthquakes in the region in the next 50 years. These results highlight the threat of large earthquakes in the region.
Generating Weather Forecast Texts with Case Based Reasoning
Adeyanju, Ibrahim
2015-01-01
Several techniques have been used to generate weather forecast texts. In this paper, case based reasoning (CBR) is proposed for weather forecast text generation because similar weather conditions occur over time and should have similar forecast texts. CBR-METEO, a system for generating weather forecast texts was developed using a generic framework (jCOLIBRI) which provides modules for the standard components of the CBR architecture. The advantage in a CBR approach is that systems can be built...
On the Forecasts of the Lushan Earthquake in 2013%芦山地震的预测
胡辉; 曾佐勋; 苏有锦; 付虹; 王锐
2015-01-01
2013年4月20日中国四川省雅安市芦山县发生了7．0级地震，对于这次地震发生的时间和地点，震前有不同程度的预测。为了推动地球科学发展，提高地震预测水平，以达到最大限度地减轻地震灾害损失，特对此作一小结。2011年3月11日日本东北部海域发生9级地震以后，利用可公度性原理，分析了近年来发生在世界各地的大地震，发现这些地震发生的时间具有可公度性，且它们基本上发生在其时间轴上的可公度值点上。根据对川滇块体地震信息的可公度性分析，该研究区的可公度值是2．44年，因而2013．24年就是未来地震可能发生的时间点；根据卫星重力异常反映的地壳密度异常变化图，雅安西侧与汶川具有两个特征相同的独立的卫星重力局部高异常梯度突变区，2008年的汶川地震只是释放了龙门山断裂带东北段的能量和应力，这导致能量和应力在龙门山断裂带南西段，特别是南西端与重力异常突变位置的叠加区加速积聚和集中，因此曾佐勋多次指出四川的下一个大震将在雅安与康定之间发生。这两方面的分析都是震前的，可惜它们是彼此独立的。如果事前能将这两个方面的预测加以综合分析，则可以达到短期预测的目的。这再一次表明，地震预测必须走综合分析之路。%A magnitude 7.0 earthquake occurred at the Lushan County, Ya’an City, Sichuan Province, China in April 20, 2013.We had various forecasts of the time and location of this earthquake.In order to advance development of geosciences and improve accuracies of forecasts of earthquakes, which, should help to minimize damages from earthquakes, we summarize these forecasts in this paper.After a magnitude 9 earthquake occurred off the northeastern coast of Japan on March 11, 2011, we used the method of commensurability to analyze the earthquakes in recent years in the world
Forecasting Urban Expansion Based on Night Lights
Stathakis, D.
2016-06-01
Forecasting urban expansion models are a very powerful tool in the hands of urban planners in order to anticipate and mitigate future urbanization pressures. In this paper, a linear regression forecasting urban expansion model is implemented based on the annual composite night lights time series available from National Oceanic and Atmospheric Administration (NOAA). The product known as 'stable lights' is used in particular, after it has been corrected with a standard intercalibration process to reduce artificial year-to-year fluctuations as much as possible. Forecasting is done for ten years after the end of the time series. Because the method is spatially explicit the predicted expansion trends are relatively accurately mapped. Two metrics are used to validate the process. The first one is the year-to-year Sum of Lights (SoL) variation. The second is the year-to-year image correlation coefficient. Overall it is evident that the method is able to provide an insight on future urbanization pressures in order to be taken into account in planning. The trends are quantified in a clear spatial manner.
Staged decision making based on probabilistic forecasting
Booister, Nikéh; Verkade, Jan; Werner, Micha; Cranston, Michael; Cumiskey, Lydia; Zevenbergen, Chris
2016-04-01
Flood forecasting systems reduce, but cannot eliminate uncertainty about the future. Probabilistic forecasts explicitly show that uncertainty remains. However, as - compared to deterministic forecasts - a dimension is added ('probability' or 'likelihood'), with this added dimension decision making is made slightly more complicated. A technique of decision support is the cost-loss approach, which defines whether or not to issue a warning or implement mitigation measures (risk-based method). With the cost-loss method a warning will be issued when the ratio of the response costs to the damage reduction is less than or equal to the probability of the possible flood event. This cost-loss method is not widely used, because it motivates based on only economic values and is a technique that is relatively static (no reasoning, yes/no decision). Nevertheless it has high potential to improve risk-based decision making based on probabilistic flood forecasting because there are no other methods known that deal with probabilities in decision making. The main aim of this research was to explore the ways of making decision making based on probabilities with the cost-loss method better applicable in practice. The exploration began by identifying other situations in which decisions were taken based on uncertain forecasts or predictions. These cases spanned a range of degrees of uncertainty: from known uncertainty to deep uncertainty. Based on the types of uncertainties, concepts of dealing with situations and responses were analysed and possible applicable concepts where chosen. Out of this analysis the concepts of flexibility and robustness appeared to be fitting to the existing method. Instead of taking big decisions with bigger consequences at once, the idea is that actions and decisions are cut-up into smaller pieces and finally the decision to implement is made based on economic costs of decisions and measures and the reduced effect of flooding. The more lead-time there is in
Shanker, D.; Paudyal, ,; Singh, H.
2010-12-01
It is not only the basic understanding of the phenomenon of earthquake, its resistance offered by the designed structure, but the understanding of the socio-economic factors, engineering properties of the indigenous materials, local skill and technology transfer models are also of vital importance. It is important that the engineering aspects of mitigation should be made a part of public policy documents. Earthquakes, therefore, are and were thought of as one of the worst enemies of mankind. Due to the very nature of release of energy, damage is evident which, however, will not culminate in a disaster unless it strikes a populated area. The word mitigation may be defined as the reduction in severity of something. The Earthquake disaster mitigation, therefore, implies that such measures may be taken which help reduce severity of damage caused by earthquake to life, property and environment. While “earthquake disaster mitigation” usually refers primarily to interventions to strengthen the built environment, and “earthquake protection” is now considered to include human, social and administrative aspects of reducing earthquake effects. It should, however, be noted that reduction of earthquake hazards through prediction is considered to be the one of the effective measures, and much effort is spent on prediction strategies. While earthquake prediction does not guarantee safety and even if predicted correctly the damage to life and property on such a large scale warrants the use of other aspects of mitigation. While earthquake prediction may be of some help, mitigation remains the main focus of attention of the civil society. Present study suggests that anomalous seismic activity/ earthquake swarm existed prior to the medium size earthquakes in the Nepal Himalaya. The mainshocks were preceded by the quiescence period which is an indication for the occurrence of future seismic activity. In all the cases, the identified episodes of anomalous seismic activity were
Probabilistic forecasts based on radar rainfall uncertainty
Liguori, S.; Rico-Ramirez, M. A.
2012-04-01
The potential advantages resulting from integrating weather radar rainfall estimates in hydro-meteorological forecasting systems is limited by the inherent uncertainty affecting radar rainfall measurements, which is due to various sources of error [1-3]. The improvement of quality control and correction techniques is recognized to play a role for the future improvement of radar-based flow predictions. However, the knowledge of the uncertainty affecting radar rainfall data can also be effectively used to build a hydro-meteorological forecasting system in a probabilistic framework. This work discusses the results of the implementation of a novel probabilistic forecasting system developed to improve ensemble predictions over a small urban area located in the North of England. An ensemble of radar rainfall fields can be determined as the sum of a deterministic component and a perturbation field, the latter being informed by the knowledge of the spatial-temporal characteristics of the radar error assessed with reference to rain-gauges measurements. This approach is similar to the REAL system [4] developed for use in the Southern-Alps. The radar uncertainty estimate can then be propagated with a nowcasting model, used to extrapolate an ensemble of radar rainfall forecasts, which can ultimately drive hydrological ensemble predictions. A radar ensemble generator has been calibrated using radar rainfall data made available from the UK Met Office after applying post-processing and corrections algorithms [5-6]. One hour rainfall accumulations from 235 rain gauges recorded for the year 2007 have provided the reference to determine the radar error. Statistics describing the spatial characteristics of the error (i.e. mean and covariance) have been computed off-line at gauges location, along with the parameters describing the error temporal correlation. A system has then been set up to impose the space-time error properties to stochastic perturbations, generated in real-time at
Rogers, M. A.
2015-12-01
Using satellite observations from GOES-E and GOES-W platforms in concert with GFS-derived cloud-level winds and a standalone radiative transfer model, an advection-derived forecast for surface GHI over the continental United States, with intercomparison between forecasts for four zones over the CONUS and Central Pacific with SURFRAD results. Primary sources for error in advection-based forecasts, primarily driven by false- or mistimed ramp events are discussed, with identification of error sources quantified along with techniques used to improve advection-based forecasts to approximately 10% MAE for designated surface locations. Development of a blended steering wind product utilizing NWP output combined with satellite-derived winds from AMV techniques to improve 0-1 hour advection forecasts will be discussed. Additionally, the use of two years' of solar forecast observations in the development of a prototype probablistic forecast for ramp events will be shown, with the intent of increasing the use of satellite-derived forecasts for grid operators and optimizing integration of renewable resources into the power grid. Elements of the work were developed under the 'Public-Private-Academic Partnership to Advance Solar Power Forecasting' project spearheaded by the National Center for Atmospheric Research.
A New Nonlinear Compound Forecasting Method Based on ANN
无
2000-01-01
In this paper the compound-forecasting method is discussed. The compound-forecasting method is one of the hotspots in the current predication. Firstly, the compound-forecasting method is introduced and various existing compound-forecasting methods arediscussed. Secondly, the Artificial Neural Network (ANN) is brought in compound-prediction research and a nonlinear compound-prediction model based on ANN is presented. Finally, inorder to avoid irregular weight, a new method is presented which uses principal component analyses to increase the availability of compound-forecasting information. Higherforecasting precision is achieved in practice.
Action-based flood forecasting for triggering humanitarian action
Coughlan de Perez, Erin; van den Hurk, Bart; van Aalst, Maarten K.; Amuron, Irene; Bamanya, Deus; Hauser, Tristan; Jongma, Brenden; Lopez, Ana; Mason, Simon; Mendler de Suarez, Janot; Pappenberger, Florian; Rueth, Alexandra; Stephens, Elisabeth; Suarez, Pablo; Wagemaker, Jurjen; Zsoter, Ervin
2016-09-01
Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.
Segou, M.; Parsons, T.; Ellsworth, W. L.
2012-12-01
We implement a retrospective forecast test specific to the 1989 Loma Prieta sequence and we focus on the comparison between two realizations of the epidemic-type aftershock sequence (ETAS) model and twenty-one models based on Coulomb stress change calculations and rate-and-state theory (CRS). We find that: (1) ETAS models forecast the spatial evolution of seismicity better in the near-source region, (2) CRS models can compete with ETAS models at off-fault regions and short-periods after the mainshock, (3) adopting optimally oriented planes as receivers could lead to better performance for short-time period up to a few days, whereas geologically specified planes should be implemented at long-term forecasting, and (4) CRS models based on shear stress have comparable performance with other CRS models, with the benefit of fewer free parameters involved in the stress calculations. The above results show that physics-based and statistical forecast models are complimentary, and that future forecasts should be combinations of ETAS and CRS models in space and time. We note that the realization in time and space of the CRS models involves a number of critical parameters ('learning' phase seismicity rates, regional stress field, loading rates on faults), which should be retrospectively tested to improve the predictive power of physics-based models.During our experiment the forecast covers Northern California [123.0-121.3°W in longitude 36.4-38.2°N in latitude] in a 2.5 km spatial grid within a 10-day interval following a mainshock, but here we focus on the results related with the post-seismic period of Loma Prieta earthquake. We consider for CRS models a common learning phase (1974-1980) to ensure consistency in our comparison, and we take into consideration stress perturbations imparted by 9 M>5.0 earthquakes between 1980-1989 in Northern California, including the 1988-1989 Lake Ellsman events. ETAS parameters correspond to the maximum likelihood estimations derived after
Short-Term Forecasting of Taiwanese Earthquakes Using a Universal Model of Fusion-Fission Processes
Cheong, Siew Ann; Tan, Teck Liang; Chen, Chien-Chih; Chang, Wu-Lung; Liu, Zheng; Chew, Lock Yue; Sloot, Peter M. A.; Johnson, Neil F.
2014-01-01
Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting from catalog data. We show how the equilibrium dynamics of this model very naturally explains the Gutenberg-Richter law. Using the high-resolution earthquake catalog of Taiwan between Jan 1994 and Feb 2009, we illustrate how out-of-equilibrium spatio-temporal signatures in the time interval between earthquakes and the integrated energy released by earthquakes can be used to reliably determine the times, magnitudes, and locations of large earthquakes, as well as the maximum numbers of large aftershocks that would follow. PMID:24406467
Short-term forecasting of Taiwanese earthquakes using a universal model of fusion-fission processes.
Cheong, Siew Ann; Tan, Teck Liang; Chen, Chien-Chih; Chang, Wu-Lung; Liu, Zheng; Chew, Lock Yue; Sloot, Peter M A; Johnson, Neil F
2014-01-10
Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting from catalog data. We show how the equilibrium dynamics of this model very naturally explains the Gutenberg-Richter law. Using the high-resolution earthquake catalog of Taiwan between Jan 1994 and Feb 2009, we illustrate how out-of-equilibrium spatio-temporal signatures in the time interval between earthquakes and the integrated energy released by earthquakes can be used to reliably determine the times, magnitudes, and locations of large earthquakes, as well as the maximum numbers of large aftershocks that would follow.
Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes
Niya Chen; Zheng Qian; Xiaofeng Meng
2013-01-01
Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm) is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposi...
Spatiotemporal fuzzy based climate forecasting for Australia
Montazerolghaem, M.; Vervoort, R. W.; Minasny, B.; McBratney, A.
2012-12-01
Variation in weather and climate events impacts agriculture production processes, and profits across years. Therefore, seasonal rainfall prediction is an important factor for strategic and tactical decision making in agricultural, land and water resource management. This study aims to apply optimal data-driven techniques for fine resolution climate classification and forecasting over South-eastern Australia. Data were used in this study were included daily precipitation, maximum and minimum temperature data collected over 40 years from 107 weather stations in Southeast Australia acquired from the Bureau of Meteorology (BOM). Fuzzy-k means clustering techniques (FKM) were applied on one year weekly time series. Cluster centroids and memberships of rainfall and temperature weekly time series for one year period provide meaningful and insight into weather variability in time and space over the study. Stations are grouped based on their memberships in rainfall and temperature classes. The result showed that FKM is a useful method for trend analysis and pattern discovery in space and time. Outcomes indicate improvement in the climate classification of the area at the station level. An associate project is gathering higher spatial density on-farm data. This high-resolution climate data collected at the farm scale will be analyzed similarly in the future to improve spatial resolution of our classification. The second stage of this study consists of development of a fine-resolution forecasting model for predicting rainfall. FKM was applied on a metrics which included input and output time series to extract rules and relationships between them. After classification, rules were extracted within each class based on forecasting time, space and extreme climate events followed by effective sea surface temperature anomalies. These rules and a lookup table of input and output centroids were used for rainfall prediction in the form of weekly time series for the next six months. One
Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes
Niya Chen
2013-01-01
Full Text Available Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.
Short-Term Forecasting of Taiwanese Earthquakes Using a Universal Model of Fusion-Fission Processes
Cheong, S.A.; Tan, T.L.; Chen, C.-C.; Chang, W.-L.; Liu, Z.; Chew, L.Y.; Sloot, P.M.A.; Johnson, N.F.
2014-01-01
Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting fr
Short-Term Forecasting of Taiwanese Earthquakes Using a Universal Model of Fusion-Fission Processes
Cheong, S.A.; Tan, T.L.; Chen, C.-C.; Chang, W.-L.; Liu, Z.; Chew, L.Y.; Sloot, P.M.A.; Johnson, N.F.
2014-01-01
Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting
Forecasting aftershock activity: 1. Adaptive estimates based on the Omori and Gutenberg-Richter laws
Baranov, S. V.; Shebalin, P. N.
2016-05-01
The method for forecasting the intensity of the aftershock processes after strong earthquakes in different magnitude intervals is considered. The method is based on the joint use of the time model of the aftershock process and the Gutenberg-Richter law. The time model serves for estimating the intensity of the aftershock flow with a magnitude larger than or equal to the magnitude of completeness. The Gutenberg-Richter law is used for magnitude scaling. The suggested approach implements successive refinement of the parameters of both components of the method, which is the main novelty distinguishing it from the previous ones. This approach, to a significant extent, takes into account the variations in the parameters of the frequency-magnitude distribution, which often show themselves by the decreasing fraction of stronger aftershocks with time. Testing the method on eight aftershock sequences in the regions with different patterns of seismicity demonstrates the high probability of successful forecasts. The suggested technique can be employed in seismological monitoring centers for forecasting the aftershock activity of a strong earthquake based on the results of operational processing.
Passive earthquake-resistance through base isolation
Wu, Ting-Shu; Seidensticker, R.W.
1990-04-01
Base isolation is an effective approach in mitigating the seismic forces transmitted to the superstructure. It is able to provide reliable protection for the superstructure, its contents and occupants. With base isolation, dynamic characteristics of the superstructure and its contents become more predictable and controllable. In recent years, different base isolation systems have been installed in various new and existing buildings, including office buildings, computer centers, buildings for high technology industries, emergency and communication centers, buildings for high technology industries, emergency and communication centers. The concept of seismic isolation is also under active investigation for nuclear power plants, where safety and reliability of these systems are of utmost importance. One such system uses laminated high-damping rubber and steel shim plates vulcanized into a solid bearing. Under a joint program between Shimizu of Japan and Argonne National Laboratory of the United States, such high-damping rubber bearings were installed in the spring of 1989 in a three-story full size test building at the Tohoku University, Sendai, Japan. Within a period of six months after their installation, this test building and the bearings have experienced more than fifteen earthquakes. Valuable information on the performance of the isolation bearings and the building has been (and is continuing to be) gathered and analyzed. Simulation studies of the effects of these earthquakes using lumped mass systems have been conducted, and are in close agreement with the actual observed responses. 3 refs., 11 figs.
Demand forecast model based on CRM
Cai, Yuancui; Chen, Lichao
2006-11-01
With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.
Flood forecasting for River Mekong with data-based models
Shahzad, Khurram M.; Plate, Erich J.
2014-09-01
In many regions of the world, the task of flood forecasting is made difficult because only a limited database is available for generating a suitable forecast model. This paper demonstrates that in such cases parsimonious data-based hydrological models for flood forecasting can be developed if the special conditions of climate and topography are used to advantage. As an example, the middle reach of River Mekong in South East Asia is considered, where a database of discharges from seven gaging stations on the river and 31 rainfall stations on the subcatchments between gaging stations is available for model calibration. Special conditions existing for River Mekong are identified and used in developing first a network connecting all discharge gages and then models for forecasting discharge increments between gaging stations. Our final forecast model (Model 3) is a linear combination of two structurally different basic models: a model (Model 1) using linear regressions for forecasting discharge increments, and a model (Model 2) using rainfall-runoff models. Although the model based on linear regressions works reasonably well for short times, better results are obtained with rainfall-runoff modeling. However, forecast accuracy of Model 2 is limited by the quality of rainfall forecasts. For best results, both models are combined by taking weighted averages to form Model 3. Model quality is assessed by means of both persistence index PI and standard deviation of forecast error.
S. Ergintav
2012-04-01
Full Text Available The 2006 Mb = 5.3 Manyas-Kus Golu (Manyas earthquake has been retrospectively "stress-forecasted" using variations in time-delays of seismic shear wave splitting to evaluate the time and magnitude at which stress-modified microcracking reaches fracture criticality within the stressed volume where strain is released. We processed micro earthquakes recorded by 29 TURDEP (Multi-Disciplinary Earthquake Research in High Risk Regions of Turkey and 33 KOERI (Kandilli Observatory and Earthquake Research Institute stations in the Marmara region by using the aspect-ratio cross-correlation and systematic analysis of crustal anisotropy methods. The aim of the analysis is to determine changes in delay-times, hence changes in stress, before and after the 2006 Manyas earthquake. We observed that clear decreases in delay times before the impending event, especially at the station GEMT are consistent with the anisotropic poro-elasticity (APE model of fluid-rock deformation, but we could not observe similar changes at other stations surrounding the main event. The logarithms of the duration of the stress-accumulation are proportional (self-similar to the magnitude of the impending event. Although time and magnitude of th 2005 Manyas earthquake could have been stress-forecasted, as has been recognized elsewhere, shear-wave splitting does not appear to provide direct information about the location of impending earthquakes.
Wei, Ben-Yong; Nie, Gao-Zhong; Su, Gui-Wu; Sun, Lei
2017-04-01
China is one of the most earthquake prone countries in the world. The priority during earthquake emergency response is saving lives and minimizing casualties. Rapid judgment of the trapped location is the important basis for government to reasonable arrange the emergency rescue forces and resources after the earthquake. Through analyzing the key factors resulting in people trapped, we constructed an assessment model of personal trapped (PTED)in collapsed buildings caused by earthquake disaster. Then taking the 2014 Ludian Earthquake as a case, this study evaluated the distribution of trapped personal during this earthquake using the assessment model based on km grid data. Results showed that, there are two prerequisites for people might be trapped by the collapse of buildings in earthquake: earthquake caused buildings collapse and there are people in building when building collapsing; the PTED model could be suitable to assess the trapped people in collapsed buildings caused by earthquake. The distribution of people trapped by the collapse of buildings in the Ludian earthquake assessed by the model is basically the same as that obtained by the actual survey. Assessment of people trapped in earthquake based on km grid can meet the requirements of search-and-rescue zone identification and rescue forces allocation in the early stage of the earthquake emergency. In future, as the basic data become more complete, assessment of people trapped in earthquake based on km grid should provide more accurate and valid suggestions for earthquake emergency search and rescue.
Weather forecasting based on hybrid neural model
Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.
2017-02-01
Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.
Reliability forecasting of vehicles basing upon the graph of states
Abramovich, M. S.; Prikhodko, Yu.
2010-01-01
The forecasting technique of reliability measures is described for the vehicle MAZ in monitoring maintenance. The base of the technique is mathematical model of vehicle maintenance as stochastic walk over a graph of state. The offered model enables to make a forecast of reliability measures both in time, and under the changing maintenance conditions or maintenance policy.
Tide forecasting method based on dynamic weight distribution for operational evaluation
Shao-wei QIU; Zeng-chuan DONG; Fen XU; Li SUN; Sheng CHEN
2009-01-01
Through analysis of operational evaluation factors for tide forecasting, the relationship between the evaluation factors and the weights of forecasters was examined. A tide forecasting method based on dynamic weight distribution for operational evaluation was developed, and multiple-forecaster synchronous forecasting was realized while avoiding the instability cased by only one forecaster. Weights were distributed to the forecasters according to each one's forecast precision. An evaluation criterion for the professional level of the forecasters was also built. The eligibility rates of forecast results demonstrate the skill of the forecasters and the stability of their forecasts. With the developed tide forecasting method, the precision and reasonableness of tide forecasting are improved. The application of the present method to tide forecasting at the Huangpu Park tidal station demonstrates the validity of the method.
Operational forecasting based on a modified Weather Research and Forecasting model
Lundquist, J; Glascoe, L; Obrecht, J
2010-03-18
Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.
Xiaoyan Zhang
2015-01-01
Full Text Available This study evaluates the impact of Tropospheric Airborne Meteorological Data Reporting (TAMDAR observations on regional 24-hour forecast error reduction over the Continental United States (CONUS domain using adjoint-based forecast sensitivity to observation (FSO method as the diagnostic tool. The relative impact of TAMDAR observations on reducing the forecast error was assessed by conducting the WRFDA FSO experiments for two two-week-long periods, one in January and one in June 2010. These experiments assimilated operational TAMDAR data and other conventional observations, as well as GPS refractivity (GPSREF. FSO results show that rawinsonde soundings (SOUND and TAMDAR exhibit the largest observation impact on 24 h WRF forecast, followed by GeoAMV, aviation routine weather reports (METAR, GPSREF, and synoptic observations (SYNOP. At 0000 and 1200 UTC, TAMDAR has an equivalent impact to SOUND in reducing the 24-hour forecast error. However, at 1800 UTC, TAMDAR has a distinct advantage over SOUND, which has the sparse observation report at these times. In addition, TAMDAR humidity observations at lower levels of the atmosphere (700 and 850 hPa have a significant impact on 24 h forecast error reductions. TAMDAR and SOUND observations present a qualitatively similar observation impact between FSO and Observation System Experiments (OSEs.
无
2012-01-01
正A serious earthquake happened in Wenchuan, Sichuan. Over 60,000 people died in the earhtquake, millins of people lost their homes. After the earthquake, people showed their love in different ways. Some gave food, medicine and everything necessary, some gave money,
Dynamic evaluation of seismic hazard and risks based on the Unified Scaling Law for Earthquakes
Kossobokov, V. G.; Nekrasova, A.
2016-12-01
We continue applying the general concept of seismic risk analysis in a number of seismic regions worldwide by constructing seismic hazard maps based on the Unified Scaling Law for Earthquakes (USLE), i.e. log N(M,L) = A + B•(6 - M) + C•log L, where N(M,L) is the expected annual number of earthquakes of a certain magnitude M within an seismically prone area of linear dimension L, A characterizes the average annual rate of strong (M = 6) earthquakes, B determines the balance between magnitude ranges, and C estimates the fractal dimension of seismic locus in projection to the Earth surface. The parameters A, B, and C of USLE are used to assess, first, the expected maximum magnitude in a time interval at a seismically prone cell of a uniform grid that cover the region of interest, and then the corresponding expected ground shaking parameters. After a rigorous testing against the available seismic evidences in the past (e.g., the historically reported macro-seismic intensity or paleo data), such a seismic hazard map is used to generate maps of specific earthquake risks for population, cities, and infrastructures. The hazard maps for a given territory change dramatically, when the methodology is applied to a certain size moving time window, e.g. about a decade long for an intermediate-term regional assessment or exponentially increasing intervals for a daily local strong aftershock forecasting. The of dynamical seismic hazard and risks assessment is illustrated by applications to the territory of Greater Caucasus and Crimea and the two-year series of aftershocks of the 11 October 2008 Kurchaloy, Chechnya earthquake which case-history appears to be encouraging for further systematic testing as potential short-term forecasting tool.
Jalayer, Fatemeh; Ebrahimian, Hossein
2014-05-01
Introduction The first few days elapsed after the occurrence of a strong earthquake and in the presence of an ongoing aftershock sequence are quite critical for emergency decision-making purposes. Epidemic Type Aftershock Sequence (ETAS) models are used frequently for forecasting the spatio-temporal evolution of seismicity in the short-term (Ogata, 1988). The ETAS models are epidemic stochastic point process models in which every earthquake is a potential triggering event for subsequent earthquakes. The ETAS model parameters are usually calibrated a priori and based on a set of events that do not belong to the on-going seismic sequence (Marzocchi and Lombardi 2009). However, adaptive model parameter estimation, based on the events in the on-going sequence, may have several advantages such as, tuning the model to the specific sequence characteristics, and capturing possible variations in time of the model parameters. Simulation-based methods can be employed in order to provide a robust estimate for the spatio-temporal seismicity forecasts in a prescribed forecasting time interval (i.e., a day) within a post-main shock environment. This robust estimate takes into account the uncertainty in the model parameters expressed as the posterior joint probability distribution for the model parameters conditioned on the events that have already occurred (i.e., before the beginning of the forecasting interval) in the on-going seismic sequence. The Markov Chain Monte Carlo simulation scheme is used herein in order to sample directly from the posterior probability distribution for ETAS model parameters. Moreover, the sequence of events that is going to occur during the forecasting interval (and hence affecting the seismicity in an epidemic type model like ETAS) is also generated through a stochastic procedure. The procedure leads to two spatio-temporal outcomes: (1) the probability distribution for the forecasted number of events, and (2) the uncertainty in estimating the
Inazu, Daisuke; Pulido, Nelson; Fukuyama, Eiichi; Saito, Tatsuhiko; Senda, Jouji; Kumagai, Hiroyuki
2016-05-01
We have developed a near-field tsunami forecast system based on an automatic centroid moment tensor (CMT) estimation using regional broadband seismic observation networks in the regions of Indonesia, the Philippines, and Chile. The automatic procedure of the CMT estimation has been implemented to estimate tsunamigenic earthquakes. A tsunami propagation simulation model is used for the forecast and hindcast. A rectangular fault model based on the estimated CMT is employed to represent the initial condition of tsunami height. The forecast system considers uncertainties due to two possible fault planes and two possible scaling laws and thus shows four possible scenarios with these associated uncertainties for each estimated CMT. The system requires approximately 15 min to estimate the CMT after the occurrence of an earthquake and approximately another 15 min to make the tsunami forecast results including the maximum tsunami height and its arrival time at the epicentral region and near-field coasts available. The retrospectively forecasted tsunamis were evaluated by the deep-sea pressure and tide gauge observations, for the past eight tsunamis ( M w 7.5-8.6) that occurred throughout the regional seismic networks. The forecasts ranged from half to double the amplitudes of the deep-sea pressure observations and ranged mostly within the same order of magnitude as the maximum heights of the tide gauge observations. It was found that the forecast uncertainties increased for greater earthquakes (e.g., M w > 8) because the tsunami source was no longer approximated as a point source for such earthquakes. The forecast results for the coasts nearest to the epicenter should be carefully used because the coasts often experience the highest tsunamis with the shortest arrival time (e.g., <30 min).
Ma Weiyu; Zhang Xingcai; Dai Xiaofang; Xie Fang
2007-01-01
Taking the three earthquakes which occurred in Tibet,China during the period of July 12 to August 25,2004 as an example,the paper analyses the Ms≥6.0 earthquakes that occurred in China and Ms≥7.0 earthquakes that occurred overseas since May of 2003 by combining the jmage data from the National Center for Environmental Prediction of America (NCEP) with the additive tectonic stress from astro-tidal-triggering (ATSA) and makes the following conclusions:The abnormal temperature image data of NCEP can better reflect the spatial-temporal evolution process of tectonic earthquake activity;The ATSA has an evident triggering effect on the activity of a fault when the terra stress is in critical status; using the NCEP images and the ATSA to forecast short-impending earthquake is a new concept:The three earthquakes occurred during the same phase of the respective ATSA cycle,i.e.that occurred at the time when the ATSA reached the relatively steady end of a peak,rather than at the time when the variation rate was maximal.In addition, the author discovered that the occurrence time of other earthquake cases during 2003～2004 in Tibet was also in the same phase of the above-mentioned cycles,and therefore,further study of this feature is needed with more earthquake eases in other areas over longer periods of time.
Michael A. Fosberg
1987-01-01
Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.
CONSIDERATION OF RECOMMENDATIONS AT INNOVATION-BASED PROJECTS RESULTS FORECASTING
Argov Nikita Vladimirovich
2012-01-01
The purpose of this paper is to highlight the importance of considering the factor of word-of-mouth communications between clients when analyzing the innovation-based projects. The paper offers the methodology of evaluating the importance of such analyses for different innovative projects. Results of the research are in the specifying the demand forecasting at innovation-based projects outcomes forecasting. Practical implications lie at the evaluation of such projects, specially by small and ...
CONSIDERATION OF RECOMMENDATIONS AT INNOVATION-BASED PROJECTS RESULTS FORECASTING
Argov Nikita Vladimirovich
2012-01-01
The purpose of this paper is to highlight the importance of considering the factor of word-of-mouth communications between clients when analyzing the innovation-based projects. The paper offers the methodology of evaluating the importance of such analyses for different innovative projects. Results of the research are in the specifying the demand forecasting at innovation-based projects outcomes forecasting. Practical implications lie at the evaluation of such projects, specially by small and ...
Long-Term Earthquake Forecasts in the San Francisco Bay Area: A Contrarian Perspective
Lindh, A. G.
2003-12-01
In historic time the San Francisco Bay Area (SFBA) has been the site of four large earthquakes, including the M7.8 1906 San Francisco earthquake, and most recently, the M6.9 1989 Loma Prieta earthquake. Of the eight major fault segments considered here, two have not experienced large earthquakes in about 200 years, and the SF Peninsula segment of the 1906 rupture on the San Andreas appears from my calculations to be close to fully reloaded as well. I have used simple geophysical and statistical models (elastic rebound model and Weibull distribution) to estimate the probability of large earthquakes (M7 or larger) in the SFBA in the coming decades. I have used seismicity, geology, and geodesy to estimate segment boundaries, recurrence intervals, and the associated uncertainties. The results indicate that the SFBA has an approximately 80% chance of a large earthquake in the next 30 years, with four segments dominating the 30 yr probabilities; San Francisco Peninsula (32%), Southern Hayward (39%), Northern Hayward (28%) and Rodgers Cr (30%). Because of the proximity of these four segments to the urban portions of San Francisco and Oakland, the probability of these most vulnerable areas experiencing strong ground motion (an M7 within 25 km or less) one or more times within the next 30 years is about 70%. Because of the breadth and quality of our understanding of the earthquake machine in the SFBA, these probabilities depend in large part on the intrinsic variance in the earthquake recurrence process itself -- most conveniently expressed as the ratio of the standard deviation to the mean recurrence time, or intrinsic coefficient of variation (CVI). I have applied a new approach to estimating CVI, using the time since the last characteristic event (the "open-interval") on well characterized segments. Combined with an estimate of the mean recurrence time on each segment, an estimate of the likelihood of each open interval can be computed, and a simple maximum likelihood
Self-Organizing Maps-based ocean currents forecasting system
Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir
2016-03-01
An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.
Field, Ned; Biasi, Glenn P.; Bird, Peter; Dawson, Timothy E.; Felzer, Karen R.; Jackson, David A.; Johnson, Kaj M.; Jordan, Thomas H.; Madden, Christopher; Michael, Andrew J.; Milner, Kevin; Page, Morgan T.; Parsons, Thomas E.; Powers, Peter; Shaw, Bruce E.; Thatcher, Wayne R.; Weldon, Ray J.; Zeng, Yuehua
2015-01-01
The 2014 Working Group on California Earthquake Probabilities (WGCEP 2014) presents time-dependent earthquake probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3). Building on the UCERF3 time-independent model, published previously, renewal models are utilized to represent elastic-rebound-implied probabilities. A new methodology has been developed that solves applicability issues in the previous approach for un-segmented models. The new methodology also supports magnitude-dependent aperiodicity and accounts for the historic open interval on faults that lack a date-of-last-event constraint. Epistemic uncertainties are represented with a logic tree, producing 5,760 different forecasts. Results for a variety of evaluation metrics are presented, including logic-tree sensitivity analyses and comparisons to the previous model (UCERF2). For 30-year M≥6.7 probabilities, the most significant changes from UCERF2 are a threefold increase on the Calaveras fault and a threefold decrease on the San Jacinto fault. Such changes are due mostly to differences in the time-independent models (e.g., fault slip rates), with relaxation of segmentation and inclusion of multi-fault ruptures being particularly influential. In fact, some UCERF2 faults were simply too long to produce M 6.7 sized events given the segmentation assumptions in that study. Probability model differences are also influential, with the implied gains (relative to a Poisson model) being generally higher in UCERF3. Accounting for the historic open interval is one reason. Another is an effective 27% increase in the total elastic-rebound-model weight. The exact factors influencing differences between UCERF2 and UCERF3, as well as the relative importance of logic-tree branches, vary throughout the region, and depend on the evaluation metric of interest. For example, M≥6.7 probabilities may not be a good proxy for other hazard or loss measures. This sensitivity, coupled with the
Radar Based Flow and Water Level Forecasting in Sewer Systems
Thorndahl, Søren; Rasmussen, Michael R.; Grum, M.
2009-01-01
This paper describes the first radar based forecast of flow and/or water level in sewer systems in Denmark. The rainfall is successfully forecasted with a lead time of 1-2 hours, and flow/levels are forecasted an additional ½-1½ hours using models describing the behaviour of the sewer system. Both...... radar data and flow/water level model are continuously updated using online rain gauges and online in-sewer measurements, in order to make the best possible predictions. The project show very promising results, and show large potentials, exploiting the existing water infrastructure in future climate...
Ohta, Y.; Tsushima, H.; Kawamoto, S.; Miyagawa, K.; Yahagi, T.; Sato, Y.; Hino, R.; Demachi, T.; Iinuma, T.; Miura, S.
2014-12-01
The 2011 Tohoku earthquake and its associated tsunami clearly showed the need for an accurate tsunami early warning system. In a short time between the occurrence of earthquakes and associating tsunamis and the tsunami arrivals to near-field coastal inhabited regions, we can use many different kinds of observations for real-time tsunami forecasting. Since individual type of the observations has its advantages and disadvantages, it is strongly required to make use of multiple kinds of data for improving estimated size and arrival timing of imminent tsunamis by reinforcing one another. For example, the rapid analysis of short-period seismic wave data, such as earthquake early warning system in Japan will provide the first information on the size and location of an earthquake, helping issuing tsunami information immediately after earthquakes. Real-time GNSS data have an advantage over the short-time seismograms because robust estimations of location and dimension of coseismic faults can be derived from spatial patterns of permanent coseismic displacement measured by real-time GNSS data. It is one of the important lessons learnt from the 2011 Tohoku earthquake that estimation of reliable finite source fault models is indispensable in tsunami forecasting after massive earthquakes. Offshore measurements of coming tsunamis must be data most relevant to the arrival times and sizes of tsunamis along shorelines. However, it takes more time to obtain credible spatial distribution of tsunami wave height from the observations due to much slower propagation of tsunamis than seismic waves and deformations. In the presentation, we will introduce the current status of the real-time crustal deformation monitoring system based on the GNSS data developed by Geospatial Information Authority of Japan and Tohoku University. We also briefly introduce the real-time tsunami forecasting based on the offshore tsunami data, developed by the Meteorological Research Institute of Japan
Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs
Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan
2016-04-01
Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more
Deep Neural Network Based Demand Side Short Term Load Forecasting
Seunghyoung Ryu
2016-12-01
Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.
EARTHQUAKE PREDICTION BASED ON THE HYDROGEODEFORMATION FIELD MONITORING DATA
Gennady V. Kulikov
2015-09-01
Full Text Available The paper discusses further ways to improve the geodynamic informativity of the hydrogeodeformation field (HGD field monitoring. New methods for efficient assessment of the stressstrain state of the geological environment and seismic hazard are proposed. There are described the methods of monitoring data processing, distinguishing of HGD cycles, and construction of «forecasting» contours along extremums of these cycles. It is revealed that responses of the HGD field to development of planetaryscale endogenic geodynamic processes of earthquake preparation (with M>7 are simultaneously manifested in all seismically active regions of Russia which are remote from each other. Such responses occur from one to three months prior to such seismic events. The mechanism of this phenomenon can be disputed. The authors support the «planetary pulsation» concept which is up for the most recent debates. As evidenced by the HGD field monitoring data, strong earthquakes are a consequence of this phenomenon.
ECONOMIC FORECASTS BASED ON ECONOMETRIC MODELS USING EViews 5
Cornelia TomescuDumitrescu,
2009-05-01
Full Text Available The forecast of evolution of economic phenomena represent on the most the final objective of econometrics. It withal represent a real attempt of validity elaborate model. Unlike the forecasts based on the study of temporal series which have an recognizable inertial character the forecasts generated by econometric model with simultaneous equations are after to contour the future of ones of important economic variables toward the direct and indirect influences bring the bear on their about exogenous variables. For the relief of the calculus who the realization of the forecasts based on the econometric models its suppose is indicate the use of the specialized informatics programs. One of this is the EViews which is applied because it reduces significant the time who is destined of the econometric analysis and it assure a high accuracy of calculus and of the interpretation of results.
Attractor-based models for individual and groups’ forecasting
Astakhova, N. N.; Demidova, L. A.; Kuzovnikov, A. V.; Tishkin, R. V.
2017-02-01
In this paper the questions of the attractors’ application in case of the development of the forecasting models on the base of the strictly binary trees have been considered. Usually, these models use the short time series as the training data sequence. The application of the principles of the attractors’ forming on the base of the long time series will allow creating the training data sequence more reasonably. The offered approach to creation of the training data sequence for the forecasting models on the base of the strictly binary trees was applied for the individual and groups’ forecasting of time series. At the same time the problems of one-objective and multiobjective optimization on the base of the modified clonal selection algorithm have been considered. The reviewed examples confirm the efficiency of the attractors’ application in sense of minimization of the used quality indicators of the forecasting models, and also the forecasting errors on 1 – 5 steps forward. Besides, the minimization of time expenditures for the development of the forecasting models is provided.
Parvez, Imtiyaz A.; Nekrasova, Anastasia; Kossobokov, Vladimir
2017-03-01
The Gujarat state of India is one of the most seismically active intercontinental regions of the world. Historically, it has experienced many damaging earthquakes including the devastating 1819 Rann of Kachchh and 2001 Bhuj earthquakes. The effect of the later one is grossly underestimated by the Global Seismic Hazard Assessment Program (GSHAP). To assess a more adequate earthquake hazard for the state of Gujarat, we apply Unified Scaling Law for Earthquakes (USLE), which generalizes the Gutenberg-Richter recurrence relation taking into account naturally fractal distribution of earthquake loci. USLE has evident implications since any estimate of seismic hazard depends on the size of the territory considered and, therefore, may differ dramatically from the actual one when scaled down to the proportion of the area of interest (e.g. of a city) from the enveloping area of investigation. We cross-compare the seismic hazard maps compiled for the same standard regular grid 0.2° × 0.2° (1) in terms of design ground acceleration based on the neo-deterministic approach, (2) in terms of probabilistic exceedance of peak ground acceleration by GSHAP, and (3) the one resulted from the USLE application. Finally, we present the maps of seismic risks for the state of Gujarat integrating the obtained seismic hazard, population density based on India's Census 2011 data, and a few model assumptions of vulnerability.
Tsushima, H.; Hayashi, Y.; Maeda, K.; Yokota, T.
2013-12-01
Near-field tsunamis in areas close to subduction zones can reach the coast in a few tens of minutes or less, and cause loss of life as well as severe damage to houses and infrastructures in coastal communities. Real-time tsunami forecasting is one of the effective ways to mitigate tsunami disasters. Transmission of a tsunami warning based on rapid and accurate tsunami forecasting to coastal communities helps the residents to make the decisions about their evacuation behaviors. Offshore tsunami data take an important role in tsunami forecasting. Tsunamis can be detected at offshore stations earlier than at coastal sites, and the data provide direct information about the impending tsunamis. In this paper, we present a method to forecast near-field tsunamis from offshore tsunami data using inversion and tsunami amplification factor techniques. We also introduce a prototype of tsunami forecasting system in which our forecasting method is installed. Our tsunami forecasting algorithm is based on a source estimation. For the algorithm, offshore tsunami waveform data are inverted for spatial distribution of an initial sea-surface displacement, and then tsunami waveforms are synthesized from the estimated source and pre-computed Green's functions by a linear superposition to forecast tsunamis at an offshore point near a coastal site. The predicted tsunami heights at the offshore points are amplified to obtain those at coastal sites using the amplification factors derived from actual tsunami observations empirically. No assumptions concerning the fault geometry and the size of an earthquake are required in the algorithm. An empirical amplification factor includes the effect of actual topography on tsunami heights that should be difficult to be modeled by the linear combination of the Green's functions. The predictions are repeated by progressively updating the offshore tsunami waveform data. Because individual predictions can be calculated within a few minutes, tsunami
A new earthquake location method based on the waveform inversion
Wu, Hao; Huang, Xueyuan; Yang, Dinghui
2016-01-01
In this paper, a new earthquake location method based on the waveform inversion is proposed. As is known to all, the waveform misfit function is very sensitive to the phase shift between the synthetic waveform signal and the real waveform signal. Thus, the convergence domain of the conventional waveform based earthquake location methods is very small. In present study, by introducing and solving a simple sub-optimization problem, we greatly expand the convergence domain of the waveform based earthquake location method. According to a large number of numerical experiments, the new method expands the range of convergence by several tens of times. This allows us to locate the earthquake accurately even from some relatively bad initial values.
Electricity Price Forecasting Based on AOSVR and Outlier Detection
Zhou Dianmin; Gao Lin; Gao Feng
2005-01-01
Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.
Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model
Haixiang Zang; Lei Fan; Mian Guo; Zhinong Wei; Guoqiang Sun; Li Zhang
2016-01-01
Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EE...
SOFT project: a new forecasting system based on satellite data
Pascual, Ananda; Orfila, A.; Alvarez, Alberto; Hernandez, E.; Gomis, D.; Barth, Alexander; Tintore, Joaquim
2002-01-01
The aim of the SOFT project is to develop a new ocean forecasting system by using a combination of satellite dat, evolutionary programming and numerical ocean models. To achieve this objective two steps are proved: (1) to obtain an accurate ocean forecasting system using genetic algorithms based on satellite data; and (2) to integrate the above new system into existing deterministic numerical models. Evolutionary programming will be employed to build 'intelligent' systems that, learning form the past ocean variability and considering the present ocean state, will be able to infer near future ocean conditions. Validation of the forecast skill will be carried out by comparing the forecasts fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the forecasting system. Validation with in situ data will indicate the capabilities of the satellite based forecast information to improve the performance of the numerical ocean models. This later validation will be accomplished considering in situ measurements in a specific oceanographic area at two different periods of time. The first set of observations will be employed to feed the hybrid systems while the second set will be used to validate the hybrid and traditional numerical model results.
Weather-based forecasts of California crop yields
Lobell, D B; Cahill, K N; Field, C B
2005-09-26
Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over the 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.
Relationship between earthquake and volcanic eruption inferred from historical records
陈洪洲; 高峰; 吴雪娟; 孟宪森
2004-01-01
A large number of seismic records are discovered for the first time in the historical materials about Wudalianchi volcanic group eruption in 1720～1721, which provides us with abundant volcanic earthquake information. Based on the written records, the relationship between earthquake and volcanic eruption is discussed in the paper. Furthermore it is pointed that earthquake swarm is an important indication of volcanic eruption. Therefore, monitoring volcanic earthquakes is of great significance for forecasting volcanic eruption.
Earthquake insurance pricing: a risk-based approach.
Lin, Jeng-Hsiang
2017-05-23
Flat earthquake premiums are 'uniformly' set for a variety of buildings in many countries, neglecting the fact that the risk of damage to buildings by earthquakes is based on a wide range of factors. How these factors influence the insurance premiums is worth being studied further. Proposed herein is a risk-based approach to estimate the earthquake insurance rates of buildings. Examples of application of the approach to buildings located in Taipei city of Taiwan were examined. Then, the earthquake insurance rates for the buildings investigated were calculated and tabulated. To fulfil insurance rating, the buildings were classified into 15 model building types according to their construction materials and building height. Seismic design levels were also considered in insurance rating in response to the effect of seismic zone and construction years of buildings. This paper may be of interest to insurers, actuaries, and private and public sectors of insurance. © 2017 The Author(s). Disasters © Overseas Development Institute, 2017.
DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA
J. Rhee
2016-06-01
Full Text Available The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6 and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6. An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.
Murru, M.; Akinci, A.; Falcone, G.; Pucci, S.; Console, R.; Parsons, T.
2016-04-01
We forecast time-independent and time-dependent earthquake ruptures in the Marmara region of Turkey for the next 30 years using a new fault segmentation model. We also augment time-dependent Brownian passage time (BPT) probability with static Coulomb stress changes (ΔCFF) from interacting faults. We calculate Mw > 6.5 probability from 26 individual fault sources in the Marmara region. We also consider a multisegment rupture model that allows higher-magnitude ruptures over some segments of the northern branch of the North Anatolian Fault Zone beneath the Marmara Sea. A total of 10 different Mw = 7.0 to Mw = 8.0 multisegment ruptures are combined with the other regional faults at rates that balance the overall moment accumulation. We use Gaussian random distributions to treat parameter uncertainties (e.g., aperiodicity, maximum expected magnitude, slip rate, and consequently mean recurrence time) of the statistical distributions associated with each fault source. We then estimate uncertainties of the 30 year probability values for the next characteristic event obtained from three different models (Poisson, BPT, and BPT + ΔCFF) using a Monte Carlo procedure. The Gerede fault segment located at the eastern end of the Marmara region shows the highest 30 year probability, with a Poisson value of 29% and a time-dependent interaction probability of 48%. We find an aggregated 30 year Poisson probability of M > 7.3 earthquakes at Istanbul of 35%, which increases to 47% if time dependence and stress transfer are considered. We calculate a twofold probability gain (ratio time dependent to time independent) on the southern strands of the North Anatolian Fault Zone.
M≥7 Earthquake rupture forecast and time-dependent probability for the Sea of Marmara region, Turkey
Murru, Maura; Akinci, Aybige; Falcone, Guiseppe; Pucci, Stefano; Console, Rodolfo; Parsons, Thomas E.
2016-01-01
We forecast time-independent and time-dependent earthquake ruptures in the Marmara region of Turkey for the next 30 years using a new fault-segmentation model. We also augment time-dependent Brownian Passage Time (BPT) probability with static Coulomb stress changes (ΔCFF) from interacting faults. We calculate Mw > 6.5 probability from 26 individual fault sources in the Marmara region. We also consider a multisegment rupture model that allows higher-magnitude ruptures over some segments of the Northern branch of the North Anatolian Fault Zone (NNAF) beneath the Marmara Sea. A total of 10 different Mw=7.0 to Mw=8.0 multisegment ruptures are combined with the other regional faults at rates that balance the overall moment accumulation. We use Gaussian random distributions to treat parameter uncertainties (e.g., aperiodicity, maximum expected magnitude, slip rate, and consequently mean recurrence time) of the statistical distributions associated with each fault source. We then estimate uncertainties of the 30-year probability values for the next characteristic event obtained from three different models (Poisson, BPT, and BPT+ΔCFF) using a Monte Carlo procedure. The Gerede fault segment located at the eastern end of the Marmara region shows the highest 30-yr probability, with a Poisson value of 29%, and a time-dependent interaction probability of 48%. We find an aggregated 30-yr Poisson probability of M >7.3 earthquakes at Istanbul of 35%, which increases to 47% if time dependence and stress transfer are considered. We calculate a 2-fold probability gain (ratio time-dependent to time-independent) on the southern strands of the North Anatolian Fault Zone.
Regime-based forecast performance during WFIP 1
Freedman, J. M.; Zack, J. W.; Manobianco, J.; Beaucage, P.; Rojowsky, K.
2015-12-01
The principal objectives of the first Wind Forecast Improvement Project (WFIP 1) were to improve short-term (0 - 6 hr) wind power forecasts through the assimilation of targeted remote sensing and surface observations with an enhanced model ensemble forcast system. The WFIP 1 field deployment/modeling campaign in the Southern Study Area (SSA--encompassing most of central and western Texas) ran from August 2011 through Septembe 2012. This ensured observational data and model output for all representative weather regimes affecting the SSA. Cold and warm season regimes featured synoptic-scale, convective, and low-level jet (LLJ) phenomena that are responsible for the favorable wind resource in the SSA, and also posed a challenge for assigning specific explanations for the observed forecast improvements (e.g. additional observations, model improvements, or a combination of both). LLJs produced hourly capacity factors exceeding 80% in aggregated wind farm power production, while synoptic-scale systems were responsible for the largest ramp events observed during WFIP 1. Accurately forecasting convective phenomena (such as outflow boundaries) during WFIP 1 was at times problematic. Here, we present regime-based and phenomenological-related forecast performance results for WFIP 1. These performance metrics suggest future research pathways that will facilitate improvements in operational wind power forecasts.
Segou, M.; Parsons, T.
2014-06-01
Main shocks are calculated to cast stress shadows across broad areas where aftershocks occur. Thus, a key problem with stress-based operational forecasts is that they can badly underestimate aftershock occurrence in the shadows. We examine the performance of two physics-based earthquake forecast models (Coulomb rate/state (CRS)) based on Coulomb stress changes and a rate-and-state friction law for their predictive power on the 1989 Mw = 6.9 Loma Prieta aftershock sequence. The CRS-1 model considers the stress perturbations associated with the main shock rupture only, whereas CRS-2 uses an updated stress field with stresses imparted by M ≥ 3.5 aftershocks. Including secondary triggering effects slightly improves predictability, but physics-based models still underestimate aftershock rates in locations of initial negative stress changes. Furthermore, CRS-2 does not explain aftershock occurrence where secondary stress changes enhance the initial stress shadow. Predicting earthquake occurrence in calculated stress shadow zones remains a challenge for stress-based forecasts, and additional triggering mechanisms must be invoked.
Likelihood analysis of earthquake focal mechanism distributions
Kagan, Y Y
2014-01-01
In our paper published earlier we discussed forecasts of earthquake focal mechanism and ways to test the forecast efficiency. Several verification methods were proposed, but they were based on ad-hoc, empirical assumptions, thus their performance is questionable. In this work we apply a conventional likelihood method to measure a skill of forecast. The advantage of such an approach is that earthquake rate prediction can in principle be adequately combined with focal mechanism forecast, if both are based on the likelihood scores, resulting in a general forecast optimization. To calculate the likelihood score we need to compare actual forecasts or occurrences of predicted events with the null hypothesis that the mechanism's 3-D orientation is random. For double-couple source orientation the random probability distribution function is not uniform, which complicates the calculation of the likelihood value. To better understand the resulting complexities we calculate the information (likelihood) score for two rota...
Modern earthquake engineering offshore and land-based structures
Jia, Junbo
2017-01-01
This book addresses applications of earthquake engineering for both offshore and land-based structures. It is self-contained as a reference work and covers a wide range of topics, including topics related to engineering seismology, geotechnical earthquake engineering, structural engineering, as well as special contents dedicated to design philosophy, determination of ground motions, shock waves, tsunamis, earthquake damage, seismic response of offshore and arctic structures, spatial varied ground motions, simplified and advanced seismic analysis methods, sudden subsidence of offshore platforms, tank liquid impacts during earthquakes, seismic resistance of non-structural elements, and various types of mitigation measures, etc. The target readership includes professionals in offshore and civil engineering, officials and regulators, as well as researchers and students in this field.
DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA
Rhee, J; Im, J.; Park, S.
2016-01-01
The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6) and the 6-month Standardized Precipitatio...
Fuzzy forecasting based on fuzzy-trend logical relationship groups.
Chen, Shyi-Ming; Wang, Nai-Yi
2010-10-01
In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.
Baurov, Yu A; Baurov, Yu A; Spitalnaya, A A; Abramyan, A A; Solodovnikov, V A
2008-01-01
To the foundation of a principally new short-term forecasting method there has been laid down a theory of surrounding us world's creation and of physical vacuum as a result of interaction of byuons - discrete objects. The definition of the byuon contains the cosmological vector-potential A_g - a novel fundamental vector constant. This theory predicts a new anisotropic interaction of nature objects with the physical vacuum. A peculiar "tap" to gain new energy (giving rise to an earthquake) are elementary particles because their masses are proportional to the modulus of some summary potential A_sum that contains potentials of all known fields. The value of A_sum cannot be larger than the modulus of A_g. In accordance with the experimental results a new force associated with A_sum ejects substance from the area of the weakened A_sum along a conical formation with the opening of 100 +- 10 and the axis directed along the vector A_sum. This vector has the following coordinates in the second equatorial coordinate sy...
Modeling earthquake activity using a memristor-based cellular grid
Vourkas, Ioannis; Sirakoulis, Georgios Ch.
2013-04-01
Earthquakes are absolutely among the most devastating natural phenomena because of their immediate and long-term severe consequences. Earthquake activity modeling, especially in areas known to experience frequent large earthquakes, could lead to improvements in infrastructure development that will prevent possible loss of lives and property damage. An earthquake process is inherently a nonlinear complex system and lately scientists have become interested in finding possible analogues of earthquake dynamics. The majority of the models developed so far were based on a mass-spring model of either one or two dimensions. An early approach towards the reordering and the improvement of existing models presenting the capacitor-inductor (LC) analogue, where the LC circuit resembles a mass-spring system and simulates earthquake activity, was also published recently. Electromagnetic oscillation occurs when energy is transferred between the capacitor and the inductor. This energy transformation is similar to the mechanical oscillation that takes place in the mass-spring system. A few years ago memristor-based oscillators were used as learning circuits exposed to a train of voltage pulses that mimic environment changes. The mathematical foundation of the memristor (memory resistor), as the fourth fundamental passive element, has been expounded by Leon Chua and later extended to a more broad class of memristors, known as memristive devices and systems. This class of two-terminal passive circuit elements with memory performs both information processing and storing of computational data on the same physical platform. Importantly, the states of these devices adjust to input signals and provide analog capabilities unavailable in standard circuit elements, resulting in adaptive circuitry and providing analog parallel computation. In this work, a memristor-based cellular grid is used to model earthquake activity. An LC contour along with a memristor is used to model seismic activity
Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model
Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun
2014-01-01
Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586
Fuzzy temporal logic based railway passenger flow forecast model.
Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun
2014-01-01
Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.
Preliminary earthquake focal mechanism forecasts for the Amatrice sequence (central Italy
Pamela Roselli
2016-12-01
Full Text Available We place the Amatrice (central Italy seismic sequence and the related epicentral area in a contest of Probabilistic Seismic Hazard Analysis (PSHA. We apply a procedure to compute the probability to observe in the future a normal, reverse or strike-slip event and the average distribution of the P, T and N axes. This is a fundamental step to reduce the uncertainty connected to the Ground Motion Prediction Equation models, part of PSHA. For this purpose we use a significant focal mechanism catalogue and the latest present-day stress field data release for Italy to produce forecasted information that we compare with the equivalent data observed during the sequence.
Learning from physics-based earthquake simulators: a minimal approach
Artale Harris, Pietro; Marzocchi, Warner; Melini, Daniele
2017-04-01
Physics-based earthquake simulators are aimed to generate synthetic seismic catalogs of arbitrary length, accounting for fault interaction, elastic rebound, realistic fault networks, and some simple earthquake nucleation process like rate and state friction. Through comparison of synthetic and real catalogs seismologists can get insights on the earthquake occurrence process. Moreover earthquake simulators can be used to to infer some aspects of the statistical behavior of earthquakes within the simulated region, by analyzing timescales not accessible through observations. The develoment of earthquake simulators is commonly led by the approach "the more physics, the better", pushing seismologists to go towards simulators more earth-like. However, despite the immediate attractiveness, we argue that this kind of approach makes more and more difficult to understand which physical parameters are really relevant to describe the features of the seismic catalog at which we are interested. For this reason, here we take an opposite minimal approach and analyze the behavior of a purposely simple earthquake simulator applied to a set of California faults. The idea is that a simple model may be more informative than a complex one for some specific scientific objectives, because it is more understandable. The model has three main components: the first one is a realistic tectonic setting, i.e., a fault dataset of California; the other two components are quantitative laws for earthquake generation on each single fault, and the Coulomb Failure Function for modeling fault interaction. The final goal of this work is twofold. On one hand, we aim to identify the minimum set of physical ingredients that can satisfactorily reproduce the features of the real seismic catalog, such as short-term seismic cluster, and to investigate on the hypothetical long-term behavior, and faults synchronization. On the other hand, we want to investigate the limits of predictability of the model itself.
Macroeconomic Factors and the German Real Estate Market: A Stock-Market-Based Forecasting Experiment
Christian Pierdzioch
2012-01-01
Based on a recursive forecasting approach, this research studies whether macro- economic factors help to forecast excess returns on a real-estate-based German stock market index. Key findings are that macroeconomic factors are often included in the optimal forecasting model, that their relative importance often differs from their importance for forecasting a broad stock-market index, and that their informational content for forecasting excess returns seems to undergo temporal shifts. This res...
A physically-based earthquake recurrence model for estimation of long-term earthquake probabilities
Ellsworth, William L.; Matthews, Mark V.; Nadeau, Robert M.; Nishenko, Stuart P.; Reasenberg, Paul A.; Simpson, Robert W.
1999-01-01
A physically-motivated model for earthquake recurrence based on the Brownian relaxation oscillator is introduced. The renewal process defining this point process model can be described by the steady rise of a state variable from the ground state to failure threshold as modulated by Brownian motion. Failure times in this model follow the Brownian passage time (BPT) distribution, which is specified by the mean time to failure, μ, and the aperiodicity of the mean, α (equivalent to the familiar coefficient of variation). Analysis of 37 series of recurrent earthquakes, M -0.7 to 9.2, suggests a provisional generic value of α = 0.5. For this value of α, the hazard function (instantaneous failure rate of survivors) exceeds the mean rate for times > μ⁄2, and is ~ ~ 2 ⁄ μ for all times > μ. Application of this model to the next M 6 earthquake on the San Andreas fault at Parkfield, California suggests that the annual probability of the earthquake is between 1:10 and 1:13.
Regression based peak load forecasting using a transformation technique
Haida, Takeshi; Muto, Shoichi (Tokyo Electric Power Co. (Japan). Computer and Communication Research Center)
1994-11-01
This paper presents a regression based daily peak load forecasting method with a transformation technique. In order to forecast the load precisely through a year, the authors should consider seasonal load change, annual load growth and the latest daily load change. To deal with these characteristics in the load forecasting, a transformation technique is presented. This technique consists of a transformation function with translation and reflection methods. The transformation function is estimated with the previous year's data points, in order that the function converts the data points into a set of new data points with preserving the shape of temperature-load relationships in the previous year. Then, the function is slightly translated so that the transformed data points will fit the shape of temperature-load relationships in the year. Finally, multivariate regression analysis with the latest daily loads and weather observations estimates the forecasting model. Large forecasting errors caused by the weather-load nonlinear characteristic in the transitional seasons such as spring and fall are reduced. Performance of the technique which is verified with simulations on actual load data of Tokyo Electric Power Company is also described.
V. Tramutoli
1996-06-01
Full Text Available An autoregressive model was selected to describe geoelectrical time series. An objective technique was subsequently applied to analyze and discriminate values above (below an a priorifixed threshold possibly related to seismic events. A complete check of the model and the main guidelines to estimate the occurrence probability of extreme events are reported. A first application of the proposed technique is discussed through the analysis of the experimental data recorded by an automatic station located in Tito, a small town on the Apennine chain in Southern Italy. This region was hit by the November 1980 Irpinia-Basilicata earthquake and it is one of most active areas of the Mediterranean region. After a preliminary filtering procedure to reduce the influence of external parameters (i.e. the meteo-climatic effects, it was demonstrated that the geoelectrical residual time series are well described by means of a second order autoregressive model. Our findings outline a statistical methodology to evaluate the efficiency of electrical seismic precursors.
Magnitude Estimation for the 2011 Tohoku-Oki Earthquake Based on Ground Motion Prediction Equations
Eshaghi, Attieh; Tiampo, Kristy F.; Ghofrani, Hadi; Atkinson, Gail M.
2015-08-01
This study investigates whether real-time strong ground motion data from seismic stations could have been used to provide an accurate estimate of the magnitude of the 2011 Tohoku-Oki earthquake in Japan. Ultimately, such an estimate could be used as input data for a tsunami forecast and would lead to more robust earthquake and tsunami early warning. We collected the strong motion accelerograms recorded by borehole and free-field (surface) Kiban Kyoshin network stations that registered this mega-thrust earthquake in order to perform an off-line test to estimate the magnitude based on ground motion prediction equations (GMPEs). GMPEs for peak ground acceleration and peak ground velocity (PGV) from a previous study by Eshaghi et al. in the Bulletin of the Seismological Society of America 103. (2013) derived using events with moment magnitude ( M) ≥ 5.0, 1998-2010, were used to estimate the magnitude of this event. We developed new GMPEs using a more complete database (1998-2011), which added only 1 year but approximately twice as much data to the initial catalog (including important large events), to improve the determination of attenuation parameters and magnitude scaling. These new GMPEs were used to estimate the magnitude of the Tohoku-Oki event. The estimates obtained were compared with real time magnitude estimates provided by the existing earthquake early warning system in Japan. Unlike the current operational magnitude estimation methods, our method did not saturate and can provide robust estimates of moment magnitude within ~100 s after earthquake onset for both catalogs. It was found that correcting for average shear-wave velocity in the uppermost 30 m () improved the accuracy of magnitude estimates from surface recordings, particularly for magnitude estimates of PGV (Mpgv). The new GMPEs also were used to estimate the magnitude of all earthquakes in the new catalog with at least 20 records. Results show that the magnitude estimate from PGV values using
Volcano-tectonic earthquakes: A new tool for estimating intrusive volumes and forecasting eruptions
White, Randall A.; McCausland, Wendy
2016-01-01
We present data on 136 high-frequency earthquakes and swarms, termed volcano-tectonic (VT) seismicity, which preceded 111 eruptions at 83 volcanoes, plus data on VT swarms that preceded intrusions at 21 other volcanoes. We find that VT seismicity is usually the earliest reported seismic precursor for eruptions at volcanoes that have been dormant for decades or more, and precedes eruptions of all magma types from basaltic to rhyolitic and all explosivities from VEI 0 to ultraplinian VEI 6 at such previously long-dormant volcanoes. Because large eruptions occur most commonly during resumption of activity at long-dormant volcanoes, VT seismicity is an important precursor for the Earth's most dangerous eruptions. VT seismicity precedes all explosive eruptions of VEI ≥ 5 and most if not all VEI 4 eruptions in our data set. Surprisingly we find that the VT seismicity originates at distal locations on tectonic fault structures at distances of one or two to tens of kilometers laterally from the site of the eventual eruption, and rarely if ever starts beneath the eruption site itself. The distal VT swarms generally occur at depths almost equal to the horizontal distance of the swarm from the summit out to about 15 km distance, beyond which hypocenter depths level out. We summarize several important characteristics of this distal VT seismicity including: swarm-like nature, onset days to years prior to the beginning of magmatic eruptions, peaking of activity at the time of the initial eruption whether phreatic or magmatic, and large non-double couple component to focal mechanisms. Most importantly we show that the intruded magma volume can be simply estimated from the cumulative seismic moment of the VT seismicity from:
Company Management Based on the Forecast in Product Area
Aleksandr Mikhaylovich Pishchukhin
2017-03-01
Full Text Available The article discusses the forecasting method based on the research of the behaviour of the line of total production of the companies-competitors in the product area, first of all, what products and how much to produce. Therefore, if to monitor developments in multi-dimensional space, whose coordinates are the volumes of production and demand for all types of products from the range, now mastered, then this picture will reflect the major events taking place in the market and determine the location of the enterprise. Naturally, it is very convenient, on the basis of the multidimensional space, to predict the main trends and strategize the behaviour of the enterprise. The aim of this study is to search for the forecasting method in this multidimensional product area and its substantiation. Every company in this area can be represented by a multidimensional parallelepiped, whose diagonal in an integrated manner displays the capabilities of the enterprise for the production of the whole range. If in this area, we consistently combine the angles of parallelepipeds for all competitors, the corner of the last parallelepiped will indicate the total capacity of all competing companies for filling the market with products. Accordingly, the “missing” vector drawn to the point reflecting the market needs, determines a parallelepiped for the selected enterprise, for which the prognosis is being made. Changing the coordinate system with the transfer of its start point to the point showing the market allows to narrow the forecasting to the study of the point on the curve in the new area. The main characteristics of the proposed forecasting method is a visual geometric representation of the developed strategy of enterprise management. It considerably simplifies the forecasting process. The experimental research has confirmed the efficiency of this forecasting method and revealed the superiority of active management strategies.
Simple noise-reduction method based on nonlinear forecasting
Tan, James P. L.
2017-03-01
Nonparametric detrending or noise reduction methods are often employed to separate trends from noisy time series when no satisfactory models exist to fit the data. However, conventional noise reduction methods depend on subjective choices of smoothing parameters. Here we present a simple multivariate noise reduction method based on available nonlinear forecasting techniques. These are in turn based on state-space reconstruction for which a strong theoretical justification exists for their use in nonparametric forecasting. The noise reduction method presented here is conceptually similar to Schreiber's noise reduction method using state-space reconstruction. However, we show that Schreiber's method has a minor flaw that can be overcome with forecasting. Furthermore, our method contains a simple but nontrivial extension to multivariate time series. We apply the method to multivariate time series generated from the Van der Pol oscillator, the Lorenz equations, the Hindmarsh-Rose model of neuronal spiking activity, and to two other univariate real-world data sets. It is demonstrated that noise reduction heuristics can be objectively optimized with in-sample forecasting errors that correlate well with actual noise reduction errors.
An ecosystem-based fisheries assessment and forecasting approach
Zhang, Chang Ik
2009-01-01
A comprehensive ecosystem-based approach is required to holistically assess, forecast and manage fisheries resources and their associated habitats by considering ecological interactions of target species with predators, competitors, and prey species, interactions between fishes and their habitats, and the effects of fishing on these processes. A pragmatic ecosystem-based approach was developed for the assessment of fisheries resources involving three management objectives: sustainability, bio...
Earthquake Analysis of Structure by Base Isolation Technique in SAP
T. Subramani
2014-06-01
Full Text Available This paper presents an overview of the present state of base isolation techniques with special emphasis and a brief on other techniques developed world over for mitigating earthquake forces on the structures. The dynamic analysis procedure for isolated structures is briefly explained. The provisions of FEMA 450 for base isolated structures are highlighted. The effects of base isolation on structures located on soft soils and near active faults are given in brief. Simple case study on natural base isolation using naturally available soils is presented. Also, the future areas of research are indicated. Earthquakes are one of nature IS greatest hazards; throughout historic time they have caused significant loss offline and severe damage to property, especially to man-made structures. On the other hand, earthquakes provide architects and engineers with a number of important design criteria foreign to the normal design process. From well established procedures reviewed by many researchers, seismic isolation may be used to provide an effective solution for a wide range of seismic design problems. The application of the base isolation techniques to protect structures against damage from earthquake attacks has been considered as one of the most effective approaches and has gained increasing acceptance during the last two decades. This is because base isolation limits the effects of the earthquake attack, a flexible base largely decoupling the structure from the ground motion, and the structural response accelerations are usually less than the ground acceleration. In general, the increase of additional viscous damping in the structure may reduce displacement and acceleration responses of the structure. This study also seeks to evaluate the effects of additional damping on the seismic response when compared with structures without additional damping for the different ground motions.
Weather forecast-based optimization of integrated energy systems.
Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.
2009-03-01
In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.
Wenfeng; YANG
2015-01-01
Over the years,the logistics development in Tibet has fallen behind the transport. Since the opening of Qinghai-Tibet Railway in2006,the opportunity for development of modern logistics has been brought to Tibet. The logistics demand analysis and forecasting is a prerequisite for regional logistics planning. By establishing indicator system for logistics demand of agricultural products,agricultural product logistics principal component regression model,gray forecasting model,BP neural network forecasting model are built. Because of the single model’s limitations,quadratic-linear programming model is used to build combination forecasting model to predict the logistics demand scale of agricultural products in Tibet over the next five years. The empirical analysis results show that combination forecasting model is superior to single forecasting model,and it has higher precision,so combination forecasting model will have much wider application foreground and development potential in the field of logistics.
Rogozhin, E. A.; Lutikov, A. I.; Sobisevich, L. E.; Shen, To; Kanonidi, K. Kh.
2016-07-01
The characteristics of the foci for the main shock and strongest aftershocks of the Gorkha earthquake of April 25, 2015 in Nepal are described. The macroseismic data and examples of seismic dislocations are discussed. The progression of the release of seismic energy by the aftershock process is analyzed. The data for the geophysical and seismological precursors of the main shock and the strongest aftershock of May 12, 2015 are presented. These data allowed us to formulate the short-term forecast of this event.
Physics-based forecasting of induced seismicity at Groningen gas field, the Netherlands
Dempsey, David; Suckale, Jenny
2017-08-01
Earthquakes induced by natural gas extraction from the Groningen reservoir, the Netherlands, put local communities at risk. Responsible operation of a reservoir whose gas reserves are of strategic importance to the country requires understanding of the link between extraction and earthquakes. We synthesize observations and a model for Groningen seismicity to produce forecasts for felt seismicity (M > 2.5) in the period February 2017 to 2024. Our model accounts for poroelastic earthquake triggering and rupture on the 325 largest reservoir faults, using an ensemble approach to model unknown heterogeneity and replicate earthquake statistics. We calculate probability distributions for key model parameters using a Bayesian method that incorporates the earthquake observations with a nonhomogeneous Poisson process. Our analysis indicates that the Groningen reservoir was not critically stressed prior to the start of production. Epistemic uncertainty and aleatoric uncertainty are incorporated into forecasts for three different future extraction scenarios. The largest expected earthquake was similar for all scenarios, with a 5% likelihood of exceeding M 4.0.
Forecast of Frost Days Based on Monthly Temperatures
Castellanos, M. T.; Tarquis, A. M.; Morató, M. C.; Saa-Requejo, A.
2009-04-01
Although frost can cause considerable crop damage and mitigation practices against forecasted frost exist, frost forecasting technologies have not changed for many years. The paper reports a new method to forecast the monthly number of frost days (FD) for several meteorological stations at Community of Madrid (Spain) based on successive application of two models. The first one is a stochastic model, autoregressive integrated moving average (ARIMA), that forecasts monthly minimum absolute temperature (tmin) and monthly average of minimum temperature (tminav) following Box-Jenkins methodology. The second model relates these monthly temperatures to minimum daily temperature distribution during one month. Three ARIMA models were identified for the time series analyzed with a stational period correspondent to one year. They present the same stational behavior (moving average differenced model) and different non-stational part: autoregressive model (Model 1), moving average differenced model (Model 2) and autoregressive and moving average model (Model 3). At the same time, the results point out that minimum daily temperature (tdmin), for the meteorological stations studied, followed a normal distribution each month with a very similar standard deviation through years. This standard deviation obtained for each station and each month could be used as a risk index for cold months. The application of Model 1 to predict minimum monthly temperatures showed the best FD forecast. This procedure provides a tool for crop managers and crop insurance companies to asses the risk of frost frequency and intensity, so that they can take steps to mitigate against frost damage and estimated the damage that frost would cost. This research was supported by Comunidad de Madrid Research Project 076/92. The cooperation of the Spanish National Meteorological Institute and the Spanish Ministerio de Agricultura, Pesca y Alimentation (MAPA) is gratefully acknowledged.
Development of Ensemble Model Based Water Demand Forecasting Model
Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop
2014-05-01
In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)
Ogata, Y.
2014-12-01
I am concerned with whether currently occurring earthquakes will be "foreshocks" of a significantly larger earthquake or not. When plural earthquakes occur in a region, I attempt to statistically discriminate foreshocks from a swarm or the mainshock-aftershock sequence. The forecast needs identification of an earthquake cluster using the single-link algorithm; and then the probability is calculated based on the clustering strength and magnitude correlations. The probability forecast model were estimated from the JMA hypocenter data of earthquakes of M≧4 in the period 1926-1993 (Ogata et al., 1996). Then we presented the performance and validation of the forecasts during 1994 - 2010 by using the same model (Ogata and Katsura, 2012). The forecasts perform significantly better than the unconditional (average) foreshock probability throughout Japan region. The frequency of the actual foreshocks is consistent with the forecasted probabilities. In my poster, I would like to discuss details of the outcomes in the forecasting and evaluations. Furthermore, I would like to apply the forecasting in California and global catalogs to show some universality in the forecasting procedure. Reference: [1] Ogata, Y., Utsu, T. and Katsura, K. (1996). Statistical discrimination of foreshocks from other earthquake clusters, Geophys. J. Int. 127, 17-30. [2]Ogata, Y. and Katsura, K. (2012). Prospective foreshock forecast experiment during the last 17 years, Geophys. J. Int., 191, 1237-1244.
Earthquake Analysis of Structure by Base Isolation Technique in SAP
T. Subramani; J. Jothi
2014-01-01
This paper presents an overview of the present state of base isolation techniques with special emphasis and a brief on other techniques developed world over for mitigating earthquake forces on the structures. The dynamic analysis procedure for isolated structures is briefly explained. The provisions of FEMA 450 for base isolated structures are highlighted. The effects of base isolation on structures located on soft soils and near active faults are given in brief. Simple case s...
21St Century Atmospheric Forecasting for Space Based Applications
Alliss, R.; Felton, B.; Craddock, M.; Kiley, H.; Mason, M.
2016-09-01
Many space based applications from imaging to communications are impacted by the atmosphere. Atmospheric impacts such as optical turbulence and clouds are the main drivers for these types of systems. For example, in space based optical communications, clouds will produce channel fades on the order of many hundreds of decibels (dB) thereby breaking the communication link. Optical turbulence can also produce fades but these can be compensated for by adaptive optics. The ability to forecast the current and future location and optical thickness of clouds for space to ground Electro Optical or optical communications is therefore critical in order to achieve a highly reliable system. We have developed an innovative method for producing such forecasts. These forecasts are intended to provide lead times on the order of several hours to days so that communication links can be transferred from a currently loudy ground location to another more desirable ground site. The system uses high resolution Numerical Weather Prediction (NWP) along with a variational data assimilation (DA) scheme to improve the initial conditions and forecasts. DA is used to provide an improved estimate of the atmospheric state by combining meteorological observations with NWP products and their respective error statistics. Variational DA accomplishes this through the minimization of a prescribed cost function, whereby differences between the observations and analysis are damped according to their perceived error. The NWP model is a fully three-dimensional (3D) physics-based model of the atmosphere initialized with gridded atmospheric data obtained from a global scale model. The global model input data has a horizontal resolution of approximately 25km, which is insufficient for the desired atmospheric forecasts required at near 1km resolution. Therefore, a variational DA system is used to improve the quality and resolution of the initial conditions first prescribed by the global model. Data used by the
Chaotic Load Series Forecasting Based on MPMR
Liu Zunxiong; Cheng Quanhua; Zhang Deyun
2006-01-01
Minimax probability machine regression (MPMR) was proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an ε to the true regression function. After exploring the principle of MPMR, and verifying the chaotic property of the load series from a certain power system, one-day-ahead predictions for 24 time points next day were done with MPMR. The results demonstrate that MPMP has satisfactory prediction efficiency. Kernel function shape parameter and regression tube value may influence the MPMR-based system performance. In the experiments, cross validation was used to choose the two parameters.
Mitigation of earthquake hazards using seismic base isolation systems
Wang, C.Y.
1994-06-01
This paper deals with mitigation of earthquake hazards using seismic base-isolation systems. A numerical algorithm is described for system response analysis of isolated structures with laminated elastomer bearings. The focus of this paper is on the adaptation of a nonlinear constitutive equation for the isolation bearing, and the treatment of foundation embedment for the soil-structure-interaction analysis. Sample problems are presented to illustrate the mitigating effect of using base-isolation systems.
Crop Yield Forecasted Model Based on Time Series Techniques
Li Hong-ying; Hou Yan-lin; Zhou Yong-juan; Zhao Hui-ming
2012-01-01
Traditional studies on potential yield mainly referred to attainable yield： the maximum yield which could be reached by a crop in a given environment. The new concept of crop yield under average climate conditions was defined in this paper, which was affected by advancement of science and technology. Based on the new concept of crop yield, the time series techniques relying on past yield data was employed to set up a forecasting model. The model was tested by using average grain yields of Liaoning Province in China from 1949 to 2005. The testing combined dynamic n-choosing and micro tendency rectification, and an average forecasting error was 1.24%. In the trend line of yield change, and then a yield turning point might occur, in which case the inflexion model was used to solve the problem of yield turn point.
FORECAST OF WATER TEMPERATURE IN RESERVOIR BASED ON ANALYTICAL SOLUTION
JI Shun-wen; ZHU Yue-ming; QIANG Sheng; ZENG Deng-feng
2008-01-01
The water temperature in reservoirs is difficult to be predicted by numerical simulations. In this article, a statistical model of forecasting the water temperature was proposed. In this model, the 3-D thermal conduction-diffusion equations were converted into a system consisting of 2-D equations with the Fourier expansion and some hypotheses. Then the statistical model of forecasting the water temperature was developed based on the analytical solution to the 2-D thermal equations. The simplified statistical model can elucidate the main physical mechanism of the temperature variation much more clearly than the numerical simulation with the Navier-Stokes equations. Finally, with the presented statistical model, the distribution of water temperature in the Shangyoujiang reservoir was determined.
Ensemble-based Probabilistic Forecasting at Horns Rev
Pinson, Pierre; Madsen, Henrik
2009-01-01
of probabilistic forecasts, the resolution of which may be maximized by using meteorological ensemble predictions as input. The paper concentrates on the test case of the Horns Rev wind form over a period of approximately 1 year, in order to describe, apply and discuss a complete ensemble-based probabilistic...... the benefit of yielding predictive distributions that are of increased reliability (in a probabilistic sense) in comparison with the raw ensemble forecasts, at the some time taking advantage of their high resolution. Copyright (C) 2008 John Wiley & Sons, Ltd....... are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which ore recursively estimated in order to maximize the overall skill of obtained predictive distributions. Such a methodology has...
A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook
Ji Yae Shin
2016-01-01
Full Text Available Reliable drought forecasting is necessary to develop mitigation plans to cope with severe drought. This study developed a probabilistic scheme for drought forecasting and outlook combined with quantification of the prediction uncertainties. The Bayesian network was mainly employed as a statistical scheme for probabilistic forecasting that can represent the cause-effect relationships between the variables. The structure of the Bayesian network-based drought forecasting (BNDF model was designed using the past, current, and forecasted drought condition. In this study, the drought conditions were represented by the standardized precipitation index (SPI. The accuracy of forecasted SPIs was assessed by comparing the observed SPIs and confidence intervals (CIs, exhibiting the associated uncertainty. Then, this study suggested the drought outlook framework based on probabilistic drought forecasting results. The overall results provided sufficient agreement between the observed and forecasted drought conditions in the outlook framework.
Short-term load forecasting based on a multi-model
Faller, C. [ETH, Zurich (Switzerland). Faculty of Electrical Engineering; Dvorakova, R.; Horacek, P. [Czech Technical University (Czech Republic). Faculty of Electrical Engineering
2000-07-01
Two algorithms for short-term electricity demand forecasting in the regional electricity distribution network are presented. Several approaches - feedforward neural network, adaptive modelling and fuzzy modelling - are applied to the forecast. Two different models are designed. A one hour forecasting is based on the General Regression Neural Network (GRNN) model and Principle Component Analysis. The multi-model with adaptive features and fuzzy reasoning is used for a longer-term forecast. (author)
Spatial Evaluation and Verification of Earthquake Simulators
Wilson, John Max; Yoder, Mark R.; Rundle, John B.; Turcotte, Donald L.; Schultz, Kasey W.
2016-09-01
In this paper, we address the problem of verifying earthquake simulators with observed data. Earthquake simulators are a class of computational simulations which attempt to mirror the topological complexity of fault systems on which earthquakes occur. In addition, the physics of friction and elastic interactions between fault elements are included in these simulations. Simulation parameters are adjusted so that natural earthquake sequences are matched in their scaling properties. Physically based earthquake simulators can generate many thousands of years of simulated seismicity, allowing for a robust capture of the statistical properties of large, damaging earthquakes that have long recurrence time scales. Verification of simulations against current observed earthquake seismicity is necessary, and following past simulator and forecast model verification methods, we approach the challenges in spatial forecast verification to simulators; namely, that simulator outputs are confined to the modeled faults, while observed earthquake epicenters often occur off of known faults. We present two methods for addressing this discrepancy: a simplistic approach whereby observed earthquakes are shifted to the nearest fault element and a smoothing method based on the power laws of the epidemic-type aftershock (ETAS) model, which distributes the seismicity of each simulated earthquake over the entire test region at a decaying rate with epicentral distance. To test these methods, a receiver operating characteristic plot was produced by comparing the rate maps to observed m>6.0 earthquakes in California since 1980. We found that the nearest-neighbor mapping produced poor forecasts, while the ETAS power-law method produced rate maps that agreed reasonably well with observations.
Spatial Evaluation and Verification of Earthquake Simulators
Wilson, John Max; Yoder, Mark R.; Rundle, John B.; Turcotte, Donald L.; Schultz, Kasey W.
2017-06-01
In this paper, we address the problem of verifying earthquake simulators with observed data. Earthquake simulators are a class of computational simulations which attempt to mirror the topological complexity of fault systems on which earthquakes occur. In addition, the physics of friction and elastic interactions between fault elements are included in these simulations. Simulation parameters are adjusted so that natural earthquake sequences are matched in their scaling properties. Physically based earthquake simulators can generate many thousands of years of simulated seismicity, allowing for a robust capture of the statistical properties of large, damaging earthquakes that have long recurrence time scales. Verification of simulations against current observed earthquake seismicity is necessary, and following past simulator and forecast model verification methods, we approach the challenges in spatial forecast verification to simulators; namely, that simulator outputs are confined to the modeled faults, while observed earthquake epicenters often occur off of known faults. We present two methods for addressing this discrepancy: a simplistic approach whereby observed earthquakes are shifted to the nearest fault element and a smoothing method based on the power laws of the epidemic-type aftershock (ETAS) model, which distributes the seismicity of each simulated earthquake over the entire test region at a decaying rate with epicentral distance. To test these methods, a receiver operating characteristic plot was produced by comparing the rate maps to observed m>6.0 earthquakes in California since 1980. We found that the nearest-neighbor mapping produced poor forecasts, while the ETAS power-law method produced rate maps that agreed reasonably well with observations.
Strategy-Based Forecasting Model for Civil Airlines
梁剑; 左洪福
2004-01-01
Airlines usually pay more attention to maintenance cost for efficiency improvement and consumption reduction. However, airlines, especially the domestic airlines, can hardly predict the cost exactly due to the uncertainty and complexity until now. In practice, the cost is calculated by collecting and calculating the invoices afterwards. To settle the problem, a maintenance cost forecasting model is proposed in this paper. Maintenance activities are classified into scheduled maintenance and unscheduled maintenance. Scheduled maintenance is periodic, in which the required materials and man-power hours can be obtained properly in advance. Nevertheless, it is impossible to acquire the necessary information of unscheduled maintenance. According to the specific characteristics of each, Activity-Based Costing (ABC) and Cost Estimating Relationships (CERs) are introduced to attack the building of forecasting models, respectively. Then practical cases, the 3C check of MD-90 and the engine shop visit are adopted to verify the cost forecasting models proposed. The results show that the models not only can predict the actual maintenance cost successfully, but also are helpful to drawing up the maintenance program and managing the maintenance funds efficiently.
A Novel Fuzzy Document Based Information Retrieval Model for Forecasting
Partha Roy
2017-06-01
Full Text Available Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1 In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2 A unique approach is followed while modeling the information retrieval (IR system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index of National Stock Exchange of India (NSE is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.
Earthquakes clustering based on the magnitude and the depths in Molluca Province
Wattimanela, H. J., E-mail: hwattimaela@yahoo.com [Pattimura University, Ambon (Indonesia); Institute of Technology Bandung, Bandung (Indonesia); Pasaribu, U. S.; Indratno, S. W.; Puspito, A. N. T. [Institute of Technology Bandung, Bandung (Indonesia)
2015-12-22
In this paper, we present a model to classify the earthquakes occurred in Molluca Province. We use K-Means clustering method to classify the earthquake based on the magnitude and the depth of the earthquake. The result can be used for disaster mitigation and for designing evacuation route in Molluca Province.
Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application
JIANG Ai-hua; MEI Chi; E Jia-qiang; SHI Zhang-ming
2010-01-01
In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.
Thermal infrared anomalies of several strong earthquakes.
Wei, Congxin; Zhang, Yuansheng; Guo, Xiao; Hui, Shaoxing; Qin, Manzhong; Zhang, Ying
2013-01-01
In the history of earthquake thermal infrared research, it is undeniable that before and after strong earthquakes there are significant thermal infrared anomalies which have been interpreted as preseismic precursor in earthquake prediction and forecasting. In this paper, we studied the characteristics of thermal radiation observed before and after the 8 great earthquakes with magnitude up to Ms7.0 by using the satellite infrared remote sensing information. We used new types of data and method to extract the useful anomaly information. Based on the analyses of 8 earthquakes, we got the results as follows. (1) There are significant thermal radiation anomalies before and after earthquakes for all cases. The overall performance of anomalies includes two main stages: expanding first and narrowing later. We easily extracted and identified such seismic anomalies by method of "time-frequency relative power spectrum." (2) There exist evident and different characteristic periods and magnitudes of thermal abnormal radiation for each case. (3) Thermal radiation anomalies are closely related to the geological structure. (4) Thermal radiation has obvious characteristics in abnormal duration, range, and morphology. In summary, we should be sure that earthquake thermal infrared anomalies as useful earthquake precursor can be used in earthquake prediction and forecasting.
Minor Component Analysis-based Landing Forecast System for Ship-borne Helicopter
ZHOU Bo,; SHI Ai-guo; WAN Lin; YANG Bao-zhang
2005-01-01
The general structure of ship-borne helicopter landing forecast system is presented, and a novel ship motion prediction model based on minor component analysis (MCA) is built up to improve the forecast effectiveness. To validate the feasibility of this landing forecast system, time series for the roll, pitch and heave are generated by simulation and then forecasted based on MCA. Simulation results show that ship-borne helicopters can land safely in higher sea condition while carrying on rescue or replenishment tasks at sea in terms of the landing forecast system.
Operational forecast products and applications based on WRF/Chem
Hirtl, Marcus; Flandorfer, Claudia; Langer, Matthias; Mantovani, Simone; Olefs, Marc; Schellander-Gorgas, Theresa
2015-04-01
The responsibilities of the national weather service of Austria (ZAMG) include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. The mother domain expands over Europe, North Africa and parts of Russia. The nested domain includes the alpine region and has a horizontal resolution of 4 km. Local emissions (Austria) are used in combination with European inventories (TNO and EMEP) for the simulations. The modeling system is presented and the results from the evaluation of the assimilation of pollutants using the 3D-VAR software GSI is shown. Currently observational data (PM10 and O3) from the Austrian Air-Quality network and from European stations (EEA) are assimilated into the model on an operational basis. In addition PM maps are produced using Aerosol Optical Thickness (AOT) observations from MODIS in combination with model data using machine learning techniques. The modeling system is operationally evaluated with different data sets. The emphasis of the application is on the forecast of pollutants which are compared to the hourly values (PM10, O3 and NO2) of the Austrian Air-Quality network. As the meteorological conditions are important for transport and chemical processes, some parameters like wind and precipitation are automatically evaluated (SAL diagrams, maps, …) with other models (e.g. ECMWF, AROME, …) and ground stations via web interface. The prediction of the AOT is also important for operators of solar power plants. In the past Numerical Weather Prediction (NWP) models were used to predict the AOT based on cloud forecasts at the ZAMG. These models do not consider the spatial and temporal variation of the aerosol distribution in the atmosphere with a consequent impact on the accuracy of forecasts especially during clear-sky days
LI Yong-jing
2008-01-01
Movement and deformation of underground rock include vertical dislocation and horizontal deformation, and the energy released by mine earthquake can be calculated basing on deformation energy. So put forwards the prediction for degree and spread of mine earthquake according to the underground rock's movement and deformation. The actual number of times and spread of mine earthquake on site were greatly identical to the prediction. The practice proves the possibility of prediction for mine earthquake basing on the analysis of underground rock's movement and deformation, and sets up new approach of mine earthquake prediction.
LI Yong-jing
2008-01-01
Movement and deformation of underground rock include vertical dislocation and horizontal deformation,and the energy released by mine earthquake can be calculated basing on deformation energy.So put forwards the prediction for degree and spread of mine earthquake according to the underground rock's movement and deformation.The actual number of times and spread of mine earthquake on site were greatly identical to the prediction.The practice proves the possibility of prediction for mine earthquake basing on the analysis of underground rock's movement and deformation,and sets up new approach of mine earthquake prediction.
How Informative Are Interest Rate Survey-based Forecasts?
Mateus A. Feitosa
2008-10-01
Full Text Available This paper studies the information content of survey-based predictions for the Brazilian short-term interest rate. We perform vector autoregression analysis to test for the dynamic relationship between market expectations of interest rates and spot interest rates, and a single regression forecasting approach. Empirical results suggest that surveys may be useful in assessing market expectations (contain relevant information and in building Central Bank credibility. Within an inflation targeting framework they are crucial in order to receive timely feedback on market sentiment regarding the conduct of monetary policy.
Study of Earthquake Disaster Prediction System of Langfang city Based on GIS
Huang, Meng; Zhang, Dian; Li, Pan; Zhang, YunHui; Zhang, RuoFei
2017-07-01
In this paper, according to the status of China’s need to improve the ability of earthquake disaster prevention, this paper puts forward the implementation plan of earthquake disaster prediction system of Langfang city based on GIS. Based on the GIS spatial database, coordinate transformation technology, GIS spatial analysis technology and PHP development technology, the seismic damage factor algorithm is used to predict the damage of the city under different intensity earthquake disaster conditions. The earthquake disaster prediction system of Langfang city is based on the B / S system architecture. Degree and spatial distribution and two-dimensional visualization display, comprehensive query analysis and efficient auxiliary decision-making function to determine the weak earthquake in the city and rapid warning. The system has realized the transformation of the city’s earthquake disaster reduction work from static planning to dynamic management, and improved the city’s earthquake and disaster prevention capability.
Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model
Haixiang Zang
2016-01-01
Full Text Available Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD, runs test (RT, and relevance vector machine (RVM. First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF components and residual (RES component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy.
Using adaptive network based fuzzy inference system to forecast regional electricity loads
Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)
2008-02-15
Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)
A nonlinear combination forecasting method based on the fuzzy inference system
董景荣; YANG; Jun; 等
2002-01-01
It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones,However,the literature on combining forecasts has almost exclusively focused on linear combining forecasts.In this paper,a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series.Furthermore,the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system.Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts.
Mahdyiar, M.; Galgana, G.; Shen-Tu, B.; Klein, E.; Pontbriand, C. W.
2014-12-01
Most time dependent rupture probability (TDRP) models are basically designed for a single-mode rupture, i.e. a single characteristic earthquake on a fault. However, most subduction zones rupture in complex patterns that create overlapping earthquakes of different magnitudes. Additionally, the limited historic earthquake data does not provide sufficient information to estimate reliable mean recurrence intervals for earthquakes. This makes it difficult to identify a single characteristic earthquake for TDRP analysis. Physical models based on geodetic data have been successfully used to obtain information on the state of coupling and slip deficit rates for subduction zones. Coupling information provides valuable insight into the complexity of subduction zone rupture processes. In this study we present a TDRP model that is formulated based on subduction zone slip deficit rate distribution. A subduction zone is represented by an integrated network of cells. Each cell ruptures multiple times from numerous earthquakes that have overlapping rupture areas. The rate of rupture for each cell is calculated using a moment balance concept that is calibrated based on historic earthquake data. The information in conjunction with estimates of coseismic slip from past earthquakes is used to formulate time dependent rupture probability models for cells. Earthquakes on the subduction zone and their rupture probabilities are calculated by integrating different combinations of cells. The resulting rupture probability estimates are fully consistent with the state of coupling of the subduction zone and the regional and local earthquake history as the model takes into account the impact of all large (M>7.5) earthquakes on the subduction zone. The granular rupture model as developed in this study allows estimating rupture probabilities for large earthquakes other than just a single characteristic magnitude earthquake. This provides a general framework for formulating physically-based
Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility
Tuba, Zoltán; Bottyán, Zsolt
2017-02-01
Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.
Road landslide information management and forecasting system base on GIS.
Wang, Wei Dong; Du, Xiang Gang; Xie, Cui Ming
2009-09-01
Take account of the characters of road geological hazard and its supervision, it is very important to develop the Road Landslides Information Management and Forecasting System based on Geographic Information System (GIS). The paper presents the system objective, function, component modules and key techniques in the procedure of system development. The system, based on the spatial information and attribute information of road geological hazard, was developed and applied in Guizhou, a province of China where there are numerous and typical landslides. The manager of communication, using the system, can visually inquire all road landslides information based on regional road network or on the monitoring network of individual landslide. Furthermore, the system, integrated with mathematical prediction models and the GIS's strongpoint on spatial analyzing, can assess and predict landslide developing procedure according to the field monitoring data. Thus, it can efficiently assists the road construction or management units in making decision to control the landslides and to reduce human vulnerability.
Image-Based Learning Approach Applied to Time Series Forecasting
J. C. Chimal-Eguía
2012-06-01
Full Text Available In this paper, a new learning approach based on time-series image information is presented. In order to implementthis new learning technique, a novel time-series input data representation is also defined. This input datarepresentation is based on information obtained by image axis division into boxes. The difference between this newinput data representation and the classical is that this technique is not time-dependent. This new information isimplemented in the new Image-Based Learning Approach (IBLA and by means of a probabilistic mechanism thislearning technique is applied to the interesting problem of time series forecasting. The experimental results indicatethat by using the methodology proposed in this article, it is possible to obtain better results than with the classicaltechniques such as artificial neuronal networks and support vector machines.
Assessing a 3D smoothed seismicity model of induced earthquakes
Zechar, Jeremy; Király, Eszter; Gischig, Valentin; Wiemer, Stefan
2016-04-01
As more energy exploration and extraction efforts cause earthquakes, it becomes increasingly important to control induced seismicity. Risk management schemes must be improved and should ultimately be based on near-real-time forecasting systems. With this goal in mind, we propose a test bench to evaluate models of induced seismicity based on metrics developed by the CSEP community. To illustrate the test bench, we consider a model based on the so-called seismogenic index and a rate decay; to produce three-dimensional forecasts, we smooth past earthquakes in space and time. We explore four variants of this model using the Basel 2006 and Soultz-sous-Forêts 2004 datasets to make short-term forecasts, test their consistency, and rank the model variants. Our results suggest that such a smoothed seismicity model is useful for forecasting induced seismicity within three days, and giving more weight to recent events improves forecast performance. Moreover, the location of the largest induced earthquake is forecast well by this model. Despite the good spatial performance, the model does not estimate the seismicity rate well: it frequently overestimates during stimulation and during the early post-stimulation period, and it systematically underestimates around shut-in. In this presentation, we also describe a robust estimate of information gain, a modification that can also benefit forecast experiments involving tectonic earthquakes.
Weide Li
2017-01-01
Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.
The practical method of improve earthquake forecast accuracy by MSDP software%MSDP软件提高地震速报质量
苏莉华; 赵晖; 李源; 魏玉霞
2012-01-01
Select the records of Henan digital seismic network within the network and outside the network (the sidelines within 100 km) of seismic events from 2008 to 2011. Analysis and comparison those records by MSDP software, and coordinate with the daily experience, generalize the practical method of improve earthquake forecast accuracy.%选取2008-2011年河南数字地震台网记录的网内和网外(边线外100 km以内)的地震事件,运用MSDP软件对这些震例进行实际分析对比,再结合日常的工作经验,从而归纳出提高地震速报质量的实用方法.
Enhancing flood forecasting with the help of processed based calibration
Cullmann, Johannes; Krauße, Thomas; Philipp, Andy
Due to the fact that the required input data are not always completely available and model structures are only a crude description of the underlying natural processes, model parameters need to be calibrated. Calibrated model parameters only reflect a small domain of the natural processes well. This imposes an obstacle on the accuracy of modelling a wide range of flood events, which, in turn is crucial for flood forecasting systems. Together with the rigid model structures of currently available rainfall-runoff models this presents a serious constraint to portraying the highly non-linear transformation of precipitation into runoff. Different model concepts (interflow, direct runoff), or rather the represented processes, such as infiltration, soil water movement etc. are more or less dominating different sections of the runoff spectrum. Most models do not account for such transient characteristics inherent to the hydrograph. In this paper we try to show a way out of the dilemma of limited model parameter validity. Exemplarily, we investigate on the model performance of WaSiM-ETH, focusing on the parameterisation strategy in the context of flood forecasting. In order to compensate for the non-transient parameters of the WaSiM model we propose a process based parameterisation strategy. This starts from a detailed analysis of the considered catchments rainfall-runoff characteristics. Based on a classification of events, WaSiM-ETH is calibrated and validated to describe all the event classes separately. These specific WaSiM-ETH event class models are then merged to improve the model performance in predicting peak flows. This improved catchment modelling can be used to train an artificial intelligence based black box forecasting tool as described in [Schmitz, G.H., Cullmann, J., Görner, W., Lennartz, F., Dröge, W., 2005. PAI-OFF: Eine neue Strategie zur Hochwasservorhersage in schnellreagierenden Einzugsgebieten. Hydrologie und Wasserbewirtschaftung 49, 226
Market-based demand forecasting promotes informed strategic financial planning.
Beech, A J
2001-11-01
Market-based demand forecasting is a method of estimating future demand for a healthcare organization's services by using a broad range of data that describe the nature of demand within the organization's service area. Such data include the primary and secondary service areas, the service-area populations by various demographic groupings, discharge utilization rates, market size, and market share by service line and organizationwide. Based on observable market dynamics, strategic planners can make a variety of explicit assumptions about future trends regarding these data to develop scenarios describing potential future demand. Financial planners then can evaluate each scenario to determine its potential effect on selected financial and operational measures, such as operating margin, days cash on hand, and debt-service coverage, and develop a strategic financial plan that covers a range of contingencies.
Efficient Resources Provisioning Based on Load Forecasting in Cloud
Rongdong Hu
2014-01-01
Full Text Available Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application’s actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements.
Efficient resources provisioning based on load forecasting in cloud.
Hu, Rongdong; Jiang, Jingfei; Liu, Guangming; Wang, Lixin
2014-01-01
Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements.
Sand-Dust Storm Ensemble Forecast Model Based on Rough Set
LU Zhiying; YANG Le; LI Yanying; ZHAO Zhichao
2007-01-01
To improve the accuracy of sand-dust storm forecast system, a sand-dust storm ensemble forecast model based on rough set (RS) is proposed. The feature data are extracted from the historical data sets using the self-organization map (SOM) clustering network and single fields forecast to form the feature values with low dimensions. Then, the unwanted attributes are reduced according to RS to discretize the continuous feature values. Lastly, the minimum decision rules are constructed according to the remainder attributes, namely sand-dust storm ensemble forecast model based on RS is constructed. Results comparison between the proposed model and the back propagation neural network model show that the sand-storm forecast model based on RS has better stability, faster running speed, and its forecasting accuracy ratio is increased from 17.1% to 86.21%.
Rough Precipitation Forecasts based on Analogue Method: an Operational System
Raffa, Mario; Mercogliano, Paola; Lacressonnière, Gwendoline; Guillaume, Bruno; Deandreis, Céline; Castanier, Pierre
2017-04-01
In the framework of the Climate KIC partnership, has been funded the project Wat-Ener-Cast (WEC), coordinated by ARIA Technologies, having the goal to adapt, through tailored weather-related forecast, the water and energy operations to the increased weather fluctuation and to climate change. The WEC products allow providing high quality forecast suited in risk and opportunities assessment dashboard for water and energy operational decisions and addressing the needs of sewage/water distribution operators, energy transport & distribution system operators, energy manager and wind energy producers. A common "energy water" web platform, able to interface with newest smart water-energy IT network have been developed. The main benefit by sharing resources through the "WEC platform" is the possibility to optimize the cost and the procedures of safety and maintenance team, in case of alerts and, finally to reduce overflows. Among the different services implemented on the WEC platform, ARIA have developed a product having the goal to support sewage/water distribution operators, based on a gradual forecast information system ( at 48hrs/24hrs/12hrs horizons) of heavy precipitation. For each fixed deadline different type of operation are implemented: 1) 48hour horizon, organisation of "on call team", 2) 24 hour horizon, update and confirm the "on call team", 3) 12 hour horizon, secure human resources and equipment (emptying storage basins, pipes manipulations …). More specifically CMCC have provided a statistical downscaling method in order to provide a "rough" daily local precipitation at 24 hours, especially when high precipitation values are expected. This statistical technique consists of an adaptation of analogue method based on ECMWF data (analysis and forecast at 24 hours). One of the most advantages of this technique concerns a lower computational burden and budget compared to running a Numerical Weather Prediction (NWP) model, also if, of course it provides only this
Overview of seismic base isolation systems, applications, and performance during earthquakes
Trummer, D.J.; Sommer, S.C.
1993-08-01
Seismic base isolation is a technique for mitigating the effects of earthquakes on structures. A structure is decoupled from the potentially damaging forces of an earthquake by placing a flexible isolation system between the structure and the ground. A data base has been created for the US Department of Energy which contains information about systems used to achieve seismic isolation, their applications in structures, and how they have performed during earthquakes. This paper will present and overview of the database.
Stuparu, Dana; Bachmann, Daniel; Bogaard, Tom; Twigt, Daniel; Verkade, Jan; de Bruijn, Karin; de Leeuw, Annemargreet
2017-04-01
Flood forecasts, warning and emergency response are important components in flood risk management. Most flood forecasting systems use models to translate weather predictions to forecasted discharges or water levels. However, this information is often not sufficient for real time decisions. A sound understanding of the reliability of embankments and flood dynamics is needed to react timely and reduce the negative effects of the flood. Where are the weak points in the dike system? When, how much and where the water will flow? When and where is the greatest impact expected? Model-based flood impact forecasting tries to answer these questions by adding new dimensions to the existing forecasting systems by providing forecasted information about: (a) the dike strength during the event (reliability), (b) the flood extent in case of an overflow or a dike failure (flood spread) and (c) the assets at risk (impacts). This work presents three study-cases in which such a set-up is applied. Special features are highlighted. Forecasting of dike strength. The first study-case focusses on the forecast of dike strength in the Netherlands for the river Rhine branches Waal, Nederrijn and IJssel. A so-called reliability transformation is used to translate the predicted water levels at selected dike sections into failure probabilities during a flood event. The reliability of a dike section is defined by fragility curves - a summary of the dike strength conditional to the water level. The reliability information enhances the emergency management and inspections of embankments. Ensemble forecasting. The second study-case shows the setup of a flood impact forecasting system in Dumfries, Scotland. The existing forecasting system is extended with a 2D flood spreading model in combination with the Delft-FIAT impact model. Ensemble forecasts are used to make use of the uncertainty in the precipitation forecasts, which is useful to quantify the certainty of a forecasted flood event. From global
AR-based Algorithms for Short Term Load Forecast
Zuhairi Baharudin
2014-02-01
Full Text Available Short-term load forecast plays an important role in planning and operation of power systems. The accuracy of the forecast value is necessary for economically efficient operation and effective control of the plant. This study describes the methods of Autoregressive (AR Burg’s and Modified Covariance (MCOV in solving the short term load forecast. Both algorithms are tested with power load data from Malaysian grid and New South Wales, Australia. The forecast accuracy is assessed in terms of their errors. For the comparison the algorithms are tested and benchmark with the previous successful proposed methods.
Asperity-based earthquake likelihood models for Italy
Danijel Schorlemmer
2010-11-01
Full Text Available The Asperity Likelihood Model (ALM hypothesizes that small-scale spatial variations in the b-value of the Gutenberg-Richter relationship have a central role in forecasting future seismicity. The physical basis of the ALM is the concept that the local b-value is inversely dependent on the applied shear stress. Thus low b-values (b <0.7 characterize locked patches of faults, or asperities, from which future mainshocks are more likely to be generated, whereas high b-values (b >1.1, which can be found, for example, in creeping sections of faults, suggest a lower probability of large events. To turn this hypothesis into a forecast model for Italy, we first determined the regional b-value (b = 0.93 ±0.01 and compared it with the locally determined b-values at each node of the forecast grid, based on sampling radii ranging from 6 km to 20 km. We used the local b-values if their Akaike Information Criterion scores were lower than those of the regional b-values. We then explored two modifications to this model: in the ALM.IT, we declustered the input catalog for M ≥2 and smoothed the node-wise rates of the declustered catalog with a Gaussian filter. Completeness values for each node were determined using the probability-based magnitude of completeness method. In the second model, the hybrid ALM (HALM, as a «hybrid» between a grid-based and a zoning model, the Italian territory was divided into eight distinct regions that depended on the main tectonic regimes, and the local b-value variability was thus mapped using the regional b-values for each tectonic zone.
Operational foreshock forecasting: Fifteen years after
Ogata, Y.
2010-12-01
We are concerned with operational forecasting of the probability that events are foreshocks of a forthcoming earthquake that is significantly larger (mainshock). Specifically, we define foreshocks as the preshocks substantially smaller than the mainshock by a magnitude gap of 0.5 or larger. The probability gain of foreshock forecast is extremely high compare to long-term forecast by renewal processes or various alarm-based intermediate-term forecasts because of a large event’s low occurrence rate in a short period and a narrow target region. Thus, it is desired to establish operational foreshock probability forecasting as seismologists have done for aftershocks. When a series of earthquakes occurs in a region, we attempt to discriminate foreshocks from a swarm or mainshock-aftershock sequence. Namely, after real time identification of an earthquake cluster using methods such as the single-link algorithm, the probability is calculated by applying statistical features that discriminate foreshocks from other types of clusters, by considering the events' stronger proximity in time and space and tendency towards chronologically increasing magnitudes. These features were modeled for probability forecasting and the coefficients of the model were estimated in Ogata et al. (1996) for the JMA hypocenter data (M≧4, 1926-1993). Currently, fifteen years has passed since the publication of the above-stated work so that we are able to present the performance and validation of the forecasts (1994-2009) by using the same model. Taking isolated events into consideration, the probability of the first events in a potential cluster being a foreshock vary in a range between 0+% and 10+% depending on their locations. This conditional forecasting performs significantly better than the unconditional (average) foreshock probability of 3.7% throughout Japan region. Furthermore, when we have the additional events in a cluster, the forecast probabilities range more widely from nearly 0% to
Forecasting of the Egg Price Based on EEMD
Dan; WANG; Yucheng; HE
2015-01-01
In the transitional period of " new normal",the target price is put forward to deepen the reform system of agricultural product price. Egg is the main agricultural product and its price has fluctuated violently in recent years. Setting up a target price for egg will reduce the price fluctuations. This article brings up a three-step agricultural price forecasting model based on EEMD and applies it to the analysis of egg price. It shows that the upward trend can be divided into three stages,and the fluctuation is greater than that of food consumer price in the foreseeable future. The volatility of egg price is bad for the development of the fresh market and stable life of the residents. This article finally puts forward some recommendations.
Hydrologic Severity-based Forecast System for Road Infrastructure Monitoring
Hernandez, F.; Li, L.; Lochan, S.; Liang, X.; Liang, Y.; Teng, W. L.
2013-12-01
The state departments of transportation in the U.S. are responsible for responding to weather- and hydrology-related emergencies affecting the transportation infrastructure, such as heavy rain, flooding, scouring of bridge structures, icing, and fog. These emergency response actions often require significant amount of effort to identify, inspect, and manage, e.g., potentially compromised bridges due to scouring. An online Hydrologic Disaster Forecasting and Response (HDFR) system is being developed for the Pennsylvania Department of Transportation (PennDOT), to provide more accurate estimates on current road infrastructure conditions. The HDFR system can automatically access satellite data from NASA data centers, NOAA radar rainfall measurements, and meteorological and hydrometeorological station observations. The accessed data can be fused, using an extended multi-scale Kalman smoother-based (MKS-based) algorithm to provide enhanced data products. The fused information is then contrasted with historical data, to assess the severity of the weather and hydrological conditions and to provide more accurate estimates of those areas with a high likelihood of being affected by similar emergencies. The real- and near-real-time data, as well as weather forecasts, are input to a multi-scale hydrological simulator. The HDFR system will be able to generate stream flow predictions at road-level scales, allowing for the monitoring of a complex and distributed infrastructure, with less computational resources than those previously required. Preliminary results will be presented that show the advantages of the HDFR system over PennDOT's current methods for identifying bridges in need of inspection.
Weather Forecast Based Conditional Pest Management: A Stochastic Optimal Control Investigation
Lu, Liang; Elbakidze, Levan
2011-01-01
In this paper, we examine conditional, forecast-based dynamic pest management in agricultural crop production given stochastic pest infestations and stochastic climate dynamics throughout the growing season. Using stochastic optimal control we show that correlation between forecast error for climate prediction and forecast error for pest outbreaks can be used to improve pesticide application efficiency. In the general setting, we apply modified Hamiltonian approach to discuss the steady state...
Physics-based estimates of maximum magnitude of induced earthquakes
Ampuero, Jean-Paul; Galis, Martin; Mai, P. Martin
2016-04-01
In this study, we present new findings when integrating earthquake physics and rupture dynamics into estimates of maximum magnitude of induced seismicity (Mmax). Existing empirical relations for Mmax lack a physics-based relation between earthquake size and the characteristics of the triggering stress perturbation. To fill this gap, we extend our recent work on the nucleation and arrest of dynamic ruptures derived from fracture mechanics theory. There, we derived theoretical relations between the area and overstress of overstressed asperity and the ability of ruptures to either stop spontaneously (sub-critical ruptures) or runaway (super-critical ruptures). These relations were verified by comparison with simulation and laboratory results, namely 3D dynamic rupture simulations on faults governed by slip-weakening friction, and laboratory experiments of frictional sliding nucleated by localized stresses. Here, we apply and extend these results to situations that are representative for the induced seismicity environment. We present physics-based predictions of Mmax on a fault intersecting cylindrical reservoir. We investigate Mmax dependence on pore-pressure variations (by varying reservoir parameters), frictional parameters and stress conditions of the fault. We also derive Mmax as a function of injected volume. Our approach provides results that are consistent with observations but suggests different scaling with injected volume than that of empirical relation by McGarr, 2014.
Design and realization of RS application system for earthquake emergency based on digital earth
Yuan, Xiaoxiang; Wang, Xiaoqing; Guo, Jianxing; Dou, Aixia; Ding, Xiang
2016-11-01
The current RS-based earthquake emergency system is mainly based on stand-alone software which cannot meet the requirements of massive remote sensing data and parallel seismic damage information extraction after a devastating earthquake. Taking Shaanxi Province as an example, this paper explored firstly the network-based working mode of seismic damage information extraction and data management strategy for multi-user cooperative operation based on analysing work flow of the RS application to earthquake emergency. Then, using WorldWind java SDK, the RS application system for earthquake emergency based on digital earth platform was brought out in CS architecture. Finally, spatial data tables of classification and grade of seismic damage were designed and the system was developed. This system realized functions including 3D display, management of seismic RS image and GIS data obtained before and after earthquake for different user levels and cooperative extraction and publish of such seismic information as building damage, traffic damage and seismo-geological disasters caused by earthquake in real time. Some application to earthquake cases such as 2014 M s6.5 Ludian earthquake show that this system can improve the efficiency of seismic damage information interpretation and data sharing, and provide import disaster information for decision making of earthquake emergency rescue and disaster relief.
Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint
Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-31
Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.
A stochastic HMM-based forecasting model for fuzzy time series.
Li, Sheng-Tun; Cheng, Yi-Chung
2010-10-01
Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.
Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems
Löwe, Roland; Vezzaro, Luca; Mikkelsen, Peter Steen
2016-01-01
This article demonstrates the incorporation of stochastic grey-box models for urban runoff forecasting into a full-scale, system-wide control setup where setpoints are dynamically optimized considering forecast uncertainty and sensitivity of overflow locations in order to reduce combined sewer...... overflow risk. The stochastic control framework and the performance of the runoff forecasting models are tested in a case study in Copenhagen (76 km2 with 6 sub-catchments and 7 control points) using 2-h radar rainfall forecasts and inlet flows to control points computed from a variety of noisy...... smoothing. Simulations demonstrate notable improvements of the control efficiency when considering forecast information and additionally when considering forecast uncertainty, compared with optimization based on current basin fillings only....
Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models
Zhang Chi
2016-01-01
Full Text Available Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.
Grey forecasting model for active vibration control systems
Lihua, Zou; Suliang, Dai; Butterworth, John; Ma, Xing; Dong, Bo; Liu, Aiping
2009-05-01
Based on the grey theory, a GM(1,1) forecasting model and an optimal GM(1,1) forecasting model are developed and assessed for use in active vibration control systems for earthquake response mitigation. After deriving equations for forecasting the control state vector, design procedures for an optimal active control method are proposed. Features of the resulting vibration control and the influence on it of time-delay based on different sampling intervals of seismic ground motion are analysed. The numerical results show that the forecasting models based on the grey theory are reliable and practical in structural vibration control fields. Compared with the grey forecasting model, the optimal forecasting model is more efficient in reducing the influences of time-delay and disturbance errors.
Disastrous Earthquake Cases in China and Disaster Information System Based on GIS
Ding Xiang; Wang Xiaoqing
2004-01-01
The China's Earthquake Cases and Disaster Information System based on GIS (MapECDIS 2002 for Windows) is a GIS system developed to provide a tool for the government and the public to inquire and learn about disaster information (since 2221BC) and case study results (since 1966) of destructive earthquakes in China. The system is expected to be helpful, as an applied supplementary tool, for scientists and management personnel in earthquake prediction practice, seismological research and earthquake disaster research. The design idea and main functions of the system are introduced in the paper.
Tohoku earthquake: a surprise?
Kagan, Yan Y
2011-01-01
We consider three issues related to the 2011 Tohoku mega-earthquake: (1) how to evaluate the earthquake maximum size in subduction zones, (2) what is the repeat time for the largest earthquakes in Tohoku area, and (3) what are the possibilities of short-term forecasts during the 2011 sequence. There are two quantitative methods which can be applied to estimate the maximum earthquake size: a statistical analysis of the available earthquake record and the moment conservation principle. The latter technique studies how much of the tectonic deformation rate is released by earthquakes. For the subduction zones, the seismic or historical record is not sufficient to provide a reliable statistical measure of the maximum earthquake. The moment conservation principle yields consistent estimates of maximum earthquake size: for all the subduction zones the magnitude is of the order 9.0--9.7, and for major subduction zones the maximum earthquake size is statistically indistinguishable. Starting in 1999 we have carried out...
Liechti, K.; L. Panziera; U. Germann; Zappa, M.
2013-01-01
This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowca...
Seismicity prior to the 2016 Kumamoto earthquakes
Nanjo, K Z; Orihara, Y; Furuse, N; Togo, S; Nitta, H; Okada, T; Tanaka, R; Kamogawa, M; Nagao, T
2016-01-01
The 2016 Kumamoto earthquakes occurred under circumstance that seismicity remains high in all parts of Japan since the 2011 Tohoku-Oki earthquake. Identifying what happened before this incident is one starting point for promote earthquake forecast research to prepare for subsequent large earthquakes in the near future in Japan. Here we report precursory seismic patterns prior to the Kumamoto earthquakes, measured by four different methods based on seismicity changes that can be used for earthquake forecasting: b-value method, two kinds of seismic quiescence evaluation methods, and a method of detailed foreshock evaluation. The spatial extent of precursory patterns differs from one method to the other and ranges from local scales (typically asperity size), to regional scales (e.g., 2{\\deg} x 3{\\deg} around the source zone). The earthquakes are preceded by periods of pronounced anomalies, which lasted decade scales (e.g., 20 years or longer) to yearly scales (e.g., 1~2 years). We demonstrate that combination of...
Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method
Wen-Yeau Chang
2013-09-01
Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.
Pagowski, M O; Grell, G A; Devenyi, D; Peckham, S E; McKeen, S A; Gong, W; Monache, L D; McHenry, J N; McQueen, J; Lee, P
2006-02-02
Forecasts from seven air quality models and surface ozone data collected over the eastern USA and southern Canada during July and August 2004 provide a unique opportunity to assess benefits of ensemble-based ozone forecasting and devise methods to improve ozone forecasts. In this investigation, past forecasts from the ensemble of models and hourly surface ozone measurements at over 350 sites are used to issue deterministic 24-h forecasts using a method based on dynamic linear regression. Forecasts of hourly ozone concentrations as well as maximum daily 8-h and 1-h averaged concentrations are considered. It is shown that the forecasts issued with the application of this method have reduced bias and root mean square error and better overall performance scores than any of the ensemble members and the ensemble average. Performance of the method is similar to another method based on linear regression described previously by Pagowski et al., but unlike the latter, the current method does not require measurements from multiple monitors since it operates on individual time series. Improvement in the forecasts can be easily implemented and requires minimal computational cost.
Flare forecasting based on sunspot-groups characteristics
Contarino, Lidia; Zuccarello, Francesca; Romano, Paolo; Spadaro, Daniele; Guglielmino, Salvatore L; Battiato, Viviana
2009-01-01
... accurate flare forecasting. In order to give a contribution to this aspect, we focused our attention on the characteristics that must be fulfilled by sunspot-groups in order to be flare-productive...
Factor-based forecasting in the presence of outliers
Kristensen, Johannes Tang
2014-01-01
Macroeconomic forecasting using factor models estimated by principal components has become a popular research topic with many both theoretical and applied contributions in the literature. In this paper we attempt to address an often neglected issue in these models: The problem of outliers...... in the data. Most papers take an ad-hoc approach to this problem and simply screen datasets prior to estimation and remove anomalous observations. We investigate whether forecasting performance can be improved by using the original unscreened dataset and replacing principal components with a robust...... apply the estimator in a simulated real-time forecasting exercise to test its merits. We use a newly compiled dataset of US macroeconomic series spanning the period 1971:2–2012:10. Our findings suggest that the chosen treatment of outliers does affect forecasting performance and that in many cases...
RMB Exchange Rate Forecast Approach Based on BP Neural Network
Ye, Sun
RMB exchange rate system has reformed since July, 2005. This article chose RMB exchange rate data during a period from July, 2005 to September 2010 to establish BP neural network model to forecast RMB exchange rate in the future by using MATLAB software. The result showed that BP neural network is effective to forecast RMB exchange rate and also indicated that RMB exchange rate will continue to appreciate in the future.
Chilean Nominal Exchange Rate: Forecasting Based Upon Technical Analysis
Ana María Abarca; Felipe Alarcón; Pablo Pincheira; Jorge Selaive
2007-01-01
: This work presents a review of the main indicators used in the technical analysis of the peso-dollar parity. We explain the interpretations carried out by technical analysts of these indicators and perform forecasting analysis of the Relative Strength Index (RSI) to predict exchange rate returns at daily frequency. The predictive exercises are done using both in-sample and out-of-sample techniques, and report a robust forecasting ability for horizons within 7 weeks.
Research on Forecast Technology of Mine Gas Emission Based on Fuzzy Data Mining(FDM)
XU Chang-kai; WANG Yao-cai; WANG Jun-wei
2004-01-01
The safe production of coalmine can be further improved by forecasting the quantity of gas emission based on the real-time data and historical data which the gas monitoring system has saved. By making use of the advantages of data warehouse and data mining technology for processing large quantity of redundancy data, the method and its application of forecasting mine gas emission quantity based on FDM were studied. The constructing fuzzy resembling relation and clustering analysis were proposed, which the potential relationship inside the gas emission data may be found. The mode finds model and forecast model were presented, and the detailed approach to realize this forecast was also proposed, which have been applied to forecast the gas emission quantity efficiently.
Zelentsov, Viacheslav; Potryasaev, Semen; Sokolov, Boris
2016-08-01
In this paper a new approach to the creation of short- term forecasting systems of river flooding is being further developed. It provides highly accurate forecasting results due to operative obtaining and integrated processing of the remote sensing and ground- based water flow data in real time. Forecasting of flood areas and depths is performed on a time interval of 12 to 48 hours to be able to perform the necessary steps to alert and evacuate the population. Forecast results are available as web services. The proposed system extends traditional separate methods based on satellite monitoring or modeling of a river's physical processes, by using an interdisciplinary approach, integration of different models and technologies, and through intelligent choice of the most suitable models for a flood forecasting.
Using ensemble weather forecast in a risk based real time optimization of urban drainage systems
Courdent, Vianney Augustin Thomas; Vezzaro, Luca; Mikkelsen, Peter Steen
2015-01-01
on DORA's approach, this study investigated the implementation of long forecast horizon using an ensemble forecast from a Numerical Weather Prediction (NWP) model. The uncertainty of the prediction is characterized by an ensemble of 25 forecast scenarios. According to the status of the UDS......) strategy was developed to operate Urban Drainage Systems (UDS) in order to minimize the expected overflow risk by considering the water volume presently stored in the drainage network, the expected runoff volume based on a 2-hours radar forecast model and an estimated uncertainty of the runoff forecast....... However, such temporal horizon (1-2 hours) is relatively short when used for the operation of large storage facilities, which may require a few days to be emptied. This limits the performance of the optimization and control in reducing combined sewer overflow and in preparing for possible flooding. Based...
Li, Linlin; Switzer, Adam D.; Wang, Yu; Chan, Chung-Han; Qiu, Qiang; Weiss, Robert
2017-04-01
Current tsunami inundation maps are commonly generated using deterministic scenarios, either for real-time forecasting or based on hypothetical "worst-case" events. Such maps are mainly used for emergency response and evacuation planning and do not include the information of return period. However, in practice, probabilistic tsunami inundation maps are required in a wide variety of applications, such as land-use planning, engineer design and for insurance purposes. In this study, we present a method to develop the probabilistic tsunami inundation map using a stochastic earthquake source model. To demonstrate the methodology, we take Macau a coastal city in the South China Sea as an example. Two major advances of this method are: it incorporates the most updated information of seismic tsunamigenic sources along the Manila megathrust; it integrates a stochastic source model into a Monte Carlo-type simulation in which a broad range of slip distribution patterns are generated for large numbers of synthetic earthquake events. When aggregated the large amount of inundation simulation results, we analyze the uncertainties associated with variability of earthquake rupture location and slip distribution. We also explore how tsunami hazard evolves in Macau in the context of sea level rise. Our results suggest Macau faces moderate tsunami risk due to its low-lying elevation, extensive land reclamation, high coastal population and major infrastructure density. Macau consists of four districts: Macau Peninsula, Taipa Island, Coloane island and Cotai strip. Of these Macau Peninsula is the most vulnerable to tsunami due to its low-elevation and exposure to direct waves and refracted waves from the offshore region and reflected waves from mainland. Earthquakes with magnitude larger than Mw8.0 in the northern Manila trench would likely cause hazardous inundation in Macau. Using a stochastic source model, we are able to derive a spread of potential tsunami impacts for earthquakes
Study on Ice Regime Forecast Based on SVR Optimized by Particle Swarm Optimization Algorithm
WANG; Fu-qiang; RONG; Fei
2012-01-01
[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.
Earthquake scenarios based on lessons from the past
Solakov, Dimcho; Simeonova, Stella; Aleksandrova, Irena; Popova, Iliana
2010-05-01
Earthquakes are the most deadly of the natural disasters affecting the human environment; indeed catastrophic earthquakes have marked the whole human history. Global seismic hazard and vulnerability to earthquakes are increasing steadily as urbanization and development occupy more areas that are prone to effects of strong earthquakes. Additionally, the uncontrolled growth of mega cities in highly seismic areas around the world is often associated with the construction of seismically unsafe buildings and infrastructures, and undertaken with an insufficient knowledge of the regional seismicity peculiarities and seismic hazard. The assessment of seismic hazard and generation of earthquake scenarios is the first link in the prevention chain and the first step in the evaluation of the seismic risk. The implementation of the earthquake scenarios into the policies for seismic risk reduction will allow focusing on the prevention of earthquake effects rather than on intervention following the disasters. The territory of Bulgaria (situated in the eastern part of the Balkan Peninsula) represents a typical example of high seismic risk area. Over the centuries, Bulgaria has experienced strong earthquakes. At the beginning of the 20-the century (from 1901 to 1928) five earthquakes with magnitude larger than or equal to MS=7.0 occurred in Bulgaria. However, no such large earthquakes occurred in Bulgaria since 1928, which may induce non-professionals to underestimate the earthquake risk. The 1986 earthquake of magnitude MS=5.7 occurred in the central northern Bulgaria (near the town of Strazhitsa) is the strongest quake after 1928. Moreover, the seismicity of the neighboring countries, like Greece, Turkey, former Yugoslavia and Romania (especially Vrancea-Romania intermediate earthquakes), influences the seismic hazard in Bulgaria. In the present study deterministic scenarios (expressed in seismic intensity) for two Bulgarian cities (Rouse and Plovdiv) are presented. The work on
Forecasting Peak Load Electricity Demand Using Statistics and Rule Based Approach
Z. Ismail
2009-01-01
Full Text Available Problem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as the consequences of under or over forecasting the demand are serious and will affect all stakeholders in the electricity supply industry. Approach: If under estimated, the result is serious since plant installation cannot easily be advanced, this will affect the economy, business, loss of time and image. If over estimated, the financial penalty for excess capacity (i.e., over-estimated and wasting of resources. Therefore this study aimed to develop new forecasting model for forecasting electricity load demand which will minimize the error of forecasting. In this study, we explored the development of rule-based method for forecasting electricity peak load demand. The rule-based system synergized human reasoning style of fuzzy systems through the use of set of rules consisting of IF-THEN approximators with the learning and connectionist structure. Prior to the implementation of rule-based models, SARIMAT model and Regression time series were used. Results: Modification of the basic regression model and modeled it using Box-Jenkins auto regressive error had produced a satisfactory and adequate model with 2.41% forecasting error. With rule-based based forecasting, one can apply forecaster expertise and domain knowledge that is appropriate to the conditions of time series. Conclusion: This study showed a significant improvement in forecast accuracy when compared with the traditional time series model. Good domain knowledge of the experts had contributed to the increase in forecast accuracy. In general, the improvement will depend on the conditions of the data
Lihua Yang
2015-04-01
Full Text Available In order to improve the accuracy of grain production forecasting, this study proposed a new combination forecasting model, the model combined stepwise regression method with RBF neural network by assigning proper weights using inverse variance method. By comparing different criteria, the result indicates that the combination forecasting model is superior to other models. The performance of the models is measured using three types of error measurement, which are Mean Absolute Percentage Error (MAPE, Theil Inequality Coefficient (Theil IC and Root Mean Squared Error (RMSE. The model with smallest value of MAPE, Theil IC and RMSE stands out to be the best model in predicting the grain production. Based on the MAPE, Theil IC and RMSE evaluation criteria, the combination model can reduce the forecasting error and has high prediction accuracy in grain production forecasting, making the decision more scientific and rational.
Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks
Antonio J. Sanchez-Esguevillas
2013-03-01
Full Text Available Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc., which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN that performs Short-Term Load Forecasting (STLF. In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.
Forecast of Flood in Chaohu Lake Basin of China Based on Grey-Markov Theory
LI Xiang; WANG Xinyuan; SHAO Wei; XIA Linyi; ZHANG Guangsheng; TIAN Bing; LI Wenda; PENG Peng
2007-01-01
Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term forecast of natural precipitation at present. In the present paper the disadvantages of grey GM (1, 1) and Markov chain are analyzed, and Grey-Markov forecast theory about flood is put forward and then the modifying model is developed by making prediction of Chaohu Lake basin. Hydrological law was conducted based on the theoretical forecasts by grey system GM (1, 1) forecast model with improved Markov chain. The above method contained Stat-analysis, embodying scientific approach, precise forecast and its reliable results.
无
2007-01-01
Based on the 500-hPa geopotential height field series of T106 numerical forecast products, by empirical orthogonal function (EOF) time-space separation, and on the hypotheses of EOF space-models being stable, the EOF time coefficient series were taken as dynamical statistic model variables. The dynamic system reconstruction idea and genetic algorithm were introduced to make the dynamical model parameters optimized, and a nonlinear dynamic statistic model of EOF separating time coefficient series was established. By the model time integral and EOF time-space reconstruction, a medium/long-range forecast of subtropical high was carried out. The results show that the dynamical model forecast and T106 numerical forecast were approximately similar in the short-range forecast (≤5 days), but in the medium/long-range forecast (≥5 days), the forecast results of dynamical model was superior to that of T106 numerical products. A new method and idea were presented for diagnosing and forecasting complicated weathers such as subtropical high, and showed a better application outlook.
ARMA based approaches for forecasting the tuple of wind speed and direction
Erdem, Ergin; Shi, Jing [Department of Industrial and Manufacturing Engineering, North Dakota State University, Dept. 2485, PO Box 6050, Fargo, ND 58108 (United States)
2011-04-15
Short-term forecasting of wind speed and direction is of great importance to wind turbine operation and efficient energy harvesting. In this study, the forecasting of wind speed and direction tuple is performed. Four approaches based on autoregressive moving average (ARMA) method are employed for this purpose. The first approach features the decomposition of the wind speed into lateral and longitudinal components. Each component is represented by an ARMA model, and the results are combined to obtain the wind direction and speed forecasts. The second approach employs two independent ARMA models - a traditional ARMA model for predicting wind speed and a linked ARMA model for wind direction. The third approach features vector autoregression (VAR) models to forecast the tuple of wind attributes. The fourth approach involves employing a restricted version of the VAR approach to predict the same. By employing these four approaches, the hourly mean wind attributes are forecasted 1-h ahead for two wind observation sites in North Dakota, USA. The results are compared using the mean absolute error (MAE) as a measure for forecasting quality. It is found that the component model is better at predicting the wind direction than the traditional-linked ARMA model, whereas the opposite is observed for wind speed forecasting. Utilizing VAR approaches rather than the univariate counterparts brings modest improvement in wind direction prediction but not in wind speed prediction. Between restricted and unrestricted versions of VAR models, there is little difference in terms of forecasting performance. (author)
Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity
M. Sonia Terreros-Olarte
2013-05-01
Full Text Available This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV plant. The model is called HIstorical SImilar MIning (HISIMI model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.
Bleier, T.; Heraud, J. A.; Dunson, J. C.
2015-12-01
QuakeFinder (QF) and its international collaborators have installed and currently maintain 165 three-axis induction magnetometer instrument sites in California, Peru, Taiwan, Greece, Chile and Sumatra. The data from these instruments are being analyzed for pre-quake signatures. This analysis consists of both private research by QuakeFinder, and institutional collaborators (PUCP in Peru, NCU in Taiwan, PUCC in Chile, NOA in Greece, Syiah Kuala University in Indonesia, LASP at U of Colo., Stanford, and USGS). Recently, NASA Hq and QuakeFinder tried a new approach to help with the analysis of this huge (50+TB) data archive. A collaboration with Apirio/TopCoder, Harvard University, Amazon, QuakeFinder, and NASA Hq. resulted in an open algorithm development contest called "Quest for Quakes" in which contestants (freelance algorithm developers) attempted to identify quakes from a subset of the QuakeFinder data (3TB). The contest included a $25K prize pool, and contained 100 cases where earthquakes (and null sets) included data from up to 5 remote sites, near and far from quakes greater than M4. These data sets were made available through Amazon.com to hundreds of contestants over a two week contest period. In a more traditional approach, several new algorithms were tried by actively sharing the QF data with universities over a longer period. These algorithms included Principal Component Analysis-PCA and deep neural networks in an effort to automatically identify earthquake signals within typical, noise-filled environments. This presentation examines the pros and cons of employing these two approaches, from both logistical and scientific perspectives.
Nanda, Trushnamayee; Beria, Harsh; Sahoo, Bhabagrahi; Chatterjee, Chandranath
2016-04-01
Increasing frequency of hydrologic extremes in a warming climate call for the development of reliable flood forecasting systems. The unavailability of meteorological parameters in real-time, especially in the developing parts of the world, makes it a challenging task to accurately predict flood, even at short lead times. The satellite-based Tropical Rainfall Measuring Mission (TRMM) provides an alternative to the real-time precipitation data scarcity. Moreover, rainfall forecasts by the numerical weather prediction models such as the medium term forecasts issued by the European Center for Medium range Weather Forecasts (ECMWF) are promising for multistep-ahead flow forecasts. We systematically evaluate these rainfall products over a large catchment in Eastern India (Mahanadi River basin). We found spatially coherent trends, with both the real-time TRMM rainfall and ECMWF rainfall forecast products overestimating low rainfall events and underestimating high rainfall events. However, no significant bias was found for the medium rainfall events. Another key finding was that these rainfall products captured the phase of the storms pretty well, but suffered from consistent under-prediction. The utility of the real-time TRMM and ECMWF forecast products are evaluated by rainfall-runoff modeling using different artificial neural network (ANN)-based models up to 3-days ahead. Keywords: TRMM; ECMWF; forecast; ANN; rainfall-runoff modeling
Load forecast method of electric vehicle charging station using SVR based on GA-PSO
Lu, Kuan; Sun, Wenxue; Ma, Changhui; Yang, Shenquan; Zhu, Zijian; Zhao, Pengfei; Zhao, Xin; Xu, Nan
2017-06-01
This paper presents a Support Vector Regression (SVR) method for electric vehicle (EV) charging station load forecast based on genetic algorithm (GA) and particle swarm optimization (PSO). Fuzzy C-Means (FCM) clustering is used to establish similar day samples. GA is used for global parameter searching and PSO is used for a more accurately local searching. Load forecast is then regressed using SVR. The practical load data of an EV charging station were taken to illustrate the proposed method. The result indicates an obvious improvement in the forecasting accuracy compared with SVRs based on PSO and GA exclusively.
Staudacher, K.
1985-02-01
Full Base Isolation (FBI, 3-D), an antiseismic concept for structures, adds vertical flexibility to horizontal base isolation (HBI, 2-D). Extensive experimental testing at the Swiss Federal Institute of Technology and the University of California, Berkeley, has shown FBI to be a practicable way to reach the final goal of earthquake protection, i.e. elastic behavior of the structural frame in extreme earthquakes. Swiss engineers pioneered base isolation by the construction of the Pestalozzi School at Skopje in 1968. Further development has made Integral Earthquake Protection possible for structures and their contents. (orig.).
Ionospheric scintillation forecasting model based on NN-PSO technique
Sridhar, M.; Venkata Ratnam, D.; Padma Raju, K.; Sai Praharsha, D.; Saathvika, K.
2017-09-01
The forecasting and modeling of ionospheric scintillation effects are crucial for precise satellite positioning and navigation applications. In this paper, a Neural Network model, trained using Particle Swarm Optimization (PSO) algorithm, has been implemented for the prediction of amplitude scintillation index (S4) observations. The Global Positioning System (GPS) and Ionosonde data available at Darwin, Australia (12.4634° S, 130.8456° E) during 2013 has been considered. The correlation analysis between GPS S4 and Ionosonde drift velocities (hmf2 and fof2) data has been conducted for forecasting the S4 values. The results indicate that forecasted S4 values closely follow the measured S4 values for both the quiet and disturbed conditions. The outcome of this work will be useful for understanding the ionospheric scintillation phenomena over low latitude regions.
Ratio-based lengths of intervals to improve fuzzy time series forecasting.
Huarng, Kunhuang; Yu, Tiffany Hui-Kuang
2006-04-01
The objective of this study is to explore ways of determining the useful lengths of intervals in fuzzy time series. It is suggested that ratios, instead of equal lengths of intervals, can more properly represent the intervals among observations. Ratio-based lengths of intervals are, therefore, proposed to improve fuzzy time series forecasting. Algebraic growth data, such as enrollments and the stock index, and exponential growth data, such as inventory demand, are chosen as the forecasting targets, before forecasting based on the various lengths of intervals is performed. Furthermore, sensitivity analyses are also carried out for various percentiles. The ratio-based lengths of intervals are found to outperform the effective lengths of intervals, as well as the arbitrary ones in regard to the different statistical measures. The empirical analysis suggests that the ratio-based lengths of intervals can also be used to improve fuzzy time series forecasting.
GIS BASED SYSTEM FOR POST-EARTHQUAKE CRISIS MANAGMENT USING CELLULAR NETWORK
M. Raeesi
2013-09-01
Full Text Available Earthquakes are among the most destructive natural disasters. Earthquakes happen mainly near the edges of tectonic plates, but they may happen just about anywhere. Earthquakes cannot be predicted. Quick response after disasters, like earthquake, decreases loss of life and costs. Massive earthquakes often cause structures to collapse, trapping victims under dense rubble for long periods of time. After the earthquake and destroyed some areas, several teams are sent to find the location of the destroyed areas. The search and rescue phase usually is maintained for many days. Time reduction for surviving people is very important. A Geographical Information System (GIS can be used for decreasing response time and management in critical situations. Position estimation in short period of time time is important. This paper proposes a GIS based system for post–earthquake disaster management solution. This system relies on several mobile positioning methods such as cell-ID and TA method, signal strength method, angel of arrival method, time of arrival method and time difference of arrival method. For quick positioning, the system can be helped by any person who has a mobile device. After positioning and specifying the critical points, the points are sent to a central site for managing the procedure of quick response for helping. This solution establishes a quick way to manage the post–earthquake crisis.
GIS Based System for Post-Earthquake Crisis Managment Using Cellular Network
Raeesi, M.; Sadeghi-Niaraki, A.
2013-09-01
Earthquakes are among the most destructive natural disasters. Earthquakes happen mainly near the edges of tectonic plates, but they may happen just about anywhere. Earthquakes cannot be predicted. Quick response after disasters, like earthquake, decreases loss of life and costs. Massive earthquakes often cause structures to collapse, trapping victims under dense rubble for long periods of time. After the earthquake and destroyed some areas, several teams are sent to find the location of the destroyed areas. The search and rescue phase usually is maintained for many days. Time reduction for surviving people is very important. A Geographical Information System (GIS) can be used for decreasing response time and management in critical situations. Position estimation in short period of time time is important. This paper proposes a GIS based system for post-earthquake disaster management solution. This system relies on several mobile positioning methods such as cell-ID and TA method, signal strength method, angel of arrival method, time of arrival method and time difference of arrival method. For quick positioning, the system can be helped by any person who has a mobile device. After positioning and specifying the critical points, the points are sent to a central site for managing the procedure of quick response for helping. This solution establishes a quick way to manage the post-earthquake crisis.
Neural Networks-Based Forecasting Regarding the Convergence Process of CEE Countries to the Eurozone
Magdalena RĂDULESCU
2014-06-01
Full Text Available In the crisis frame, many forecasts failed to provide well determined ratios. What we tried to explain in this paper is how some selected Central and Eastern European countries will perform in the near future: Romania, Bulgaria, Hungary, Poland and Czech Republic, using neural networks- based forecasting model which we created for the nominal and real convergence ratios. As a methodology, we propose the forecasting based on artificial neural network (ANN, using the well-known software tool GMDH Shell. For each output variable, we obtain a forecast model, according to previous values and other input related variables, and we applied the model to all countries. Our forecasts are much closer to the partial results of 2013 in the analyzed countries than the European Commission’s or other international organizations’ forecasts. The results of the forecast are important both for governments to design their financial strategies and for the investors in these selected countries. According to our results, the Czech Republic seems to be closer to achieve its nominal convergence in the next two years, but it faces great difficulties in the real convergence area, because it did not overpass the recession.
Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint
Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen
2017-05-17
A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severe voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.
Seismic comprehensive forecast based on modified project pursuit regression
Anxu Wu; Xiangdong Lin; Changsheng Jiang; Yongxian Zhang; Xiaodong Zhang; Mingxiao Li; Pingan Li
2009-01-01
In the research of projection pursuit for seismic comprehensive forecast, the algorithm of projection pursuit regression (PPR) is one of most applicable methods. But generally, the algorithm structure of the PPR is very complicated. By partial smooth regressions for many times, it has a large amount of calculation and complicated extrapolation, so it is easily trapped in partial solution. On the basis of the algorithm features of the PPR method, some solutions are given as below to aim at some shortcomings in the PPR calculation: to optimize project direction by using particle swarm optimization instead of Gauss-Newton algorithm, to simplify the optimal process with fitting ridge function by using Hermitian polynomial instead of piecewise linear regression. The overall optimal ridge function can be obtained without grouping the parameter optimization. The modeling capability and calculating accuracy of projection pursuit method are tested by means of numerical emulation technique on the basis of particle swarm optimization and Hermitian polynomial, and then applied to the seismic comprehensive forecasting models of poly-dimensional seismic time series and general disorder seismic samples. The calculation and analysis show that the projection pursuit model in this paper is characterized by simplicity, celerity and effectiveness. And this model is approved to have satisfactory effects in the real seismic comprehensive forecasting, which can be regarded as a comprehensive analysis method in seismic comprehensive forecast.
Adjoint-Based Forecast Error Sensitivity Diagnostics in Data Assimilation
Langland, R.; Daescu, D.
2016-12-01
We present an up-to-date review of the adjoint-data assimilation system (DAS) approach to evaluate the forecast sensitivity to error covariance parameters and provide guidance to flow-dependent adaptive covariance tuning (ACT) procedures. New applications of the forecast sensitivity to observation error covariance (FSR) are investigated including the sensitivity to observation error correlations and a priori first-order assessment to the error correlation impact on the forecasts. Issues related to ambiguities in the a posteriori estimation to the observation error covariance (R) and background error covariance (B) are discussed. A synergistic framework to adaptive covariance tuning is considered that combines R-estimates derived from a posteriori covariance diagnosis and FSR derivative information. The evaluation of the forecast sensitivity to the innovation-weight coefficients is introduced as a computationally-feasible approach to account for the characteristics of both R- and B-parameters and perform direct tuning of the DAS gain operator (K). Theoretical aspects are discussed and recent results are provided with the adjoint versions of the Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR).
Analysis of the seismicity preceding large earthquakes
Stallone, Angela; Marzocchi, Warner
2017-04-01
The most common earthquake forecasting models assume that the magnitude of the next earthquake is independent from the past. This feature is probably one of the most severe limitations of the capability to forecast large earthquakes. In this work, we investigate empirically on this specific aspect, exploring whether variations in seismicity in the space-time-magnitude domain encode some information on the size of the future earthquakes. For this purpose, and to verify the stability of the findings, we consider seismic catalogs covering quite different space-time-magnitude windows, such as the Alto Tiberina Near Fault Observatory (TABOO) catalogue, the California and Japanese seismic catalog. Our method is inspired by the statistical methodology proposed by Baiesi & Paczuski (2004) and elaborated by Zaliapin et al. (2008) to distinguish between triggered and background earthquakes, based on a pairwise nearest-neighbor metric defined by properly rescaled temporal and spatial distances. We generalize the method to a metric based on the k-nearest-neighbors that allows us to consider the overall space-time-magnitude distribution of k-earthquakes, which are the strongly correlated ancestors of a target event. Finally, we analyze the statistical properties of the clusters composed by the target event and its k-nearest-neighbors. In essence, the main goal of this study is to verify if different classes of target event magnitudes are characterized by distinctive "k-foreshocks" distributions. The final step is to show how the findings of this work may (or not) improve the skill of existing earthquake forecasting models.
Operational flash flood forecasting platform based on grid technology
Thierion, V.; Ayral, P.-A.; Angelini, V.; Sauvagnargues-Lesage, S.; Nativi, S.; Payrastre, O.
2009-04-01
Flash flood events of south of France such as the 8th and 9th September 2002 in the Grand Delta territory caused important economic and human damages. Further to this catastrophic hydrological situation, a reform of flood warning services have been initiated (set in 2006). Thus, this political reform has transformed the 52 existing flood warning services (SAC) in 22 flood forecasting services (SPC), in assigning them territories more hydrological consistent and new effective hydrological forecasting mission. Furthermore, national central service (SCHAPI) has been created to ease this transformation and support local services in their new objectives. New functioning requirements have been identified: - SPC and SCHAPI carry the responsibility to clearly disseminate to public organisms, civil protection actors and population, crucial hydrologic information to better anticipate potential dramatic flood event, - a new effective hydrological forecasting mission to these flood forecasting services seems essential particularly for the flash floods phenomenon. Thus, models improvement and optimization was one of the most critical requirements. Initially dedicated to support forecaster in their monitoring mission, thanks to measuring stations and rainfall radar images analysis, hydrological models have to become more efficient in their capacity to anticipate hydrological situation. Understanding natural phenomenon occuring during flash floods mainly leads present hydrological research. Rather than trying to explain such complex processes, the presented research try to manage the well-known need of computational power and data storage capacities of these services. Since few years, Grid technology appears as a technological revolution in high performance computing (HPC) allowing large-scale resource sharing, computational power using and supporting collaboration across networks. Nowadays, EGEE (Enabling Grids for E-science in Europe) project represents the most important
K. Liechti
2013-10-01
Full Text Available This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues, an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.
A functional assay-based strategy for nanomaterial risk forecasting
Hendren, Christine Ogilvie, E-mail: christine.hendren@duke.edu [Center for the Environmental Implications of NanoTechnology, Duke University, Durham, NC 27708 (United States); Lowry, Gregory V., E-mail: glowry@andrew.cmu.edu [Center for the Environmental Implications of NanoTechnology, Duke University, Durham, NC 27708 (United States); Department of Civil and Environmental Engineering, Carnegie Mellon University, 119 Porter Hall, Pittsburgh, PA 15213 (United States); Unrine, Jason M., E-mail: jason.unrine@uky.edu [Center for the Environmental Implications of NanoTechnology, Duke University, Durham, NC 27708 (United States); Department of Plant and Soil Sciences, University of Kentucky, Agricultural Science Center, Lexington, KY 40546 (United States); Wiesner, Mark R., E-mail: wiesner@duke.edu [Center for the Environmental Implications of NanoTechnology, Duke University, Durham, NC 27708 (United States); Department of Civil and Environmental Engineering, Duke University, 121 Hudson Hall PO Box 90287, Durham, NC 27708 (United States)
2015-12-01
The study of nanomaterial impacts on environment, health and safety (nanoEHS) has been largely predicated on the assumption that exposure and hazard can be predicted from physical–chemical properties of nanomaterials. This approach is rooted in the view that nanoöbjects essentially resemble chemicals with additional particle-based attributes that must be included among their intrinsic physical–chemical descriptors. With the exception of the trivial case of nanomaterials made from toxic or highly reactive materials, this approach has yielded few actionable guidelines for predicting nanomaterial risk. This article addresses inherent problems in structuring a nanoEHS research strategy based on the goal of predicting outcomes directly from nanomaterial properties, and proposes a framework for organizing data and designing integrated experiments based on functional assays (FAs). FAs are intermediary, semi-empirical measures of processes or functions within a specified system that bridge the gap between nanomaterial properties and potential outcomes in complex systems. The three components of a functional assay are standardized protocols for parameter determination and reporting, a theoretical context for parameter application and reference systems. We propose the identification and adoption of reference systems where FAs may be applied to provide parameter estimates for environmental fate and effects models, as well as benchmarks for comparing the results of FAs and experiments conducted in more complex and varied systems. Surface affinity and dissolution rate are identified as two critical FAs for characterizing nanomaterial behavior in a variety of important systems. The use of these FAs to predict bioaccumulation and toxicity for initial and aged nanomaterials is illustrated for the case of silver nanoparticles and Caenorhabditis elegans. - Highlights: • Approaches to predict risk directly from nanomaterial (NM) properties are problematic. • We propose
Sabuncu, A.; Uca Avci, Z. D.; Sunar, F.
2016-06-01
Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very important step after earthquakes since earthquake-induced building damage is one of the most critical threats to cities and countries in terms of the area of damage, rate of collapsed buildings, the damage grade near the epicenters and also building damage types for all constructions. Van-Ercis (Turkey) earthquake (Mw= 7.1) was occurred on October 23th, 2011; at 10:41 UTC (13:41 local time) centered at 38.75 N 43.36 E that places the epicenter about 30 kilometers northern part of the city of Van. It is recorded that, 604 people died and approximately 4000 buildings collapsed or seriously damaged by the earthquake. In this study, high-resolution satellite images of Van-Ercis, acquired by Quickbird-2 (Digital Globe Inc.) after the earthquake, were used to detect the debris areas using an object-based image classification. Two different land surfaces, having homogeneous and heterogeneous land covers, were selected as case study areas. As a first step of the object-based image processing, segmentation was applied with a convenient scale parameter and homogeneity criterion parameters. As a next step, condition based classification was used. In the final step of this preliminary study, outputs were compared with streetview/ortophotos for the verification and evaluation of the classification accuracy.
A Simplified Short Term Load Forecasting Method Based on Sequential Patterns
Kouzelis, Konstantinos; Bak-Jensen, Birgitte; Mahat, Pukar
2014-01-01
, require considerable expertise for model construction and re-construction. Consequently, they might be impractical to use in case multiple regional forecasts are to be conducted. In this perspective, a simplified hour-ahead load forecasting algorithm was created so as to provide an automated approach...... to the problem as an alternative to other established forecasting techniques. This algorithm is based on sequential patterns and, hence, the continuous data are discretized in order to compare recent to past patterns. Although some error due to discretization is introduced, the method performs adequately well...... in comparison with an ARIMA model....
Short-term load forecasting study of wind power based on Elman neural network
Tian, Xinran; Yu, Jing; Long, Teng; Liu, Jicheng
2017-01-01
Since wind power has intermittent, irregular and volatility nature, improving load forecasting accuracy of wind power has significant influence on controlling wind system and guarantees stable operation of power grids. This paper constructed the wind farm loading forecasting in short-term based on Elman neural network, and made a numerical example analysis. . Examples show that, using input delayed of feedback Elman neural network, can reflect the inherent laws of wind load operation better, so as to present a new idea for short-term load forecasting of wind power.
Fu-Kwun Wang
2012-01-01
Full Text Available It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides short-term forecasts using four data points. This study develops a combined model based on the rolling Grey model (RGM and the Bass diffusion model to forecast motherboard shipments. In addition, we investigate evolutionary optimization algorithms to determine the optimal parameters. Our results indicate that the combined model using a hybrid algorithm outperforms other methods for the fitting and forecasting processes in terms of mean absolute percentage error.
Real-time determination of the worst tsunami scenario based on Earthquake Early Warning
Furuya, Takashi; Koshimura, Shunichi; Hino, Ryota; Ohta, Yusaku; Inoue, Takuya
2016-04-01
In recent years, real-time tsunami inundation forecasting has been developed with the advances of dense seismic monitoring, GPS Earth observation, offshore tsunami observation networks, and high-performance computing infrastructure (Koshimura et al., 2014). Several uncertainties are involved in tsunami inundation modeling and it is believed that tsunami generation model is one of the great uncertain sources. Uncertain tsunami source model has risk to underestimate tsunami height, extent of inundation zone, and damage. Tsunami source inversion using observed seismic, geodetic and tsunami data is the most effective to avoid underestimation of tsunami, but needs to expect more time to acquire the observed data and this limitation makes difficult to terminate real-time tsunami inundation forecasting within sufficient time. Not waiting for the precise tsunami observation information, but from disaster management point of view, we aim to determine the worst tsunami source scenario, for the use of real-time tsunami inundation forecasting and mapping, using the seismic information of Earthquake Early Warning (EEW) that can be obtained immediately after the event triggered. After an earthquake occurs, JMA's EEW estimates magnitude and hypocenter. With the constraints of earthquake magnitude, hypocenter and scaling law, we determine possible multi tsunami source scenarios and start searching the worst one by the superposition of pre-computed tsunami Green's functions, i.e. time series of tsunami height at offshore points corresponding to 2-dimensional Gaussian unit source, e.g. Tsushima et al., 2014. Scenario analysis of our method consists of following 2 steps. (1) Searching the worst scenario range by calculating 90 scenarios with various strike and fault-position. From maximum tsunami height of 90 scenarios, we determine a narrower strike range which causes high tsunami height in the area of concern. (2) Calculating 900 scenarios that have different strike, dip, length
Rundle, John B.; Tiampo, Kristy F.; Klein, William
2007-06-01
The recent article in Eos by Kafka and Ebel [2007] is a criticism of a NASA press release issued on 4 October 2004 describing an earthquake forecast (http://quakesim.jpl.nasa.gov/scorecard.html) based on a pattern informatics (PI) method [Rundle et al., 2002]. This 2002 forecast was a map indicating the probable locations of earthquakes having magnitude m>5.0 that would occur over the period of 1 January 2000 to 31 December 2009. Kafka and Ebel [2007] compare the Rundle et al. [2002] forecast to a retrospective analysis using a cellular seismology (CS) method. Here we analyze the performance of the Rundle et al. [2002] forecast using the first 15 of the m>5.0 earthquakes that occurred in the area covered by the forecasts.
Multitask Learning-Based Security Event Forecast Methods for Wireless Sensor Networks
Hui He
2016-01-01
Full Text Available Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wireless sensor networks to various security attacks, a region similarity multitask-based security event forecast method for wireless sensor networks is proposed. This method performs topology partitioning on a large-scale sensor network and calculates the similarity degree among regional subnetworks. The trend of unknown network security events can be predicted through multitask learning of the occurrence and transmission characteristics of known network security events. Second, in case of lacking regional data, the quantitative trend of unknown regional network security events can be calculated. This study introduces a sensor network security event forecast method named Prediction Network Security Incomplete Unmarked Data (PNSIUD method to forecast missing attack data in the target region according to the known partial data in similar regions. Experimental results indicate that for an unknown security event forecast the forecast accuracy and effects of the similarity forecast algorithm are better than those of single-task learning method. At the same time, the forecast accuracy of the PNSIUD method is better than that of the traditional support vector machine method.
Study and application of time series forecasting based on rough set and Kernel method
杨淑霞
2008-01-01
A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the redundant attribute for forecasting from condition attribute by rough set method; then use the minimum condition attribute set obtained after the reduction and the corresponding initial data, reform a new training sample set which only retain the important attributes influencing the forecasting accuracy; study and train the support vector machine with the training sample obtained after reduction, and then input the reformed testing sample set according to the minimum condition attribute and corresponding initial data. The model was tested and the mapping relation was got between the condition attribute and forecasting variable. Eventually, power supply and demand were forecasted in this model. The average absolute error rates of power consumption of the whole society and yearly maximum load are respectively 14.21% and 13.23%. It shows that RS-SVM time series forecasting model has high forecasting accuracy.
地震预测预报能与不能的争论%Dispute on Capability and Incapability for Earthquake Prediction and Forecast
李世煇
2012-01-01
对破坏力巨大的地震的预测、预报,既是地震理论上的难题,也是技术上、实践上和政治政策上的难题。在我国曾有过成功的经验,但也有人为因素不能回避的惨痛的教训。在学术上存在着地震不可预测预报与地震可预测预报的尖锐争论,而这种争论又因为掺杂了政治上的因素而变得更加微妙、复杂、纠缠不清和难以深入,也制约着防震减灾事业的发展。地震不可预测预报论是目前的主流观点。不可预测预报论与可预测预报论的学术立场分歧也必然延伸到人们的防震减灾的社会实践当中。六十余年来,可预测预报论者积极进行预测预报上的学术创新,先后诞生过李四光的地质力学理论、翁文波的信息预测理论和天灾预测法、耿庆国的旱震关系理论、钱复业等的＂潮汐力谐振共振短临前兆模型＂、钱学森的＂开放的复杂巨系统论＂与＂定性与定量的综合集成法＂等等理论和技术成果。可预测预报论突破传统科学的还原论思维模式,积极发展现代系统论的思维模式,并挖掘中国古代传统科学中的一些要素和整理利用中国古代积累下来的对自然灾变观测的丰富资料,进行理论创新,并在地震预测预报实践中取得了一定的成效,但目前仍然克服不了种种理论的批判和质疑。尽管如此,可预测预报论者仍坚信如果能更好地克服思想上、体制上的障碍,中国地震预测预报科学研究工作者能创新出一种＂半经验半理论的系统科学方法＂的地震预测预报理论,在该领域为人类的科学进步事业做出巨大贡献。%The prediction and forecast of massive destructive earthquakes are not only a problem in earthquake theory,but also that in technique,practice and political policy.There have been in China successful experiences and unavoidable agonizing lessons due to human factors.Academically,the disputes
Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas;
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas;
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
Short-Term Forecasting of Urban Water Consumption Based on the Largest Lyapunov Exponent
无
2007-01-01
An approach for short-term forecasting of municipal water consumption was presented based on the largest Lyapunov exponent of chaos theory. The chaotic characteristics of time series of urban water consumption were examined by means of the largest Lyapunov exponent and correlation dimension. By using the largest Lyapunov exponent a short-term forecasting model for urban water consumption was developed, which was compared with the artificial neural network (ANN) approach in a case study. The result indicates that the model based on the largest Lyapunov exponent has higher prediction precision and forecasting stability than the ANN method, and its forecasting mean relative error is 9.6% within its maximum predictable time scale while it is 60.6% beyond the scale.
Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
A new method for choosing parameters in delay reconstruction-based forecast strategies
Garland, Joshua; Bradley, Elizabeth
2015-01-01
Delay-coordinate reconstruction is a proven modeling strategy for building effective forecasts of nonlinear time series. The first step in this process is the estimation of good values for two parameters, the time delay and the reconstruction dimension. Many heuristics and strategies have been proposed in the literature for estimating these values. Few, if any, of these methods were developed with forecasting in mind, however, and their results are not optimal for that purpose. Even so, these heuristics -- intended for other applications -- are routinely used when building delay coordinate reconstruction-based forecast models. In this paper, we propose a general framework for choosing optimal parameter values for forecast methods that are based on delay-coordinate reconstructions. The basic calculation involves maximizing the shared information between each delay vector and the future state of the system. We illustrate the effectiveness of this method on several synthetic and experimental systems, showing tha...
Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning
Ya’nan Wang
2016-01-01
Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.
Analysis of the Earthquake Impact towards water-based fire extinguishing system
Lee, J.; Hur, M.; Lee, K.
2015-09-01
Recently, extinguishing system installed in the building when the earthquake occurred at a separate performance requirements. Before the building collapsed during the earthquake, as a function to maintain a fire extinguishing. In particular, the automatic sprinkler fire extinguishing equipment, such as after a massive earthquake without damage to piping also must maintain confidentiality. In this study, an experiment installed in the building during the earthquake, the water-based fire extinguishing saw grasp the impact of the pipe. Experimental structures for water-based fire extinguishing seismic construction step by step, and then applied to the seismic experiment, the building appears in the extinguishing of the earthquake response of the pipe was measured. Construction of acceleration caused by vibration being added to the size and the size of the displacement is measured and compared with the data response of the pipe from the table, thereby extinguishing water piping need to enhance the seismic analysis. Define the seismic design category (SDC) for the four groups in the building structure with seismic criteria (KBC2009) designed according to the importance of the group and earthquake seismic intensity. The event of a real earthquake seismic analysis of Category A and Category B for the seismic design of buildings, the current fire-fighting facilities could have also determined that the seismic performance. In the case of seismic design categories C and D are installed in buildings to preserve the function of extinguishing the required level of seismic retrofit design is determined.
Hovius, Niels; Marc, Odin; Meunier, Patrick
2016-04-01
Large earthquakes deform Earth's surface and drive topographic growth in the frontal zones of mountain belts. They also induce widespread mass wasting, reducing relief. Preliminary studies have proposed that above a critical magnitude earthquake would induce more erosion than uplift. Other parameters such as fault geometry or earthquake depth were not considered yet. A new seismologically consistent model of earthquake induced landsliding allow us to explore the importance of parameters such as earthquake depth and landscape steepness. We have compared these eroded volume prediction with co-seismic surface uplift computed with Okada's deformation theory. We found that the earthquake depth and landscape steepness to be the most important parameters compared to the fault geometry (dip and rake). In contrast with previous studies we found that largest earthquakes will always be constructive and that only intermediate size earthquake (Mw ~7) may be destructive. Moreover, with landscapes insufficiently steep or earthquake sources sufficiently deep earthquakes are predicted to be always constructive, whatever their magnitude. We have explored the long term topographic contribution of earthquake sequences, with a Gutenberg Richter distribution or with a repeating, characteristic earthquake magnitude. In these models, the seismogenic layer thickness, that sets the depth range over which the series of earthquakes will distribute, replaces the individual earthquake source depth.We found that in the case of Gutenberg-Richter behavior, relevant for the Himalayan collision for example, the mass balance could remain negative up to Mw~8 for earthquakes with a sub-optimal uplift contribution (e.g., transpressive or gently-dipping earthquakes). Our results indicate that earthquakes have probably a more ambivalent role in topographic building than previously anticipated, and suggest that some fault systems may not induce average topographic growth over their locked zone during a
Comparative evaluation of physics-based and statistical forecasts in Northern California
Segou, M.; Parsons, T.; Ellsworth, W.
2013-12-01
perform a retrospective forecast test using Northern California seismicity for the period between 1980 and 2009. We compare 7 realizations of the short-term clustering epidemic-type aftershock sequence (ETAS) model, and 21 models combining Coulomb stress change calculations and Rate/State theory (CRS) to forecast seismicity rates in 10 day time intervals. We employ a common learning phase (1974-1980) for CRS models to ensure consistency, and we evaluate the forecasts with log likelihood statistics to detect any spatial inconsistencies and compare the total numbers of forecasts versus observed events. We find that: (1) ETAS models are better forecasters of the spatial evolution in seismicity in the near-source region, (2) CRS models can compete with ETAS models away from the mainshock rupture, and for short periods after mainshocks, (3) CRS models with optimally oriented receiver fault planes perform better in the first few days after mainshocks, whereas mapped fault planes should be implemented for longer-term forecasting, and (4) CRS models based on shear stress change calculations have comparable performance with Coulomb stress change models, with the benefit of lesser parameters involved in stress calculations. We conclude that physics-based and statistical forecast models are complimentary to each other and that future forecasts should be based on statistical models for near-source regions, and physical models for longer periods and distances. However, the realization of the CRS models involves a number of critical parameters (reference seismicity rates, regional stress field, and loading rates), which should be retrospectively tested to improve the predictive power of physics-based models.
PSF : Introduction to R Package for Pattern Sequence Based Forecasting Algorithm
Bokde, Neeraj; Asencio-Cortés, Gualberto; Martínez-Álvarez, Francisco; Kulat, Kishore
2016-01-01
This paper discusses about an R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like op...
Evaluating Atlantic tropical cyclone track error distributions based on forecast confidence
Hauke, Matthew D.
2006-01-01
A new Tropical Cyclone (TC) surface wind speed probability product from the National Hurricane Center (NHC) takes into account uncertainty in track, maximum wind speed, and wind radii. A Monte Carlo (MC) model is used that draws from probability distributions based on historic track errors. In this thesis, distributions of forecast track errors conditioned on forecast confidence are examined to determine if significant differences exist in distribution characteristics. Two predictors are ...
Fu-Kwun Wang; Yu-Yao Hsiao; Ku-Kuang Chang
2012-01-01
It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides short-term forecasts using four data points. This study develops a combined model based on the rolling Grey model (RGM) and the Bass diffusion model to forecast motherboard shipments. In addition, we investigate evolutio...
Fu-Kwun Wang; Yu-Yao Hsiao; Ku-Kuang Chang
2012-01-01
It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides short-term forecasts using four data points. This study develops a combined model based on the rolling Grey model (RGM) and the Bass diffusion model to forecast motherboard shipments. In addition, we investigate evolutio...
Traffic Forecasting Model Based on Takagi-Sugeno Fuzzy Logical System
WANG Wei-gong; LI Zheng; CHENG Mei-ling
2005-01-01
The local multiple regression fuzzy(LMRF)model based on Takagi-Sugeno fuzzy logical system and its application in traffic forecasting is proposed. Besides its prediction accuracy is testified and the model is proved much better than conventional forecasting methods. According to the regional traffic system, the model perfectly states the complex non-linear relation of the traffic and the local social economy. The model also efficiently deals with the system lack of enough data.
Cheng Yugui
2013-01-01
A kind of power forecast model combined cellular genetic algorithm with BP neural network was established in this article. Mid-long term power demand in urban areas was done load forecasting and analysis based on material object of the actual power consumption in urban areas of Nanchang. The results show that this method has the characteristic of the minimum training times, the shortest consumption time, the minimum error and the shortest operation time to obtain the best fitting effect.
Seasonal Forecasting of Fire Weather Based on a New Global Fire Weather Database
Dowdy, Andrew J.; Field, Robert D.; Spessa, Allan C.
2016-01-01
Seasonal forecasting of fire weather is examined based on a recently produced global database of the Fire Weather Index (FWI) system beginning in 1980. Seasonal average values of the FWI are examined in relation to measures of the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The results are used to examine seasonal forecasts of fire weather conditions throughout the world.
Delorit, Justin; Cristian Gonzalez Ortuya, Edmundo; Block, Paul
2017-09-01
In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October-January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950-2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The
Short-term Power Load Forecasting Based on Gray Theory
Cui Herui
2013-11-01
Full Text Available Power load forecasting provides the basis for the preparation of power planning, especially the accurate short-term power load forecasting. It can formulate power rationing program of area load reliably and timely, to maintain the normal production and life. This article describes the gray prediction method, and improves GM (1,1 model via processing the original data sequence smoothly, using the correction model of parameteramending parameter values, adding the residual model, and also applying the idea of the metabolism. It conducts an empirical analysis of the 10KV large cable of Guigang Power Supply Bureau in Nan Ping, and verifies the limitations of ordinary gray theory. The improved gray model has a higher prediction accuracy than the conventional GM (1,1 model.
Neural Network Based Forecasting of Foreign Currency Exchange Rates
S. Kumar Chandar
2014-06-01
Full Text Available The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable to the prediction of foreign exchange rates. The exchange rates between Indian Rupee and four other major currencies, Pound Sterling, US Dollar, Euro and Japanese Yen are forecast by the trained neural networks. The neural network was trained by three different learning algorithms using historical data to find the suitable algorithm for prediction. The forecasting performance of the proposed system is evaluated using three statistical metrics and compared. The results presented here demonstrate that significantly close prediction can be made without extensive knowledge of market data.
Wani, Omar; Beckers, Joost V. L.; Weerts, Albrecht H.; Solomatine, Dimitri P.
2017-08-01
A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearest-neighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement.
A Model of Debris Flow Forecast Based on the Water-Soil Coupling Mechanism
Shaojie Zhang; Hongjuan Yang; Fangqiang Wei; Yuhong Jiang; Dunlong Liu
2014-01-01
Debris flow forecast is an important means of disaster mitigation. However, the accuracy of the statistics-based debris flow forecast is unsatisfied while the mechanism-based forecast is un-available at the watershed scale because most of existing researches on the initiation mechanism of de-bris flow took a single slope as the main object. In order to solve this problem, this paper developed a model of debris flow forecast based on the water-soil coupling mechanism at the watershed scale. In this model, the runoff and the instable soil caused by the rainfall in a watershed is estimated by the distrib-uted hydrological model (GBHM) and an instable identification model of the unsaturated soil. Because the debris flow is a special fluid composed of soil and water and has a bigger density, the density esti-mated by the runoff and instable soil mass in a watershed under the action of a rainfall is employed as a key factor to identify the formation probability of debris flow in the forecast model. The Jiangjia Gulley, a typical debris flow valley with a several debris flow events each year, is selected as a case study wa-tershed to test this forecast model of debris flow. According the observation data of Dongchuan Debris Flow Observation and Research Station, CAS located in Jiangjia Gulley, there were 4 debris flow events in 2006. The test results show that the accuracy of the model is satisfied.
Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-07-26
In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.
Housing Value Forecasting Based on Machine Learning Methods
Jingyi Mu
2014-01-01
Full Text Available In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM, least squares support vector machine (LSSVM, and partial least squares (PLS methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.
Tarr, A.; Benz, H.; Earle, P.; Wald, D. J.
2003-12-01
Earthquake Summary Posters (ESP's), a new product of the U.S. Geological Survey's Earthquake Program, are produced at the National Earthquake Information Center (NEIC) in Golden. The posters consist of rapidly-generated, GIS-based maps made following significant earthquakes worldwide (typically M>7.0, or events of significant media/public interest). ESP's consolidate, in an attractive map format, a large-scale epicentral map, several auxiliary regional overviews (showing tectonic and geographical setting, seismic history, seismic hazard, and earthquake effects), depth sections (as appropriate), a table of regional earthquakes, and a summary of the reional seismic history and tectonics. The immediate availability of the latter text summaries has been facilitated by the availability of Rapid, Accurate Tectonic Summaries (RATS) produced at NEIC and posted on the web following significant events. The rapid production of ESP's has been facilitated by generating, during the past two years, regional templates for tectonic areas around the world by organizing the necessary spatially-referenced data for the map base and the thematic layers that overlay the base. These GIS databases enable scripted Arc Macro Language (AML) production of routine elements of the maps (for example background seismicity, tectonic features, and probabilistic hazard maps). However, other elements of the maps are earthquake-specific and are produced manually to reflect new data, earthquake effects, and special characteristics. By the end of this year, approximately 85% of the Earth's seismic zones will be covered for generating future ESP's. During the past year, 13 posters were completed, comparable to the yearly average expected for significant earthquakes. Each year, all ESPs will be published on a CD in PDF format as an Open-File Report. In addition, each is linked to the special event earthquake pages on the USGS Earthquake Program web site (http://earthquake.usgs.gov). Although three formats
Morales-Esteban, A.; Martínez-Álvarez, F.; Reyes, J.
2013-05-01
A method to predict earthquakes in two of the seismogenic areas of the Iberian Peninsula, based on Artificial Neural Networks (ANNs), is presented in this paper. ANNs have been widely used in many fields but only very few and very recent studies have been conducted on earthquake prediction. Two kinds of predictions are provided in this study: a) the probability of an earthquake, of magnitude equal or larger than a preset threshold magnitude, within the next 7 days, to happen; b) the probability of an earthquake of a limited magnitude interval to happen, during the next 7 days. First, the physical fundamentals related to earthquake occurrence are explained. Second, the mathematical model underlying ANNs is explained and the configuration chosen is justified. Then, the ANNs have been trained in both areas: The Alborán Sea and the Western Azores-Gibraltar fault. Later, the ANNs have been tested in both areas for a period of time immediately subsequent to the training period. Statistical tests are provided showing meaningful results. Finally, ANNs were compared to other well known classifiers showing quantitatively and qualitatively better results. The authors expect that the results obtained will encourage researchers to conduct further research on this topic. Development of a system capable of predicting earthquakes for the next seven days Application of ANN is particularly reliable to earthquake prediction. Use of geophysical information modeling the soil behavior as ANN's input data Successful analysis of one region with large seismic activity
A Robust Scheme for the Global Earthquake Early Warning Based on Characteristic Frequency
Wu, Yu-Chang; Chiao, Ling-Yun; Wu, Cheng-Ju
2017-04-01
Earthquake hazards mitigation have always been an important issue. Prompt and rapid high precision magnitude estimation is essential to achieve the goal of effective early warning. However, the current state of the method including the maximum predominant period (τpmax), the vertical displacement of P-wave (Pd), and the τc × Pdmethod has reached a standstill for nearly a decade. The major shortcoming is that these methods are not quite applicable for large earthquakes (M>7). Therefore, a new magnitude estimation method for earthquake early warning is crucial and is needed for human preventing loss and casualties in the large earthquakes. Here we demonstrate a robust scheme based on the characteristic frequency. Our result shows a linear relation between the momentum magnitude and the characteristic frequency of P-wave which appears within the first few seconds. This method requires fewer calculations and doesn't need to apply any filter to obtain better results, so the data processing time needed for the real-time earthquake early warning system is greatly reduced. This method also indicates strong applicability for estimating earthquakes with magnitude larger than 7. We demonstrate a robust scheme of global earthquake early warning.
Mihaela Simionescu
2014-12-01
Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.
Lihua Yang
2015-04-01
Full Text Available Export volume forecasting of fresh fruits is a complex task due to the large number of factors affecting the demand. In order to guide the fruit growers’ sales, decreasing the cultivating cost and increasing their incomes, a hybrid fresh apple export volume forecasting model is proposed. Using the actual data of fresh apple export volume, the Seasonal Decomposition (SD model of time series and Radial Basis Function (RBF model of artificial neural network are built. The predictive results are compared among the three forecasting model based on the criterion of Mean Absolute Percentage Error (MAPE. The result indicates that the proposed combined forecasting model is effective because it can improve the prediction accuracy of fresh apple export volumes.
Study on load forecasting to data centers of high power density based on power usage effectiveness
Zhou, C. C.; Zhang, F.; Yuan, Z.; Zhou, L. M.; Wang, F. M.; Li, W.; Yang, J. H.
2016-08-01
There is usually considerable energy consumption in data centers. Load forecasting to data centers is in favor of formulating regional load density indexes and of great benefit to getting regional spatial load forecasting more accurately. The building structure and the other influential factors, i.e. equipment, geographic and climatic conditions, are considered for the data centers, and a method to forecast the load of the data centers based on power usage effectiveness is proposed. The cooling capacity of a data center and the index of the power usage effectiveness are used to forecast the power load of the data center in the method. The cooling capacity is obtained by calculating the heat load of the data center. The index is estimated using the group decision-making method of mixed language information. An example is given to prove the applicability and accuracy of this method.
Forecast method for used number of parts and components based on complex network
LIU Fu-yun; QI Guo-ning; YANG Qing-hai
2006-01-01
Applying directed complex network to model the main structure of a product family,according to in-degree bi-logarithmic coordinate distribution curve and distribution rule of nodes of the network,in-degree evolving rule of nodes of the network is presented and analytic expression of in-degree probability density of nodes is derived.Through the analysis of the relation between existing kinds of components and existing product numbers,an expression of the relation between kinds of components and product numbers is derived.A forecast method for the increment of component numbers and parts based on the increment of products is presented.As an example,the component numbers of an industrial steam turbine product family is forecasted,forecast result verified and forecast error analyzed.
Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing
Pinson, Pierre; Madsen, Henrik
methodology. In a first stage, ensemble forecasts of meteorological variables are converted to power through a suitable power curve model. The relevance and benefits of employing a newly developed orthogonal fitting method for the power curve model over the traditional least-squares one are discussed...... predictive distributions. Such a methodology has the benefit of yielding predictive distributions that are of increased reliability (in a probabilistic sense) in comparison with the raw ensemble forecasts, while taking advantage of their high resolution....... of probabilistic forecasts, the resolution of which may be maximized by using meteorological ensemble predictions as input. The paper concentrates on the test case of the Horns Rev wind farm over a period of approximately one year, in order to describe, apply and discuss a complete ensemble-based forecasting...
Rethinking earthquake-related DC-ULF electromagnetic phenomena: towards a physics-based approach
Q. Huang
2011-11-01
Full Text Available Numerous electromagnetic changes possibly related with earthquakes have been independently reported and have even been attempted to apply to short-term prediction of earthquakes. However, there are active debates on the above issue because the seismogenic process is rather complicated and the studies have been mainly empirical (i.e. a kind of experience-based approach. Thus, a physics-based study would be helpful for understanding earthquake-related electromagnetic phenomena and strengthening their applications. As a potential physics-based approach, I present an integrated research scheme, taking into account the interaction among observation, methodology, and physical model. For simplicity, this work focuses only on the earthquake-related DC-ULF electromagnetic phenomena. The main approach includes the following key problems: (1 how to perform a reliable and appropriate observation with some clear physical quantities; (2 how to develop a robust methodology to reveal weak earthquake-related electromagnetic signals from noisy background; and (3 how to develop plausible physical models based on theoretical analyses and/or laboratory experiments for the explanation of the earthquake-related electromagnetic signals observed in the field conditions.
Modeling the behavior of an earthquake base-isolated building.
Coveney, V. A.; Jamil, S.; Johnson, D. E.; Kulak, R. F.; Uras, R. A.
1997-11-26
Protecting a structure against earthquake excitation by supporting it on laminated elastomeric bearings has become a widely accepted practice. The ability to perform accurate simulation of the system, including FEA of the bearings, would be desirable--especially for key installations. In this paper attempts to model the behavior of elastomeric earthquake bearings are outlined. Attention is focused on modeling highly-filled, low-modulus, high-damping elastomeric isolator systems; comparisons are made between standard triboelastic solid model predictions and test results.
Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA
Dreher, Joseph G.; Crawford, Winifred; Lafosse, Richard; Hoeth, Brian; Burns, Kerry
2009-01-01
The peak winds near the surface are an important forecast element for space shuttle landings. As defined in the Flight Rules (FR), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings, and is required to issue surface average and 10-minute peak wind speed forecasts. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMU) developed a PC-based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center (KSC; Lambert 2003). However, the shuttle occasionally may land at Edwards Air Force Base (EAFB) in southern California when weather conditions at KSC in Florida are not acceptable, so SMG forecasters requested a similar tool be developed for EAFB.
Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-23
In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.
Spatial forecast of landslides in three gorges based on spatial data mining.
Wang, Xianmin; Niu, Ruiqing
2009-01-01
The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.
L. Mediero
2012-12-01
Full Text Available Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.
E. Ulutas
2012-06-01
Full Text Available This study analyzes the response of the Global Disasters Alerts and Coordination System (GDACS in relation to a case study: the Kepulaunan Mentawai earthquake and related tsunami, which occurred on 25 October 2010. The GDACS, developed by the European Commission Joint Research Center, combines existing web-based disaster information management systems with the aim to alert the international community in case of major disasters. The tsunami simulation system is an integral part of the GDACS. In more detail, the study aims to assess the tsunami hazard on the Mentawai and Sumatra coasts: the tsunami heights and arrival times have been estimated employing three propagation models based on the long wave theory. The analysis was performed in three stages: (1 pre-calculated simulations by using the tsunami scenario database for that region, used by the GDACS system to estimate the alert level; (2 near-real-time simulated tsunami forecasts, automatically performed by the GDACS system whenever a new earthquake is detected by the seismological data providers; and (3 post-event tsunami calculations using GCMT (Global Centroid Moment Tensor fault mechanism solutions proposed by US Geological Survey (USGS for this event. The GDACS system estimates the alert level based on the first type of calculations and on that basis sends alert messages to its users; the second type of calculations is available within 30–40 min after the notification of the event but does not change the estimated alert level. The third type of calculations is performed to improve the initial estimations and to have a better understanding of the extent of the possible damage. The automatic alert level for the earthquake was given between Green and Orange Alert, which, in the logic of GDACS, means no need or moderate need of international humanitarian assistance; however, the earthquake generated 3 to 9 m tsunami run-up along southwestern coasts of the Pagai Islands where 431 people died
Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power
Bracale, Antonio; Carpinelli, Guido; Di Fazio, Annarita; Khormali, Shahab
2014-01-01
Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems' losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered.
The "SABEIS" Project: Warning systems based on earthquake and tsunamis-induced ionospheric effects.
Rodriguez-Bouza, Marta; Sánchez-Dulcet, Francisco; Herraiz, Miguel; Rodríguez-Caderot, Gracia; Altadill, David; Blanch, Estefania; Santoyo, Miguel Angel
2016-04-01
The study of a possible lithosphere-atmosphere-ionosphere coupling (LAI) is mainly focused on the analysis and comprehension of atmospheric and ionospheric anomalies caused by extreme lithospheric events. In this context, earthquakes are considered as possible sources of atmosphere-ionosphere anomalies. The goal of the two-year long project SABEIS (Sistemas de Alerta Basados en Efectos de terremotos y tsunamis en la IonoSfera) granted by the Spanish Ministry of Economy and Competitiveness, is to analyze the disturbances caused by earthquakes and tsunamis and their possible contribution to warning systems. These topics are receiving increased attention in the scientific community and their correct understanding can meaningfully contribute to the protection of people and economic assets in areas subject to seismic threat. The project is based on the analysis of Total Electron Content (TEC) obtained from signals of Global Navigation Satellite Systems (GNSS) and anomalies of the ionospheric F2 layer observed in ionograms. This methodology was partially applied in a previous study of the Mw6.1 earthquake in Greece occurred on January 26, 2014. In that case two TEC disturbances were detected the days prior the earthquake. The first one, four days before, was registered by the majority of the stations analyzed over Europe and after studying its temporal variation, was considered unrelated to the earthquake. The second one occurred the day before the earthquake. This anomaly appeared only at stations close to the epicenter and their temporal proximity to the earthquake point to a possible connection with the earthquake preparation process. In the SABEIS project possible anomalies caused by earthquakes in Mexico and Peru with magnitude ranging from 5.5 to 8.2, will be studied. If the results confirm the influence of seismic events on the ionosphere, the possibility of incorporating this type of analysis in a seismic alert network for the Gulf of Cadiz (southern Iberian
WU XiaoPing; MAO Wei; HUANG Yong; HU Hui; HU YiLi
2009-01-01
Dividing the mainland Chins into different tectonic stress regions, we calculate tidal stress components along the seismic compressive and extensional principal stress axes at every earthquake's focus in different tectonic stress regions. Tidal stress triggering effect on every earthquake fault is analyzed. Based on this, the lunar-solar location parameters on the occurring times of earthquakes which suf-fered tidal triggering effects are calculated, and the distribution patterns of the lunar-solar location parameters in different tectonic stress regions are obtained. The results Indicate that earthquake tidal triggering effects and related astronomical characteristics are dependent on the properties of regional tectonic stress and the geographic locations of earthquake faults.
Improved grey-based approach for power demand forecasting
LIN Jia-mu; LIU Dan
2006-01-01
Grey theory is a multidisciplinary and generic theory to cope with systems of poor or deficient information. We proposed in this paper an improved grey method (GM) to overcome the disadvantages of the general GM(1,1). In the improved GM(1,1), a new background value formula is deduced and Markov-chain sign estimation is imbedded into the residual modification model. We tested the efficiency and accuracy of our model by applying it to the power demand forecasting in Taiwan. Experimental results demonstrate the new method has obviously a higher prediction accuracy than the general model.
Bialas, James; Oommen, Thomas; Rebbapragada, Umaa; Levin, Eugene
2016-07-01
Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.
Chen, T. C.; Hotta, D.; Kalnay, E.
2015-12-01
Operational numerical weather prediction (NWP) systems occasionally exhibit "forecast skill dropouts" in which the forecast skill drops to an abnormally low level, due in part to the assimilation of flawed observational data. Recent studies have shown that a diagnostic technique called Ensemble Forecast Sensitivity to Observations (EFSO) can detect such observations (Kalnay et.al 2012; Ota et al. 2013, Tellus A). Based on this technique, a new Quality Control (QC) scheme called Proactive QC (PQC) has been proposed which detects "flawed" observations using EFSO after just 6 hours forecast, when the analysis at the next cycle becomes available for verification and then repeats the analysis and forecast without using the detected observations (Hotta 2014). In Hotta (2014), it was shown using the JCSDA S4 Testbed that the 6hr PQC reduces the 24-hour forecast errors from the detected skill dropout events. With such encouraging results we are performing preliminary experiments towards operational implementation. First, we show that offline PQC correction can significantly reduce forecast errors up to 5 days, and that the reduction and improved areal coverage can grow with synoptic weather disturbances for several days. Second, with online PQC cycle experiment the reduction of forecast error is shown to be even larger than in the offline version, since the effect could accumulate over each time we perform a PQC correction. Finally, the operational center imposes very tight schedule in order to deliver the products on time, thus the computational cost has to be minimized in order for PQC to be implemented. To avoid performing the analysis twice, which is the most expensive part of PQC, we test the accuracy of constant-K approximation, which assumes the Kalman gain K doesn't change much given the fact that only a small subset of observation is rejected. In this presentation, we will demonstrate the performance and feasibility of PQC implementation in real-time operational
Szolgayová Elena
2014-03-01
Full Text Available Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting. In this paper, the forecasting performance of a new model combining a long range dependent autoregressive fractionally integrated moving average (ARFIMA model with a wavelet transform used as a method of deseasonalization is examined. It is analysed, whether applying wavelets in order to model the seasonal component in a hydrological time series, is an alternative to moving average deseasonalization in combination with an ARFIMA model. The one-to-ten-steps-ahead forecasting performance of this model is compared with two other models, an ARFIMA model with moving average deseasonalization, and a multiresolution wavelet based model. All models are applied to a time series of mean daily discharge exhibiting long range dependence. For one and two day forecasting horizons, the combined wavelet - ARFIMA approach shows a similar performance as the other models tested. However, for longer forecasting horizons, the wavelet deseasonalization - ARFIMA combination outperforms the other two models. The results show that the wavelets provide an attractive alternative to the moving average deseasonalization.
Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
Yuancheng Li
2016-11-01
Full Text Available The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays. Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.
Navigating a Path Toward Operational, Short-term, Ensemble Based, Probablistic Streamflow Forecasts
Hartman, R. K.; Schaake, J.
2004-12-01
The National Weather Service (NWS) has federal responsibility for issuing public flood warnings in the United States. Additionally, the NWS has been engaged in longer range water resources forecasts for many years, particularly in the Western U.S. In the past twenty years, longer range forecasts have increasingly incorporated ensemble techniques. Ensemble techniques are attractive because they allow a great deal of flexibility, both temporally and in content. This technique also provides for the influence of additional forcings (i.e. ENSO), through either pre or post processing techniques. More recently, attention has turned to the use of ensemble techniques in the short-term streamflow forecasting process. While considerably more difficult, the development of reliable short-term probabilistic streamflow forecasts has clear application and value for many NWS customers and partners. During flood episodes, expensive mitigation actions are initialed or withheld and critical reservoir management decisions are made in the absence of uncertainty and risk information. Limited emergency services resources and the optimal use of water resources facilities necessitates the development of a risk-based decision making process. The development of reliable short-term probabilistic streamflow forecasts are an essential ingredient in the decision making process. This paper addresses the utility of short-term ensemble streamflow forecasts and the considerations that must be addressed as techniques and operational capabilities are developed. Verification and validation information are discussed from both a scientific and customer perspective. Education and training related to the interpretation and use of ensemble products are also addressed.
Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game
Arnal, Louise; Ramos, Maria-Helena; Coughlan de Perez, Erin; Cloke, Hannah Louise; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk Jan; Pappenberger, Florian
2016-08-01
Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called "How much are you prepared to pay for a forecast?". The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydro-meteorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.
Evaluation of impact of earthquake on agriculture in Nepal based on remote sensing
Sekiyama, Ayako; Shimada, Sawahiko; Okazawa, Hiromu; Mihara, Machito; Kuo, Kuang Ting
2016-07-01
The big earthquake happening on April, 2015 killed over than 8000 people in Nepal. The effect of earthquake not only affected safety of local people but also agricultural field. Agricultural economy dominates income of local people. Therefore, restoration of agricultural areas are required for improving life of local people. However, lack of information about agricultural areas is main problem for local government to assess and restore damaged agricultural areas. Remote sensing was applied for accessing damaged agricultural field due to its advantages in observing responds of environment without temporal and spatial restriction. Accordingly, the objective of the study is to evaluate impact of earthquake on agriculture in Nepal based on remote sensing. The experimental procedure includes conducting the impact of earthquake on changes of total agricultural area, and analysis of response of greenness affected by earthquake in agricultural land. For conducting agricultural land changes, land use map was first created and classified into four categories: road, city, forest, and agricultural land. Changes before and after earthquake in total area of agricultural land were analyzed by GIS. Moreover, vegetation index was used as indicator for evaluating greenness responds in agricultural land and computed based on high-resolution satellite imagery such as World view-3. Finally, the conclusion of the study and suggestions will be made and provided for helping local government in Nepal restore agricultural areas.
A feature fusion based forecasting model for financial time series.
Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie
2014-01-01
Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models.
A feature fusion based forecasting model for financial time series.
Zhiqiang Guo
Full Text Available Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models.
Dust forecast over North Africa: verification with satellite and ground based observations
Singh, Aditi; Kumar, Sumit; George, John P.
2016-05-01
Arid regions of North Africa are considered as one of the major dust source. Present study focuses on the forecast of aerosol optical depth (AOD) of dust over different regions of North Africa. NCMRWF Unified Model (NCUM) produces dust AOD forecasts at different wavelengths with lead time upto 240 hr, based on 00UTC initial conditions. Model forecast of dust AOD at 550 nm up to 72 hr forecast, based on different initial conditions are verified against satellite and ground based observations of total AOD during May-June 2014 with the assumption that except dust, presence of all other aerosols type are negligible. Location specific and geographical distribution of dust AOD forecast is verified against Aerosol Robotic Network (AERONET) station observations of total and coarse mode AOD. Moderate Resolution Imaging Spectroradiometer (MODIS) dark target and deep blue merged level 3 total aerosol optical depth (AOD) at 550 nm and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrieved dust AOD at 532 nm are also used for verification. CALIOP dust AOD was obtained by vertical integration of aerosol extinction coefficient at 532 nm from the aerosol profile level 2 products. It is found that at all the selected AERONET stations, the trend in dust AODs is well predicted by NCUM up to three days advance. Good correlation, with consistently low bias (~ +/-0.06) and RMSE (~ 0.2) values, is found between model forecasts and point measurements of AERONET, except over one location Cinzana (Mali). Model forecast consistently overestimated the dust AOD compared to CALIOP dust AOD, with a bias of 0.25 and RMSE of 0.40.
A new grey forecasting model based on BP neural network and Markov chain
无
2007-01-01
A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model(GM(1,1)). Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system's known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model (GNNM(1,1)) was built. In order to reduce stochastic phenomenon in GNNM(1,1), the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model(MNNGM(1,1)) was proposed by combining Markov chain with GNNM(1,1). Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM(1,1) and GNNM(1,1) with the proposed model, the results indicate that the absolute mean error of MNNGM(1,1) is about 0.4 times of GNNM(1,1) and 0.2 times of GM(1,1), and the mean square error of MNNGM(1,1) is about 0.25 times of GNNM(1,1) and 0.1 times of GM(1,1).
On the comparison between physics-based numerical simulations and observations from real earthquakes
Smerzini, Chiara; Paolucci, Roberto; Pitilakis, Kyriazis
2016-04-01
Physics-based numerical simulations of earthquake ground motion, including a full 3D seismic wave propagation model from the source to the site, are expected to become, in near future, the most promising tool to generate ground shaking scenarios from future realistic earthquakes. These simulation methods are, in fact, able to model within a single computational domain all factors that affect earthquake ground motion, i.e.: the features of the seismic fault rupture, the propagation path in heterogeneous Earth media, directivity of seismic waves, complex site effects due to localized topographic and geologic irregularities, variability/specificity of soil properties at a regional and local scale. Stimulated by the increasing availability of computational resources, such sophisticated tools are now mature enough to provide realistic estimates of earthquake ground motion in a variety of geomorphological conditions and to favor a deeper understanding of the effect of the main physical parameters on ground shaking and on its spatial variability. Nevertheless, to be accepted and used by the engineering community as an alternative tool to standard empirical approaches (i.e., Ground Motion Prediction Equations) and within a Probabilistic Seismic Hazard Assessment (PSHA) framework, physics-based numerical simulations still need further validation studies, i.e. to compare with observations from real earthquakes. In this contribution, we summarize the experience and the most salient results of the 3D numerical modelling work carried out by a high-performance spectral element code, SPEED (http://speed.mox.polimi.it/), developed at Politecnico di Milano, to simulate real earthquakes which occurred in Europe. Specifically, the following case studies will be presented: the May 29 2012 MW 6.0 Po-Plain earthquake, Northeastern Italy; the April 6 2009 MW 6.3 L'Aquila earthquake, Central Italy; the June 20 1978 MW 6.5 Volvi earthquake, Northeastern Greece. In the discussion of the
Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab
2017-04-01
Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53
Li Ping; Tao Xiaxin
2009-01-01
This paper presents a method to integrate remote sensing (RS) data processing, generation of isoseismal lines and human-computer interaction modules into an improved GIS-based disaster reduction system. Using the RS data processing module, a statistical sample gray value and the RS-intensity at each field survey point in the region are calculated from the probabilistic relationship between the RS-variable and earthquake intensity, and stored in the G1S-based system database. Then, isoseismal lines are generated by a trend surface model from RS-intensity. They are further improved via modification of the isoseismal lines based on the empirical attenuation relationship calculated by using the RS-variable n the human-computer interaction module. The field survey shows that the proposed method gives a good generation of isoseismic lines. As a result, the accuracy of the damage and loss evaluation and the efficiency of the emergency decision making capability are improved.
Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar
2017-02-01
Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.
FOGCAST: Probabilistic fog forecasting based on operational (high-resolution) NWP models
Masbou, M.; Hacker, M.; Bentzien, S.
2013-12-01
The presence of fog and low clouds in the lower atmosphere can have a critical impact on both airborne and ground transports and is often connected with serious accidents. The improvement of localization, duration and variations in visibility therefore holds an immense operational value. Fog is generally a small scale phenomenon and mostly affected by local advective transport, radiation, turbulent mixing at the surface as well as its microphysical structure. Sophisticated three-dimensional fog models, based on advanced microphysical parameterization schemes and high vertical resolution, have been already developed and give promising results. Nevertheless, the computational time is beyond the range of an operational setup. Therefore, mesoscale numerical weather prediction models are generally used for forecasting all kinds of weather situations. In spite of numerous improvements, a large uncertainty of small scale weather events inherent in deterministic prediction cannot be evaluated adequately. Probabilistic guidance is necessary to assess these uncertainties and give reliable forecasts. In this study, fog forecasts are obtained by a diagnosis scheme similar to Fog Stability Index (FSI) based on COSMO-DE model outputs. COSMO-DE I the German-focused high-resolution operational weather prediction model of the German Meteorological Service. The FSI and the respective fog occurrence probability is optimized and calibrated with statistical postprocessing in terms of logistic regression. In a second step, the predictor number of the FOGCAST model has been optimized by use of the LASSO-method (Least Absolute Shrinkage and Selection Operator). The results will present objective out-of-sample verification based on the Brier score and is performed for station data over Germany. Furthermore, the probabilistic fog forecast approach, FOGCAST, serves as a benchmark for the evaluation of more sophisticated 3D fog models. Several versions have been set up based on different
Earthquake: Game-based learning for 21st century STEM education
Perkins, Abigail Christine
To play is to learn. A lack of empirical research within game-based learning literature, however, has hindered educational stakeholders to make informed decisions about game-based learning for 21st century STEM education. In this study, I modified a research and development (R&D) process to create a collaborative-competitive educational board game illuminating elements of earthquake engineering. I oriented instruction- and game-design principles around 21st century science education to adapt the R&D process to develop the educational game, Earthquake. As part of the R&D, I evaluated Earthquake for empirical evidence to support the claim that game-play results in student gains in critical thinking, scientific argumentation, metacognitive abilities, and earthquake engineering content knowledge. I developed Earthquake with the aid of eight focus groups with varying levels of expertise in science education research, teaching, administration, and game-design. After developing a functional prototype, I pilot-tested Earthquake with teacher-participants (n=14) who engaged in semi-structured interviews after their game-play. I analyzed teacher interviews with constant comparison methodology. I used teachers' comments and feedback from content knowledge experts to integrate game modifications, implementing results to improve Earthquake. I added player roles, simplified phrasing on cards, and produced an introductory video. I then administered the modified Earthquake game to two groups of high school student-participants (n = 6), who played twice. To seek evidence documenting support for my knowledge claim, I analyzed videotapes of students' game-play using a game-based learning checklist. My assessment of learning gains revealed increases in all categories of students' performance: critical thinking, metacognition, scientific argumentation, and earthquake engineering content knowledge acquisition. Players in both student-groups improved mostly in critical thinking, having
Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati; Pal, P. K.
2016-10-01
An extended range tropical cyclogenesis forecast model has been developed using the forecasts of global models available from TIGGE portal. A scheme has been developed to detect the signatures of cyclogenesis in the global model forecast fields [i.e., the mean sea level pressure and surface winds (10 m horizontal winds)]. For this, a wind matching index was determined between the synthetic cyclonic wind fields and the forecast wind fields. The thresholds of 0.4 for wind matching index and 1005 hpa for pressure were determined to detect the cyclonic systems. These detected cyclonic systems in the study region are classified into different cyclone categories based on their intensity (maximum wind speed). The forecasts of up to 15 days from three global models viz., ECMWF, NCEP and UKMO have been used to predict cyclogenesis based on multi-model ensemble approach. The occurrence of cyclonic events of different categories in all the forecast steps in the grided region (10 × 10 km2) was used to estimate the probability of the formation of cyclogenesis. The probability of cyclogenesis was estimated by computing the grid score using the wind matching index by each model and at each forecast step and convolving it with Gaussian filter. The proposed method is used to predict the cyclogenesis of five named tropical cyclones formed during the year 2013 in the north Indian Ocean. The 6-8 days advance cyclogenesis of theses systems were predicted using the above approach. The mean lead prediction time for the cyclogenesis event of the proposed model has been found as 7 days.
Analysis of natural mineral earthquake and blast based on Hilbert-Huang transform (HHT)
Li, Xuelong; Li, Zhonghui; Wang, Enyuan; Feng, Junjun; Kong, Xiangguo; Chen, Liang; Li, Baolin; Li, Nan
2016-05-01
There are important theoretical and scientific benefits to identify natural mineral earthquake and blast accurately to ensure the safety of mining. In the paper, we studied the wave characteristics of natural mineral earthquake and blast in a coal mine based on the Hilbert-Huang transform (HHT) method. Results show that the dominant frequency of natural mineral earthquake wave is 20 Hz, which is lower than the other frequency bands. The blast wave frequency is relatively complex and its dominant frequency is 140 Hz, which is higher than the other frequency bands. The natural mineral earthquake wave amplitude is 50 mV and the blast signal amplitude reaches up to 250 mV. However, the decay rate of natural mineral earthquake wave is slower than the blast wave. Both of them could be decomposed into 9 intrinsic mode functions (IMFs) by empirical mode decomposition (EMD). c2, c3, c4 and c5 are the main part of the natural mineral earthquake wave; while c2, c3, and c4 are the main part of the blast wave. These IMFs contain most of the signal energy and belong to the advantage part of the original signal. Besides, the instantaneous energy duration of natural mineral earthquake wave is longer, its peak energy arrival time is earlier and decay rate is slower, while the value is lower. The natural mineral earthquake wave Hilbert energy distributes in the sampling points 600-1200, frequency less than 50 Hz, and the energy peak 100 is at 25 Hz. By contrast, the blast wave Hilbert energy is concentrated on the sampling points 600-800, frequency around 50 Hz and 140 Hz, and the energy peak 170 is at 140 Hz.
Yong Ye
2017-01-01
Full Text Available The allocation of rescue resources after an earthquake has become a popular research topic in the field of emergency management. The allocation of first-aid medicine for earthquake rescue has stronger time sensitivity than that of general rescue materials. This study focuses on the problem of first-aid medicine allocation in earthquake response. First, we consider the incompleteness and renewal of decision information in an emergency environment, as well as the balance between the risk of decision error and delay. Second, we propose an equilibrium decision method for the allocation of first-aid medicine in earthquake rescue based on information update. This method attempts to realize a fair allocation to all disaster places and minimize total transport time loss. Third, a simulation analysis is performed in which the proposed method is applied to the first-aid medicine allocation problem in the Wenchuan earthquake response. Results show that the method can be used to create a good allocation plan in an earthquake rescue situation.
Frequency spectrum method-based stress analysis for oil pipelines in earthquake disaster areas.
Xiaonan Wu
Full Text Available When a long distance oil pipeline crosses an earthquake disaster area, inertial force and strong ground motion can cause the pipeline stress to exceed the failure limit, resulting in bending and deformation failure. To date, researchers have performed limited safety analyses of oil pipelines in earthquake disaster areas that include stress analysis. Therefore, using the spectrum method and theory of one-dimensional beam units, CAESAR II is used to perform a dynamic earthquake analysis for an oil pipeline in the XX earthquake disaster area. This software is used to determine if the displacement and stress of the pipeline meet the standards when subjected to a strong earthquake. After performing the numerical analysis, the primary seismic action axial, longitudinal and horizontal displacement directions and the critical section of the pipeline can be located. Feasible project enhancement suggestions based on the analysis results are proposed. The designer is able to utilize this stress analysis method to perform an ultimate design for an oil pipeline in earthquake disaster areas; therefore, improving the safe operation of the pipeline.
Ozcep, T.; Ozcep, F.
2012-04-01
Natural disaster reduction focuses on the urgent need for prevention activities to reduce loss of life, damage to property, infrastructure and environment, and the social and economic disruption caused by natural hazards. One of the most important factors in reduction of the potential damage of earthquakes is trained manpower. To understanding the causes of earthquakes and other natural phenomena (landslides, avalanches, floods, volcanoes, etc.) is one of the pre-conditions to show a conscious behavior. The aim of the study is to analysis and to investigate, how earthquakes and other natural phenomena are perceived by the students and the possible consequences of this perception, and their effects of reducing earthquake damage. One of the crucial questions is that is our education system fear or curiosity based education system? Effects of the damages due to earthquakes have led to look like a fear subject. In fact, due to the results of the effects, the earthquakes are perceived scary phenomena. In the first stage of the project, the learning (or perception) levels of earthquakes and other natural disasters for the students of primary school are investigated with a survey. Aim of this survey study of earthquakes and other natural phenomena is that have the students fear based or curiosity based approaching to the earthquakes and other natural events. In the second stage of the project, the path obtained by the survey are evaluated with the statistical point of approach. A questionnaire associated with earthquakes and natural disasters are applied to primary school students (that total number of them is approximately 700 pupils) to measure the curiosity and/or fear levels. The questionnaire consists of 17 questions related to natural disasters. The questions are: "What is the Earthquake ?", "What is power behind earthquake?", "What is the mental response during the earthquake ?", "Did we take lesson from earthquake's results ?", "Are you afraid of earthquake
THE PROGRESS OF FORECAST RESEARCH ON IMPENDING VIOLENT EARTHQUAKES%大地震临震预测的研究进展
任振球; 李均之; 曾小苹
2001-01-01
Short-term and impending earthquake predictions are still well known hard challenges in the world. This paper summarizes the study progress on the impending earthquake prediction by Chinese scientists from the method of multi-subject intersection and the internal and external factors coupling. Depending on very hard work during recent years, they get some important signals of predicting impending earthquakes, including the anomalies of infrasonic sound wave, crustal stress, behavior of budgerigars, general geoelectric, geomagnetic, satellite heat infrared and high frequency gravity pulsation. The authors have discovered an important triggering factor of a violent earthquakes—the abnormal superimposition of tide-generating force resonance from typical analysis and compared all astronomical and geophysical factors. Based on these, the examinational immediate prediction has been carried out by combining the tide-generating force resonance with the impending signals for last six years.The results show that the successful ratio of three basic elements of earthquake prediction accounts for 40% . At last, the authors give some views on the impending violent earthquake problem.%短临地震预报尤其是临震预报，是当今国内外公认的世界性科学难点。文章综述了中国科学家从多学科交叉和内外因耦合的科学途径，已在大地震临震信号方面获得了次声波异常、地应力突跳、虎皮鹦鹉跳跃异常、地电和地磁异常、卫星红外异常、重力高频脉冲等临震预测的手段。笔者通过典型分析和普查对比各种地球物理因子和各种天文因子可能影响的整体研究，找到了大地震临震的主要触发因子——以月亮为主的非经典引潮力共振的异常叠加。在此基础上，采取内外因耦合的途径和方法，在6 a多来联合进行的临震预测内部试验表明，地震三要素均基本正确的成功率已达40%。最后，还对临震预报问题提出了一些看法。
Interchanges Safety: Forecast Model Based on ISAT Algorithm
Sascia Canale
2013-09-01
Full Text Available The ISAT algorithm (Interchange Safety Analysis Tool, developed by the Federal Highway Administration (FHWA, provides design and safety engineers with an automated tool for assessing the safety effects of geometric design and traffic control features at an existing interchange and adjacent roadway network. Concerning the default calibration coefficients and crash distributions by severity and type, the user should modify these default values to more accurately reflect the safety experience of their local/State agency prior to using ISAT to perform actual safety assessments. This paper will present the calibration process of the FHWA algorithm to the local situation of Oriental Sicily. The aim is to realize an instrument for accident forecast analyses, useful to Highway Managers, in order to individuate those infrastructural elements that can contribute to improve the safety level of interchange areas, if suitably calibrated.
Stock prices forecasting based on wavelet neural networks with PSO
Wang Kai-Cheng
2017-01-01
Full Text Available This research examines the forecasting performance of wavelet neural network (WNN model using published stock data obtained from Financial Times Stock Exchange (FTSE Taiwan Stock Exchange (TWSE 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX, hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO to choose the appropriate initial network values for different companies. The findings come with two advantages. First, the network initial values are automatically selected instead of being a constant. Second, threshold and training data percentage become constant values, because PSO assists with self-adjustment. We can achieve a success rate over 73% without the necessity to manually adjust parameter or create another math model.
Forecasting China's natural gas consumption based on a combination model
Gang Xu; Weiguo Wang
2010-01-01
Ensuring a sufficient energy supply is essential to a country.Natural gas constitutes a vital part in energy supply and therefore forecasting natural gas consumption reliably and accurately is an essential part of a country's energy policy.Over the years,studies have shown that a combinative model gives better projected results compared to a single model.In this study,we used Polynomial Curve and Moving Average Combination Projection (PCMACP) model to estimate the future natural gas consumption in China from 2009 to 2015.The new proposed PCMACP model shows more reliable and accurate results:its Mean Absolute Percentage Error (MAPE) is less than those of any previous models within the investigated range.According to the PCMACP model,the average annual growth rate will increase for the next 7 years and the amount of natural gas consumption will reach 171600 million cubic meters in 2015 in China.
West, W. L., III (Principal Investigator)
1981-01-01
The content, format, and storage of data bases developed for the Foreign Commodity Production Forecasting project and used to produce normal crop calendars are described. In addition, the data bases may be used for agricultural meteorology, modeling of stage sequences and planting dates, and as indicators of possible drought and famine.
ZHOU Rong-yi; LIU Ai-qun; LI Shu-qing
2007-01-01
Directing at the non-linear dynamic characteristics of water inrush from coal seam floor and by the analysis of the shortages of current forecast methods for water inrush from coal seam floor,a new forecast method was raised based on wavelet neural network(WNN)that was a model combining wavelet function with artificiaI neural network.Firstly basic principle of WNN was described.then a forecast model for water inrush from coal seam floor based on WNN was established and analyzed,finally an example of forecasting the quantity of water inrush from coal floor was illustrated to verify the feasibility and superiority of this method.Conclusions show that the forecast result based on WNN is more precise and that using WNN model to forecast the quantity of water inrush from coal seam floor is feasible and practical.
Operational perspective of remote sensing-based forest fire danger forecasting systems
Chowdhury, Ehsan H.; Hassan, Quazi K.
2015-06-01
Forest fire is a natural phenomenon in many ecosystems across the world. One of the most important components of forest fire management is the forecasting of fire danger conditions. Here, our aim was to critically analyse the following issues, (i) current operational forest fire danger forecasting systems and their limitations; (ii) remote sensing-based fire danger monitoring systems and usefulness in operational perspective; (iii) remote sensing-based fire danger forecasting systems and their functional implications; and (iv) synergy between operational forecasting systems and remote sensing-based methods. In general, the operational systems use point-based measurements of meteorological variables (e.g., temperature, wind speed and direction, relative humidity, precipitations, cloudiness, solar radiation, etc.) and generate danger maps upon employing interpolation techniques. Theoretically, it is possible to overcome the uncertainty associated with the interpolation techniques by using remote sensing data. During the last several decades, efforts were given to develop fire danger condition systems, which could be broadly classified into two major groups: fire danger monitoring and forecasting systems. Most of the monitoring systems focused on determining the danger during and/or after the period of image acquisition. A limited number of studies were conducted to forecast fire danger conditions, which could be adaptable. Synergy between the operational systems and remote sensing-based methods were investigated in the past but too much complex in nature. Thus, the elaborated understanding about these developments would be worthwhile to advance research in the area of fire danger in the context of making them operational.
Artificial Neural Network Based Model for Forecasting of Inflation in India
Gour Sundar Mitra Thakur
2016-03-01
Full Text Available Inflation can be attributed to both microeconomic and macroeconomic factors which influence the stability of the economy of any nation. With the raising of recession at the end of the year 2008, world communities started paying much contemplation on inflation and put enormous hard work to predict it accurately. Prediction of inflation is not a simple task. Moreover, the behavior of inflation is so complex and uncertain that both economists and statisticians have been striving to model and forecast inflation in an accurate way. As a result, many researchers have proposed inflation forecasting models based on different methods; however the accuracy is always being a major constraint. In this paper, we have analyzed the historical monthly economic data of India between January 2000 and December 2012 and constructed an inflation forecasting model based on feed forward back propagation neural network. Initially some critical factors that can considerably influence the inflation of India have been identified, then an efficient artificial neural network (ANN model has been proposed to forecast the inflation. Accuracy of the model is proved to be satisfactory when compared with the forecasting of some well-known agencies.
Short-term and long-term earthquake occurrence models for Italy: ETES, ERS and LTST
Maura Murru
2010-11-01
Full Text Available This study describes three earthquake occurrence models as applied to the whole Italian territory, to assess the occurrence probabilities of future (M ≥5.0 earthquakes: two as short-term (24 hour models, and one as long-term (5 and 10 years. The first model for short-term forecasts is a purely stochastic epidemic type earthquake sequence (ETES model. The second short-term model is an epidemic rate-state (ERS forecast based on a model that is physically constrained by the application to the earthquake clustering of the Dieterich rate-state constitutive law. The third forecast is based on a long-term stress transfer (LTST model that considers the perturbations of earthquake probability for interacting faults by static Coulomb stress changes. These models have been submitted to the Collaboratory for the Study of Earthquake Predictability (CSEP for forecast testing for Italy (ETH-Zurich, and they were locked down to test their validity on real data in a future setting starting from August 1, 2009.
Jingwei Song
2014-01-01
Full Text Available A simulated annealing (SA based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN, and partial least square support vector machine (PLS-SVM to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model, 12.93% (ANN, and 12.94% (PLS-SVM to 9.38%. Five-week average has been raised from 13.02% (chaotic model, 15.69% (ANN, and 15.92% (PLS-SVM to 11.27%.
Remote-sensing based approach to forecast habitat quality under climate change scenarios
Requena-Mullor, Juan M.; López, Enrique; Castro, Antonio J.; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier
2017-01-01
As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological
Remote-sensing based approach to forecast habitat quality under climate change scenarios.
Requena-Mullor, Juan M; López, Enrique; Castro, Antonio J; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier
2017-01-01
As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological
Forecasting the High Energy Electron Radiation Belts Using Physics Based Models
Horne, R. B.
2012-12-01
Wave-particle interactions waves play an important role in the loss and acceleration of electrons in the radiation belts. Here we present results from the SPACECAST project to forecast the high energy electron radiation belts using physics based models in the UK and France. The forecasting models include wave-particle interactions, radial diffusion, and losses by Coulomb collisions, and highlight the importance of various types of wave-particle interactions. The system is driven by a time series of the Kp index derived from solar wind data and ground based magnetometers and provides a forecast of the radiation belts up to 3 hours ahead, updated every hour. We show that during the storm of 8-9 March, 2012 the forecasts were able to reproduce the electron flux at geostationary orbit measured by GOES 13 to within a factor of two initially, and to within a factor of 10 later on during the event. By including wave-particle interactions between L* = 6.5 and 8 the forecast of the electron flux at geostationary orbit was significantly improved for the month of March 2012. We show examples of particle injection into the slot region, and relativistic flux drop-outs and suggest that flux drop outs are more likely to be associated with magnetopause motion than losses due to wave-particle interactions. To improve the forecasts we have developed a new database of whistler mode chorus waves from 5 different satellite missions. We present data on the power spectra of the waves as a function of magnetic local time, latitude and radial distance, and present pitch angle and energy diffusion coefficients for use in global models. We show that waves at different latitudes result in structure in the diffusion rates and we illustrate the effects on the trapped electron flux. We present forecasting skill scores which show quantitatively that including wave-particle interactions improves our ability to forecast the high energy electron radiation belt. Finally we suggest several areas where
Urban MEMS based seismic network for post-earthquakes rapid disaster assessment
D'Alessandro, Antonino; Luzio, Dario; D'Anna, Giuseppe
2014-05-01
worship. The waveforms recorded could be promptly used to determine ground-shaking parameters, like peak ground acceleration/velocity/displacement, Arias and Housner intensity, that could be all used to create, few seconds after a strong earthquakes, shaking maps at urban scale. These shaking maps could allow to quickly identify areas of the town center that have had the greatest earthquake resentment. When a strong seismic event occur, the beginning of the ground motion observed at the site could be used to predict the ensuing ground motion at the same site and so to realize a short term earthquake early warning system. The data acquired after a moderate magnitude earthquake, would provide valuable information for the detail seismic microzonation of the area based on direct earthquake shaking observations rather than from a model-based or indirect methods. In this work, we evaluate the feasibility and effectiveness of such seismic network taking in to account both technological, scientific and economic issues. For this purpose, we have simulated the creation of a MEMS based urban seismic network in a medium size city. For the selected town, taking into account the instrumental specifics, the array geometry and the environmental noise, we investigated the ability of the planned network to detect and measure earthquakes of different magnitude generated from realistic near seismogentic sources.
Advances in Business and Management Forecasting
Lawrence, Kenneth D
2011-01-01
The topics within Advances in Business and Management Forecasting will normally include sales and marketing, forecasting, new product forecasting, judgmentally-based forecasting, the application of surveys to forecasting, forecasting for strategic business decisions, improvements in forecasting accuracy, and sales response models.
Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA
Watson, Leela R.; Bauman, William H., III
2008-01-01
NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.
A space weather forecasting system with multiple satellites based on a self-recognizing network.
Tokumitsu, Masahiro; Ishida, Yoshiteru
2014-05-05
This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron ﬂux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic ﬁeld and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron ﬂux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.
Forecasting DNI and GHI based on the WRF model. An evaluation study in Andalusia (Southern Spain)
Lara Fanego, Vicente; Ruiz Arias, Jose Antonio; Pozo Vazquez, Antonio David; Santos Alamillos, Francisco Javier; Tovar Pescador, Joaquin [Jaen Univ. (Spain). Dept. of Physics; Quesada Ruiz, Samuel [Jaen Univ. (Spain). Dept. of Computer Engineering
2011-07-01
In this work, we evaluate the reliability of GHI and DNI forecast based on the WRF mesoscale atmospheric model in Andalusia (Southern Spain). Particularly, the role of the spatial resolution of the model set up and the use of a spatial-averaging post-processing step was analyzed. To this end, a set of two-days-ahead one-year-length integrations, with different spatial resolutions (1, 3, 9 and 27 km) were evaluated. Results showed, firstly, that an increment in the spatial resolution does not enhance the reliability of the model forecasts, except under clear sky conditions. Secondly, that, in general, an spatial averaging of the solar forecasts corresponding to the grid points surrounding the location of interest provides a notable improvement in the forecasting skills. The most significant improvement is found when forecasts corresponding to an area of about 100 by 100 km are averaged. The role of the WRF model cloud representation in the former results is discussed. (orig.)
Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand
Omitaomu, Olufemi A [ORNL; Li, Xueping [University of Tennessee, Knoxville (UTK); Zhou, Shengchao [University of Tennessee, Knoxville (UTK)
2015-01-01
The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.
A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network
Masahiro Tokumitsu
2014-05-01
Full Text Available This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron ﬂux (>2 MeV. The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic ﬁeld and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron ﬂux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.
Lin, Kun-Hsiang; Tseng, Hung-Wei; Kuo, Chen-Min; Yang, Tao-Chang; Yu, Pao-Shan
2016-04-01
Typhoons with heavy rainfall and strong wind often cause severe floods and losses in Taiwan, which motivates the development of rainfall forecasting models as part of an early warning system. Thus, this study aims to develop rainfall forecasting models based on two machine learning methods, support vector machines (SVMs) and random forests (RFs), and investigate the performances of the models with different predictor sets for searching the optimal predictor set in forecasting. Four predictor sets were used: (1) antecedent rainfalls, (2) antecedent rainfalls and typhoon characteristics, (3) antecedent rainfalls and meteorological factors, and (4) antecedent rainfalls, typhoon characteristics and meteorological factors to construct for 1- to 6-hour ahead rainfall forecasting. An application to three rainfall stations in Yilan River basin, northeastern Taiwan, was conducted. Firstly, the performance of the SVMs-based forecasting model with predictor set #1 was analyzed. The results show that the accuracy of the models for 2- to 6-hour ahead forecasting decrease rapidly as compared to the accuracy of the model for 1-hour ahead forecasting which is acceptable. For improving the model performance, each predictor set was further examined in the SVMs-based forecasting model. The results reveal that the SVMs-based model using predictor set #4 as input variables performs better than the other sets and a significant improvement of model performance is found especially for the long lead time forecasting. Lastly, the performance of the SVMs-based model using predictor set #4 as input variables was compared with the performance of the RFs-based model using predictor set #4 as input variables. It is found that the RFs-based model is superior to the SVMs-based model in hourly typhoon rainfall forecasting. Keywords: hourly typhoon rainfall forecasting, predictor selection, support vector machines, random forests
A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain
Francesca Gagliardi
2017-07-01
Full Text Available This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods, were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.
Brocher, Thomas M.; Blakely, Richard J.; Sherrod, Brian
2017-01-01
We investigate spatial and temporal relations between an ongoing and prolific seismicity cluster in central Washington, near Entiat, and the 14 December 1872 Entiat earthquake, the largest historic crustal earthquake in Washington. A fault scarp produced by the 1872 earthquake lies within the Entiat cluster; the locations and areas of both the cluster and the estimated 1872 rupture surface are comparable. Seismic intensities and the 1–2 m of coseismic displacement suggest a magnitude range between 6.5 and 7.0 for the 1872 earthquake. Aftershock forecast models for (1) the first several hours following the 1872 earthquake, (2) the largest felt earthquakes from 1900 to 1974, and (3) the seismicity within the Entiat cluster from 1976 through 2016 are also consistent with this magnitude range. Based on this aftershock modeling, most of the current seismicity in the Entiat cluster could represent aftershocks of the 1872 earthquake. Other earthquakes, especially those with long recurrence intervals, have long‐lived aftershock sequences, including the Mw">MwMw 7.5 1891 Nobi earthquake in Japan, with aftershocks continuing 100 yrs after the mainshock. Although we do not rule out ongoing tectonic deformation in this region, a long‐lived aftershock sequence can account for these observations.
Forecasting Low-Visibility Conditions at Vienna Airport with Tree-Based Statistical Models
Dietz, Sebastian; Kneringer, Philipp; Mayr, Georg J.; Zeileis, Achim
2016-04-01
Low visibility conditions at airports can lead to capacity problems and therefore to delays or cancelation of arriving and departing airplanes. To keep the capacity as high as possible, accurate visibility forecasts are required. Therefore tree-based statistical nowcasting models were developed, which split the data in the sense of decision rules by recursive partitioning. Benefits of this models are fast update cycles and low computation times. Highly-resolved meteorological observation data at the airport form the large pool of input variables for the models. In this study we identify the most important predictors for different lead times to create the most accurate forecasts.
Cheng Yugui
2013-07-01
Full Text Available A kind of power forecast model combined cellular genetic algorithm with BP neural network was established in this article. Mid-long term power demand in urban areas was done load forecasting and analysis based on material object of the actual power consumption in urban areas of Nanchang. The results show that this method has the characteristic of the minimum training times, the shortest consumption time, the minimum error and the shortest operation time to obtain the best fitting effect.
Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine
Yiming Xing
2017-01-01
Full Text Available Real-time and accurate prediction of traffic flow is the key to intelligent transportation systems (ITS. However, due to the nonstationarity of traffic flow data, traditional point forecasting can hardly be accurate, so probabilistic forecasting methods are essential for quantification of the potential risks and uncertainties for traffic management. A probabilistic forecasting model of traffic flow based on a multikernel extreme learning machine (MKELM is proposed. Moreover, the optimal output weights of MKELM are obtained by utilizing Quantum-behaved particle swarm optimization (QPSO algorithm. To verify its effectiveness, traffic flow probabilistic prediction using QPSO-MKELM was compared with other learning methods. Experimental results show that QPSO-MKELM is more effective for practical applications. And it will help traffic managers to make right decisions.
Neural network forecasting model based on phase space re-construction in water yield of mine
LIU Wei-lin; DONG Zeng-chuan; CHEN Nan-xiang; CAO Lian-hai
2007-01-01
The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example,the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.
Modeling and computing of stock index forecasting based on neural network and Markov chain.
Dai, Yonghui; Han, Dongmei; Dai, Weihui
2014-01-01
The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.
牛东晓; 刘达; 邢棉
2008-01-01
A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.
Optimal Planning Strategy for Large PV/Battery System Based on Long-Term Insolation Forecasting
Yona, Atsushi; Uchida, Kosuke; Senjyu, Tomonobu; Funabashi, Toshihisa
Photovoltaic (PV) systems are rapidly gaining acceptance as some of the best alternative energy sources. Usually the power output of PV system fluctuates depending on weather conditions. In order to control the fluctuating power output for PV system, it requires control method of energy storage system. This paper proposes an optimization approach to determine the operational planning of power output for PV system with battery energy storage system (BESS). This approach aims to obtain more benefit for electrical power selling and to smooth the fluctuating power output for PV system. The optimization method applies genetic algorithm (GA) considering PV power output forecast error. The forecast error is based on our previous works with the insolation forecasting at one day ahead by using weather reported data, fuzzy theory and neural network(NN). The validity of the proposed method is confirmed by the computer simulations.
Cloud based N-dimensional weather forecast visualization tool with image analysis capabilities
Laka-Iñurrategi, M.; Alberdi, I.; Alonso, K.; Quartulli, M.
2013-10-01
Until recently the majority of data analysis and visualization tools were desktop applications that demanded high requirement hardware to carry out those processes. However, nowadays there is a trend to evolve this kind of applications to service based solutions that can be accessed remotely. Considering the implications that the weather has in the health and the safety of the human beings, authorities require a further knowledge of the weather forecasts and their impacts but they have difficulties to properly understand the raw forecasts since they usually are not experts in the field of meteorology. For this purpose, we have designed and implemented a framework that permits a remote access to weather forecasts. With this tool, the practitioners can access, visualise and interact with the data from a web browser. Furthermore, it contains an image and numeric analysis module that permits the generation of new information what is helpful in decision making processes.
Product Design Time Forecasting by Kernel-Based Regression with Gaussian Distribution Weights
Zhi-Gen Shang
2016-06-01
Full Text Available There exist problems of small samples and heteroscedastic noise in design time forecasts. To solve them, a kernel-based regression with Gaussian distribution weights (GDW-KR is proposed here. GDW-KR maintains a Gaussian distribution over weight vectors for the regression. It is applied to seek the least informative distribution from those that keep the target value within the confidence interval of the forecast value. GDW-KR inherits the benefits of Gaussian margin machines. By assuming a Gaussian distribution over weight vectors, it could simultaneously offer a point forecast and its confidence interval, thus providing more information about product design time. Our experiments with real examples verify the effectiveness and flexibility of GDW-KR.
Crime Forecasting System (An exploratory web-based approach
Yaseen Ahmed Meenai
2011-08-01
Full Text Available With the continuous rise in crimes in some big cities of the world like Karachi and the increasing complexity of these crimes, the difficulties the law enforcing agencies are facing in tracking down and taking out culprits have increased manifold. To help cut back the crime rate, a Crime Forecasting System (CFS can be used which uses historical information maintained by the local Police to help them predict crime patterns with the support of a huge and self-updating database. This system operates to prevent crime, helps in apprehending criminals, and to reduce disorder. This system is also vital in helping the law enforcers in forming a proactive approach by helping them in identifying early warning signs, take timely and necessary actions, and eventually help stop crime before it actually happens. It will also be beneficial in maintaining an up to date database of criminal suspects includes information on arrest records, communication with police department, associations with other known suspects, and membership in gangs/activist groups. After exploratory analysis of the online data acquired from the victims of these crimes, a broad picture of the scenario can be analyzed. The degree of vulnerability of an area at some particular moment can be highlighted by different colors aided by Google Maps. Some statistical diagrams have also been incorporated. The future of CFS can be seen as an information engine for the analysis, study and prediction of crimes.
Holliday, James R; Rundle, John B; Turcotte, Donald L
2013-01-01
We develop and implement a new type of global earthquake forecast. Our forecast is a perturbation on a smoothed seismicity (Relative Intensity) spatial forecast combined with a temporal time-averaged (Poisson) forecast. A variety of statistical and fault-system models have been discussed for use in computing forecast probabilities. Our paper takes a new approach. The idea is based on the observation that GR statistics characterize seismicity for all space and time. Small magnitude event counts (quake counts) are used as markers for the approach of large events. More specifically, if the GR b-value = 1, then for every 1000 M>3 earthquakes, one expects 1 M>6 earthquake. So if ~1000 M>3 events have occurred in a spatial region since the last M>6 earthquake, another M>6 earthquake should be expected soon. In physics, event count models have been called natural time models, since counts of small events represent a physical or natural time scale characterizing the system dynamics. In a previous paper, we used condi...
Probabilistic precipitation forecasts based on a convection-permitting high-resolution NWP model
Bentzien, S.; Friederichs, P.
2011-12-01
High-resolution limited-area numerical weather prediction (NWP) models are particularly developed in order to predict high-impact weather. Due to their high resolution of a few km and their non-hydrostatic dynamics, they are able to describe mesoscale processes in a more detailed and explicit way. Although high-resolution model forecasts lead to more realistic mesoscale structures, forecasts especially for precipitation are still affected by systematic biases, displacement errors, and fast error growth. Due to the large uncertainties, probabilistic prediction is likely to be the best choice to forecast precipitation. Ensemble predictions systems (EPS) have become the prime instrument to assess the uncertainty in mesoscale NWP. EPS can describe uncertainty due to errors in initial and boundary conditions, or physical parameterizations. However, EPS are unable to account for all sources of uncertainty, and are therefore underdispersive. A statistical postprocessing is necessary in order to obtain calibrated and reliable forecasts. A low-cost ensemble can be generated from high-resolution operational NWP forecasts which are frequently updated by data assimilation. Several successively started operational forecasts that cover a limited common time period build a time-lagged ensemble (TLE) forecasts. TLE come at low costs, are often available for several years and define a suitable baseline in order to assess the benefit of an EPS. We present a statistical postprocessing for precipitation forecast based on the COSMO-DE TLE. The COSMO-DE model has a horizontal grid spacing of 2.8 km and runs operationally at the German meteorological service (Deutscher Wetterdienst, DWD) eight times a day. In order to obtain calibrated probabilistic precipitation forecasts, several semi-parametric and parametric techniques are employed. Semi-parametric approaches like logistic or quantile regression are used to estimate probabilities of threshold exceedance (PoT) and quantiles
A Threshold-Based Earthquake Early-Warning System for Offshore Events in Southern Iberia
Picozzi, M.; Colombelli, S.; Zollo, A.; Carranza, M.; Buforn, E.
2015-09-01
The south of the Iberian Peninsula is situated at the convergence of the Eurasian and African plates. This region experiences large earthquakes with long separation in time, the best known of which was the great 1755 Lisbon Earthquake, which occurred SW of San Vicente Cape (SW Iberian Peninsula). The high risk of damaging earthquakes has recently led Carranza et al. (Geophys. Res. Lett. 40, 2013) to investigate the feasibility of an EEWS in this region. Analysis of the geometry for the Iberian seismic networks and the San Vicente Cape area led the authors to conclude that a threshold-based approach, which would not require real-time location of the earthquake, might be the best option for an EEWS in SW Iberia. In this work we investigate this hypothesis and propose a new EEW approach that extends standard P-wave threshold-based single-station analysis to the whole network. The proposed method enables real-time estimation of the potential damage at stations that are triggered by P-waves and those which are not triggered, with the advantage of greater lead-times for release of alerts. Results of tests made with synthetic data mimicking the scenario of the great 1755 Lisbon Earthquake, and those conducted by applying the new approach to available recordings, indicate that an EEW estimation of the potential damage associated with an event in the San Vicente Cape area can be obtained for a very large part of the Iberian Peninsula.
Knowledge base about earthquakes as a tool to minimize strong events consequences
Frolova, Nina; Bonnin, Jean; Larionov, Valery; Ugarov, Alexander; Kijko, Andrzej
2017-04-01
The paper describes the structure and content of the knowledge base on physical and socio-economical consequences of damaging earthquakes, which may be used for calibration of near real-time loss assessment systems based on simulation models for shaking intensity, damage to buildings and casualties estimates. Such calibration allows to compensate some factors which influence on reliability of expected damage and loss assessment in "emergency" mode. The knowledge base contains the description of past earthquakes' consequences for the area under study. It also includes the current distribution of built environment and population at the time of event occurrence. Computer simulation of the recorded in knowledge base events allow to determine the sets of regional calibration coefficients, including rating of seismological surveys, peculiarities of shaking intensity attenuation and changes in building stock and population distribution, in order to provide minimum error of damaging earthquakes loss estimations in "emergency" mode. References 1. Larionov, V., Frolova, N: Peculiarities of seismic vulnerability estimations. In: Natural Hazards in Russia, volume 6: Natural Risks Assessment and Management, Publishing House "Kruk", Moscow, 120-131, 2003. 2. Frolova, N., Larionov, V., Bonnin, J.: Data Bases Used In Worlwide Systems For Earthquake Loss Estimation In Emergency Mode: Wenchuan Earthquake. In Proc. TIEMS2010 Conference, Beijing, China, 2010. 3. Frolova N. I., Larionov V. I., Bonnin J., Sushchev S. P., Ugarov A. N., Kozlov M. A. Loss Caused by Earthquakes: Rapid Estimates. Natural Hazards Journal of the International Society for the Prevention and Mitigation of Natural Hazards, vol.84, ISSN 0921-030, Nat Hazards DOI 10.1007/s11069-016-2653
Moslem Yousefi
2015-12-01
Full Text Available Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF and partial auto correlation factor (PACF on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurate
Weiser, Deborah Anne
Induced seismicity is occurring at increasing rates around the country. Brodsky and Lajoie (2013) and others have recognized anthropogenic quakes at a few geothermal fields in California. I use three techniques to assess if there are induced earthquakes in California geothermal fields; there are three sites with clear induced seismicity: Brawley, The Geysers, and Salton Sea. Moderate to strong evidence is found at Casa Diablo, Coso, East Mesa, and Susanville. Little to no evidence is found for Heber and Wendel. I develop a set of tools to reduce or cope with the risk imposed by these earthquakes, and also to address uncertainties through simulations. I test if an earthquake catalog may be bounded by an upper magnitude limit. I address whether the earthquake record during pumping time is consistent with the past earthquake record, or if injection can explain all or some of the earthquakes. I also present ways to assess the probability of future earthquake occurrence based on past records. I summarize current legislation for eight states where induced earthquakes are of concern. Unlike tectonic earthquakes, the hazard from induced earthquakes has the potential to be modified. I discuss direct and indirect mitigation practices. I present a framework with scientific and communication techniques for assessing uncertainty, ultimately allowing more informed decisions to be made.
Vogel, Peter; Klar, Manuel; Schlüter, Andreas; Knippertz, Peter; Fink, Andreas H.; Gneiting, Tilmann
2017-04-01
Precipitation forecasts for one up to several days are of high socioeconomic importance for agriculturally dominated societies in West Africa, regarding both the occurrence as well as the amount of precipitation. However, disappointingly forecasts based on numerical weather prediction models and even statistically postprocessed forecasts still do not outperform simple reference forecasts such as climatology or persistence. More elaborate statistical forecasts can hopefully lead to an improvement in the quality of precipitation forecasts above climatological or persistent ones. In this contribution, we concentrate on the potential of statistical forecasts to predict the occurrence of precipitation, while the prediction of the amount will be addressed in the future. Using increasingly sophisticated statistical models, we start with forecasts solely relying on the spatio-temporal information contained in precipitation observations. With the necessity of a full spatial coverage of precipitation observations in order to understand its spatio-temporal properties, we rely on Tropical Rainfall Measuring Mission (TRMM) observations and use accumulation periods of 1 to 5 days for the monsoon seasons from May to mid-October of the years 2007 to 2014. Especially for the full monsoon from the end of June to the end of September, the precipitation fields exhibit clear spatio-temporal information that is meteorologically interpretable and statistically meaningful. Using Markov models, we do in fact find an increased forecast quality for this period. While such forecasts already outperform persistent and climatological forecasts for the full monsoon, the forecast quality increases further and also covers the whole monsoon period from May to mid-October, when we add additional predictors. We find the activity of tropical waves such as Kelvin or African Easterly waves or the Madden-Julian Oscillation to be informative predictors and test for additional predictors closely linked to
Web-based hydrological modeling system for flood forecasting and risk mapping
Wang, Lei; Cheng, Qiuming
2008-10-01
Mechanism of flood forecasting is a complex system, which involves precipitation, drainage characterizes, land use/cover types, ground water and runoff discharge. The application of flood forecasting model require the efficient management of large spatial and temporal datasets, which involves data acquisition, storage, pre-processing and manipulation, analysis and display of model results. The extensive datasets usually involve multiple organizations, but no single organization can collect and maintain all the multidisciplinary data. The possible usage of the available datasets remains limited primarily because of the difficulty associated with combining data from diverse and distributed data sources. Difficulty in linking data, analysis tools and model is one of the barriers to be overcome in developing real-time flood forecasting and risk prediction system. The current revolution in technology and online availability of spatial data, particularly, with the construction of Canadian Geospatial Data Infrastructure (CGDI), a lot of spatial data and information can be accessed in real-time from distributed sources over the Internet to facilitate Canadians' need for information sharing in support of decision-making. This has resulted in research studies demonstrating the suitability of the web as a medium for implementation of flood forecasting and flood risk prediction. Web-based hydrological modeling system can provide the framework within which spatially distributed real-time data accessed remotely to prepare model input files, model calculation and evaluate model results for flood forecasting and flood risk prediction. This paper will develop a prototype web-base hydrological modeling system for on-line flood forecasting and risk mapping in the Oak Ridges Moraine (ORM) area, southern Ontario, Canada, integrating information retrieval, analysis and model analysis for near real time river runoff prediction, flood frequency prediction, flood risk and flood inundation
Likelihood-based scoring rules for comparing density forecasts in tails
Diks, C.; Panchenko, V.; van Dijk, D.
2011-01-01
We propose new scoring rules based on conditional and censored likelihood for assessing the predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. These scoring rules can be interpreted in terms of Kullback-Leibler d
Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support
C.G.H. Diks (Cees); V. Panchenko (Valentyn); O. Sokolinskiy (Oleg); D.J.C. van Dijk (Dick)
2013-01-01
textabstractThis paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample)
Comparing the accuracy of copula-based multivariate density forecasts in selected regions of support
Diks, C.; Panchenko, V.; Sokolinskiy, O.; van Dijk, D.
2013-01-01
This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional
Matasci, G.; Pozdnoukhov, A.; Kanevski, M.
2009-04-01
The recent progress in environmental monitoring technologies allows capturing extensive amount of data that can be used to assist in avalanche forecasting. While it is not straightforward to directly obtain the stability factors with the available technologies, the snow-pack profiles and especially meteorological parameters are becoming more and more available at finer spatial and temporal scales. Being very useful for improving physical modelling, these data are also of particular interest regarding their use involving the contemporary data-driven techniques of machine learning. Such, the use of support vector machine classifier opens ways to discriminate the ``safe'' and ``dangerous'' conditions in the feature space of factors related to avalanche activity based on historical observations. The input space of factors is constructed from the number of direct and indirect snowpack and weather observations pre-processed with heuristic and physical models into a high-dimensional spatially varying vector of input parameters. The particular system presented in this work is implemented for the avalanche-prone site of Ben Nevis, Lochaber region in Scotland. A data-driven model for spatio-temporal avalanche danger forecasting provides an avalanche danger map for this local (5x5 km) region at the resolution of 10m based on weather and avalanche observations made by forecasters on a daily basis at the site. We present the further work aimed at overcoming the ``black-box'' type modelling, a disadvantage the machine learning methods are often criticized for. It explores what the data-driven method of support vector machine has to offer to improve the interpretability of the forecast, uncovers the properties of the developed system with respect to highlighting which are the important features that led to the particular prediction (both in time and space), and presents the analysis of sensitivity of the prediction with respect to the varying input parameters. The purpose of the
无
2010-01-01
This paper aims at constructing an emission source inversion model using a variational processing method and adaptive nudging scheme for the Community Multiscale Air Quality Model (CMAQ) based on satellite data to investigate the applicability of high resolution OMI (Ozone Monitoring Instrument) column concentration data for air quality forecasts over the North China. The results show a reasonable consistency and good correlation between the spatial distributions of NO2 from surface and OMI satellite measurements in both winter and summer. Such OMI products may be used to implement integrated variational analysis based on observation data on the ground. With linear and variational corrections made, the spatial distribution of OMI NO2 clearly revealed more localized distributing characteristics of NO2 concentration. With such information, emission sources in the southwest and southeast of North China are found to have greater impacts on air quality in Beijing. When the retrieved emission source inventory based on high-resolution OMI NO2 data was used, the coupled Weather Research Forecasting CMAQ model (WRF-CMAQ) performed significantly better in forecasting NO2 concentration level and its tendency as reflected by the more consistencies between the NO2 concentrations from surface observation and model result. In conclusion, satellite data are particularly important for simulating NO2 concentrations on urban and street-block scale. High-resolution OMI NO2 data are applicable for inversing NOx emission source inventory, assessing the regional pollution status and pollution control strategy, and improving the model forecasting results on urban scale.
SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting
Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.
2014-12-01
Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.
Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game
Arnal, Louise; Ramos, Maria-Helena; Coughlan, Erin; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk-Jan; Pappenberger, Florian
2016-04-01
Forecast uncertainty is a twofold issue, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic forecasts over deterministic forecasts for a diversity of activities in the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. The setup and the results of a risk-based decision-making experiment, designed as a game on the topic of flood protection mitigation, called ``How much are you prepared to pay for a forecast?'', will be presented. The game was played at several workshops in 2015, including during this session at the EGU conference in 2015, and a total of 129 worksheets were collected and analysed. The aim of this experiment was to contribute to the understanding of the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game showed that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers. Balancing avoided costs and the cost (or the benefit) of having forecasts available for making decisions is not straightforward, even in a simplified game situation, and is a topic that deserves more attention from the hydrological forecasting community in the future.
A fast approach to regional earthquake hazard evaluation based on population statistical data
WEI Fu-quan; CAI Zong-wen; JIAO Shuang-jian; WEI Wei; HUANG Hong-sheng; FU Zai-yang; HUANG Tian-zhou; CHEN Lin
2008-01-01
In the prediction process of large-scale earthquake damage occurred in urban and rural regions, new models and approaches, which are different from traditional ones, should be adopted to rapidly predict earthquake damage. This article utilizes sampled population and buildings data that is easily available from the statistical database to conduct vulnerability analysis of buildings on the basis of earthquake damage of existing urban buildings in an analogical way, so as to provide a relation model between population data and disaster losses. In virtue of this model, the average vulnerability matrix of buildings of different structures in Fujian Province is established, the matrix adjustment coefficient of different decades is developed in accordance with the economic conditions, and the rapid evaluation system is set up as well. The result shows: this evaluation model, based on the population statistical data has merits as small investment, automatic data prediction, regular updates, as well as the advantage of easy accessibility.
EQRM: An open-source event-based earthquake risk modeling program
Robinson, D. J.; Dhu, T.; Row, P.
2007-12-01
Geoscience Australia's Earthquake Risk Model (EQRM) is an event-based tool for earthquake scenario ground motion and scenario loss modeling as well as probabilistic seismic hazard (PSHA) and risk (PSRA) modeling. It has been used to conduct PSHA and PSRA for many of Australia's largest cities and it has become an important tool for the emergency management community which use it for scneario response planning. It has the potential to link with earthquake monitoring programs to provide automatic loss estimates from network recorded events. An open-source alpha-release version of the software is freely available on SourceForge. It can be used for hazard or risk analyses in any region of the world by supplying appropriately formatted input files. Source code is also supplied so advanced users can modify individual components to suit their needs.
Dynamic Rupture Simulations Based on the Characterized Source Model of the 2011 Tohoku Earthquake
Tsuda, Kenichi; Iwase, Satoshi; Uratani, Hiroaki; Ogawa, Sachio; Watanabe, Takahide; Miyakoshi, Jun'ichi; Ampuero, Jean Paul
2017-01-01
as the Chile subduction zone and the Nankai Trough. Dynamic rupture simulations based on the characterized source model might provide useful insights for hazard assessment associated with future mega-thrust earthquakes.
Short-term load and wind power forecasting using neural network-based prediction intervals.
Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas
2014-02-01
Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.
Wangren Qiu
2015-01-01
Full Text Available In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS (M, N based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parameters M and N, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.
Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting
Nenad Floranović
2013-02-01
Full Text Available Providing accurate load forecast to electric utility corporations is essential in order to reduce their operational costs and increase profits. Hence, training set selection is an important preprocessing step which has to be considered in practice in order to increase the accuracy of load forecasts. The usage of mutual information (MI has been recently proposed in regression tasks, mostly for feature selection and for identifying the real instances from training sets that contains noise and outliers. This paper proposes a methodology for the training set selection in a least squares support vector machines (LS-SVMs load forecasting model. A new application of the concept of MI is presented for the selection of a training set based on MI computation between initial training set instances and testing set instances. Accordingly, several LS-SVMs models have been trained, based on the proposed methodology, for hourly prediction of electric load for one day ahead. The results obtained from a real-world data set indicate that the proposed method increases the accuracy of load forecasting as well as reduces the size of the initial training set needed for model training.
Using a physics-based earthquake simulator to evaluate seismic hazard in NW Iran
Khodaverdian, A.; Zafarani, H.; Rahimian, M.
2016-07-01
NW Iran is a region of active deformation in the Eurasia-Arabia collision zone. This high strain field has caused intensive faulting accompanied by several major (M > 6.5) earthquakes as it is evident from historical records. Whereas seismic data (i.e. instrumental and historical catalogues) are either short, or inaccurate and inhomogeneous, physics-based long-term simulations are beneficial to better assess seismic hazard. In this study, a deterministic seismicity model, which consists of major active faults, is first constructed, and used to generate a synthetic catalogue of large-magnitude (M > 5.5) earthquakes. The frequency-magnitude distribution of the synthetic earthquake catalogue, which is based on the physical characteristic and slip rate of the mapped faults, is consistent with the empirical distribution evaluated using record of instrumental and historical events. The obtained results are also in accordance with palaeoseismic studies and other independent kinematic deformation models of the Iranian Plateau. Using the synthetic catalogue, characteristic magnitude for all 16 active faults in the study area is determined. Magnitude and epicentre of these earthquakes are comparable with the historical records. Large earthquake recurrence times and their variations are evaluated, either for an individual fault or for the region as a whole. Goodness-of-fitness tests revealed that recurrence times can be well described by the Weibull distribution. Time-dependent conditional probabilities for large earthquakes in the study area are also estimated for different time intervals. The resulting synthetic catalogue can be utilized as a useful data set for hazard and risk assessment instead of short, incomplete and inhomogeneous available catalogues.
L. Fedulova
2009-01-01
The author reveals the role of the technology factor in crisis situations and justifies the significance of the mechanisms of technological forecasting in the choice of strategic guidelines of a country's development. She proposes a conceptual model of the system of scientifico-technological forecasting and shows its place in the innovation based renewal of economic activities.
L. Fedulova
2009-01-01
The author reveals the role of the technology factor in crisis situations and justifies the significance of the mechanisms of technological forecasting in the choice of strategic guidelines of a country's development. She proposes a conceptual model of the system of scientifico-technological forecasting and shows its place in the innovation based renewal of economic activities.
Combining Multiple Rupture Models in Real-Time for Earthquake Early Warning
Minson, S. E.; Wu, S.; Beck, J. L.; Heaton, T. H.
2015-12-01
The ShakeAlert earthquake early warning system for the west coast of the United States is designed to combine information from multiple independent earthquake analysis algorithms in order to provide the public with robust predictions of shaking intensity at each user's location before they are affected by strong shaking. The current contributing analyses come from algorithms that determine the origin time, epicenter, and magnitude of an earthquake (On-site, ElarmS, and Virtual Seismologist). A second generation of algorithms will provide seismic line source information (FinDer), as well as geodetically-constrained slip models (BEFORES, GPSlip, G-larmS, G-FAST). These new algorithms will provide more information about the spatial extent of the earthquake rupture and thus improve the quality of the resulting shaking forecasts.Each of the contributing algorithms exploits different features of the observed seismic and geodetic data, and thus each algorithm may perform differently for different data availability and earthquake source characteristics. Thus the ShakeAlert system requires a central mediator, called the Central Decision Module (CDM). The CDM acts to combine disparate earthquake source information into one unified shaking forecast. Here we will present a new design for the CDM that uses a Bayesian framework to combine earthquake reports from multiple analysis algorithms and compares them to observed shaking information in order to both assess the relative plausibility of each earthquake report and to create an improved unified shaking forecast complete with appropriate uncertainties. We will describe how these probabilistic shaking forecasts can be used to provide each user with a personalized decision-making tool that can help decide whether or not to take a protective action (such as opening fire house doors or stopping trains) based on that user's distance to the earthquake, vulnerability to shaking, false alarm tolerance, and time required to act.
Delaney, C.; Hartman, R. K.; Mendoza, J.; Evans, K. M.; Evett, S.
2016-12-01
Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation or flow forecasts to inform the flood operations of reservoirs. Previous research and modeling for flood control reservoirs has shown that FIRO can reduce flood risk and increase water supply for many reservoirs. The risk-based method of FIRO presents a unique approach that incorporates flow forecasts made by NOAA's California-Nevada River Forecast Center (CNRFC) to model and assess risk of meeting or exceeding identified management targets or thresholds. Forecasted risk is evaluated against set risk tolerances to set reservoir flood releases. A water management model was developed for Lake Mendocino, a 116,500 acre-foot reservoir located near Ukiah, California. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United State Army Corps of Engineers and is operated by the Sonoma County Water Agency for water supply. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has been plagued with water supply reliability issues since 2007. FIRO is applied to Lake Mendocino by simulating daily hydrologic conditions from 1985 to 2010 in the Upper Russian River from Lake Mendocino to the City of Healdsburg approximately 50 miles downstream. The risk-based method is simulated using a 15-day, 61 member streamflow hindcast by the CNRFC. Model simulation results of risk-based flood operations demonstrate a 23% increase in average end of water year (September 30) storage levels over current operations. Model results show no increase in occurrence of flood damages for points downstream of Lake Mendocino. This investigation demonstrates that FIRO may be a viable flood control operations approach for Lake Mendocino and warrants further investigation through additional modeling and analysis.
Survey-based indicators vs. hard data: What improves export forecasts in Europe?
2015-01-01
In this study, we evaluate whether survey-based indicators produce lower forecast errorsfor export growth than indicators obtained from hard data such as price and costcompetitiveness measures. Our pseudo out-of-sample analyses and forecastencompassingtests reveal that survey-based indicators outperform the benchmarkmodel as well as the indicators from hard data for most of the twenty European statesfocused on in our study and the aggregates EA-18 and EU-28. The most accurate forecastsare on ...
FORECASTING OF BIOPLASTICS MARKET DEVELOPMENT IN RUSSIA BASED ON IN-DEPTH ANALYSIS AND DATA MINIMG
BAZHANOV N.N.
2015-01-01
The paper deals with the research of market opportunities for potential participants of Russian bioplastics market and with strategic market opportunities evaluation based on data analysis. The paper is aimed at is developing of model and methods of strategic forecasting based on data analysis for Russian bioplastics market. World’s and Russia’s market drivers, development trends and market opportunities for potential market participants were brought to light and analyzed in this research.
Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.
2012-04-01
In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. The comparison of the empirical and dynamical approaches shows the limits of the empirical approach which is not able to take into account hydrological
Estimation of vulnerability functions based on a global earthquake damage database
Spence, R. J. S.; Coburn, A. W.; Ruffle, S. J.
2009-04-01
Developing a better approach to the estimation of future earthquake losses, and in particular to the understanding of the inherent uncertainties in loss models, is vital to confidence in modelling potential losses in insurance or for mitigation. For most areas of the world there is currently insufficient knowledge of the current building stock for vulnerability estimates to be based on calculations of structural performance. In such areas, the most reliable basis for estimating vulnerability is performance of the building stock in past earthquakes, using damage databases, and comparison with consistent estimates of ground motion. This paper will present a new approach to the estimation of vulnerabilities using the recently launched Cambridge University Damage Database (CUEDD). CUEDD is based on data assembled by the Martin Centre at Cambridge University since 1980, complemented by other more-recently published and some unpublished data. The database assembles in a single, organised, expandable and web-accessible database, summary information on worldwide post-earthquake building damage surveys which have been carried out since the 1960's. Currently it contains data on the performance of more than 750,000 individual buildings, in 200 surveys following 40 separate earthquakes. The database includes building typologies, damage levels, location of each survey. It is mounted on a GIS mapping system and links to the USGS Shakemaps of each earthquake which enables the macroseismic intensity and other ground motion parameters to be defined for each survey and location. Fields of data for each building damage survey include: · Basic earthquake data and its sources · Details of the survey location and intensity and other ground motion observations or assignments at that location · Building and damage level classification, and tabulated damage survey results · Photos showing typical examples of damage. In future planned extensions of the database information on human
Artificial intelligence based models for stream-flow forecasting: 2000-2015
Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba
2015-11-01
The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.
A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.
Araújo, Ricardo de A
2012-04-01
Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature.
Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model
Wei Sun
2015-01-01
Full Text Available Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1 (GM (1, 1, back propagation (BP and LSSVM, in terms of prediction accuracy and forecasting risk.
Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR
Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng
2017-06-01
The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.
Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas
2015-09-01
Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.
Long-term flow forecasts based on climate and hydrologic modeling: Uruguay River basin
Tucci, Carlos Eduardo Morelli; Clarke, Robin Thomas; Collischonn, Walter; da Silva Dias, Pedro Leite; de Oliveira, Gilvan Sampaio
2003-07-01
This paper describes a procedure for predicting seasonal flow in the Rio Uruguay drainage basin (area 75,000 km2, lying in Brazilian territory), using sequences of future daily rainfall given by the global climate model (GCM) of the Brazilian agency for climate prediction (Centro de Previsão de Tempo e Clima, or CPTEC). Sequences of future daily rainfall given by this model were used as input to a rainfall-runoff model appropriate for large drainage basins. Forecasts of flow in the Rio Uruguay were made for the period 1995-2001 of the full record, which began in 1940. Analysis showed that GCM forecasts underestimated rainfall over almost all the basin, particularly in winter, although interannual variability in regional rainfall was reproduced relatively well. A statistical procedure was used to correct for the underestimation of rainfall. When the corrected rainfall sequences were transformed to flow by the hydrologic model, forecasts of flow in the Rio Uruguay basin were better than forecasts based on historic mean or median flows by 37% for monthly flows and by 54% for 3-monthly flows.
E, Jianwei; Bao, Yanling; Ye, Jimin
2017-10-01
As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.
Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian; van Andel, Schalk-Jan; Wood, Andy
2014-05-01
Probabilistic streamflow forecasts have been increasingly used or requested by practitioners in the operation of multipurpose water reservoirs. They usually integrate hydrologic inflow forecasts to their operational management rules to optimize water allocation or its economic value, to mitigate droughts, for flood and ecological control, among others. In this paper, we present an experiment conducted to investigate the use of probabilistic forecasts to make decisions on water reservoir outflows. The experiment was set up as a risk-based decision-making game. In the game, each participant acted as a water manager. A sequence of probabilistic inflow forecasts was presented to be used to make a reservoir release decision at a monthly time step, subject to a few constraints. After each decision, the actual inflow was presented and the consequences of the decisions made were discussed. Results from the application of the game to different groups of scientists and operational managers during conferences and meetings in 2013 (a total of about 150 participants) illustrate the different strategies adopted by the players. This game experiment allowed participants to experience first hand the challenges of probabilistic, quantitative decision-making.
Dynamic Critical Rainfall-Based Flash Flood Early Warning and Forecasting for Medium-Small Rivers
Liu, Z.; Yang, D.; Hu, J.
2012-04-01
China is extremely frequent food disasters hit countries, annual flood season flash floods triggered by rainfall, mudslides, landslides have caused heavy casualties and property losses, not only serious threaten the lives of the masses, but the majority of seriously restricting the mountain hill areas of economic and social development and the people become rich, of building a moderately prosperous society goals. In the next few years, China will focus on prevention and control area in the flash flood disasters initially built "for the surveillance, communications, forecasting, early warning and other non-engineering measure based, non-engineering measures and the combinations of engineering measures," the mitigation system. The latest progresses on global torrential flood early warning and forecasting techniques are reviewed in this paper, and then an early warning and forecasting approach is proposed on the basis of a distributed hydrological model according to dynamic critical rainfall index. This approach has been applied in Suichuanjiang River basin in Jiangxi province, which is expected to provide valuable reference for building a national flash flood early warning and forecasting system as well as control of such flooding.
SOM-based Hybrid Neural Network Model for Flood Inundation Extent Forecasting
Chang, Li-Chiu; Shen, Hung-Yu; Chang, Fi-John
2014-05-01
In recent years, the increasing frequency and severity of floods caused by climate change and/or land overuse has been reported both nationally and globally. Therefore, estimation of flood depths and extents may provide disaster information for alleviating risk and loss of life and property. The conventional inundation models commonly need a huge amount of computational time to carry out a high resolution spatial inundation map. Moreover, for implementing appropriate mitigation strategies of various flood conditions, different flood scenarios and the corresponding mitigation alternatives are required. Consequently, it is difficult to reach real-time forecast of the inundation extent by conventional inundation models. This study proposed a SOM-RNARX model, for on-line forecasting regional flood inundation depths and extents. The SOM-RNARX model is composed of SOM (Self-Organizing Map) and RNARX (recurrent configuration of nonlinear autoregressive with exogenous inputs). The SOM network categorizes various flood inundation maps of the study area to produce a meaningful regional flood topological map. The RNARX model is built to forecast the total flooded volume of the study area. To find the neuron with the closest total inundated volume to the forecasted total inundated volumes, the forecasted value is used to adjust the weights (inundated depths) of the closest neuron and obtain a regional flood inundation map. The proposed methodology was trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model in Yilan County, Taiwan. For comparison, the CHIM (clustering-based hybrid inundation model) model which was issued by Chang et al. (2010) was performed. The major difference between these two models is that CHIM classify flooding characteristics, and SOM-RNARX extracts the relationship between rainfall pattern and flooding spatial distribution. The results show that (1)two models can adequately provide on
Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks
Zhisheng Zhang
2016-01-01
Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.
U.S. Geological Survey, Department of the Interior — The U. S. Geological Survey (USGS) makes long-term seismic hazard forecasts that are used in building codes. The hazard models usually consider only natural...
Hirata, K.; Fujiwara, H.; Nakamura, H.; Osada, M.; Morikawa, N.; Kawai, S.; Ohsumi, T.; Aoi, S.; Yamamoto, N.; Matsuyama, H.; Toyama, N.; Kito, T.; Murashima, Y.; Murata, Y.; Inoue, T.; Saito, R.; Takayama, J.; Akiyama, S.; Korenaga, M.; Abe, Y.; Hashimoto, N.
2015-12-01
The Earthquake Research Committee(ERC)/HERP, Government of Japan (2013) revised their long-term evaluation of the forthcoming large earthquake along the Nankai Trough; the next earthquake is estimated M8 to 9 class, and the probability (P30) that the next earthquake will occur within the next 30 years (from Jan. 1, 2013) is 60% to 70%. In this study, we assess tsunami hazards (maximum coastal tsunami heights) in the near future, in terms of a probabilistic approach, from the next earthquake along Nankai Trough, on the basis of ERC(2013)'s report. The probabilistic tsunami hazard assessment that we applied is as follows; (1) Characterized earthquake fault models (CEFMs) are constructed on each of the 15 hypothetical source areas (HSA) that ERC(2013) showed. The characterization rule follows Toyama et al.(2015, JpGU). As results, we obtained total of 1441 CEFMs. (2) We calculate tsunamis due to CEFMs by solving nonlinear, finite-amplitude, long-wave equations with advection and bottom friction terms by finite-difference method. Run-up computation on land is included. (3) A time predictable model predicts the recurrent interval of the present seismic cycle is T=88.2 years (ERC,2013). We fix P30 = 67% by applying the renewal process based on BPT distribution with T and alpha=0.24 as its aperiodicity. (4) We divide the probability P30 into P30(i) for i-th subgroup consisting of the earthquakes occurring in each of 15 HSA by following a probability re-distribution concept (ERC,2014). Then each earthquake (CEFM) in i-th subgroup is assigned a probability P30(i)/N where N is the number of CEFMs in each sub-group. Note that such re-distribution concept of the probability is nothing but tentative because the present seismology cannot give deep knowledge enough to do it. Epistemic logic-tree approach may be required in future. (5) We synthesize a number of tsunami hazard curves at every evaluation points on coasts by integrating the information about 30 years occurrence
Kachakhidze, Manana; Kachakhidze, Nino
2016-04-01
Authors of abstract have created work which offers model of earth electromagnetic emissions generation detected in the process of earthquake preparation on the basis of electrodynamics. The model gives qualitative explanation of a mechanism of generation of electromagnetic waves emitted in the earthquake preparation period. Besides, scheme of the methodology of earthquake forecasting is created based on avalanche-like unstable model of fault formation and an analogous model of electromagnetic contour, synthesis of which, is rather harmonious. According to the authors of the work electromagnetic emissions in radiodiapason is more universal and reliable than other anomalous variations of various geophysical phenomena in earthquake preparation period; Besides, VLF/LF electromagnetic emissions might be declared as the main precursor of earthquake because it might turn out very useful with the view of prediction of large (M ≥5) inland earthquakes and to govern processes going on in lithosphere-atmosphere - ionosphere coupling (LAIC) system. Since the other geophysical phenomena, which may accompany earthquake preparation process and expose themselves several months, weeks or days prior to earthquakes are less informative with the view of earthquake forecasting, it is admissible to consider them as earthquake indicators. Physical mechanisms of mentioned phenomena are explained on the basis of the model of generation of electromagnetic emissions detected before earthquake, where a process of earthquake preparation and its realization are considered taking into account distributed and conservative systems properties. Up to these days electromagnetic emissions detection network did not exist in Georgia. European colleagues helped us (Prof. Dr. PF Biagi, Prof. Dr. Aydın BÜYÜKSARAÇ) and made possible the installation of a receiver. We are going to develop network and put our share in searching of earthquakes problem. Participation in conference is supported by financial
2014-02-01
Cold, normal, warm conditions Continuous Visual, continuous, probabilistic, spatial, ensemble Maximum temperature Object-, or event-oriented...errors are much smaller than the expected error in the forecast, allowing them to be ignored. Verification results tend to be more trustworthy when
BozorgMagham, Amir E.; Ross, Shane D.; Schmale, David G.
2013-09-01
The language of Lagrangian coherent structures (LCSs) provides a new means for studying transport and mixing of passive particles advected by an atmospheric flow field. Recent observations suggest that LCSs govern the large-scale atmospheric motion of airborne microorganisms, paving the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. In addition, having reliable predictions of the timing of hyperbolic LCSs may contribute to improved aerobiological sampling of microorganisms with unmanned aerial vehicles and LCS-based early warning systems. Chaotic atmospheric dynamics lead to unavoidable forecasting errors in the wind velocity field, which compounds errors in LCS forecasting. In this study, we reveal the cumulative effects of errors of (short-term) wind field forecasts on the finite-time Lyapunov exponent (FTLE) fields and the associated LCSs when realistic forecast plans impose certain limits on the forecasting parameters. Objectives of this paper are to (a) quantify the accuracy of prediction of FTLE-LCS features and (b) determine the sensitivity of such predictions to forecasting parameters. Results indicate that forecasts of attracting LCSs exhibit less divergence from the archive-based LCSs than the repelling features. This result is important since attracting LCSs are the backbone of long-lived features in moving fluids. We also show under what circumstances one can trust the forecast results if one merely wants to know if an LCS passed over a region and does not need to precisely know the passage time.
Development of a remote sensing-based rice yield forecasting model
Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H.
2016-11-01
This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh. (Author)
Recent Achievements of the Collaboratory for the Study of Earthquake Predictability
Jackson, D. D.; Liukis, M.; Werner, M. J.; Schorlemmer, D.; Yu, J.; Maechling, P. J.; Zechar, J. D.; Jordan, T. H.
2015-12-01
Maria Liukis, SCEC, USC; Maximilian Werner, University of Bristol; Danijel Schorlemmer, GFZ Potsdam; John Yu, SCEC, USC; Philip Maechling, SCEC, USC; Jeremy Zechar, Swiss Seismological Service, ETH; Thomas H. Jordan, SCEC, USC, and the CSEP Working Group The Collaboratory for the Study of Earthquake Predictability (CSEP) supports a global program to conduct prospective earthquake forecasting experiments. CSEP testing centers are now operational in California, New Zealand, Japan, China, and Europe with 435 models under evaluation. The California testing center, operated by SCEC, has been operational since Sept 1, 2007, and currently hosts 30-minute, 1-day, 3-month, 1-year and 5-year forecasts, both alarm-based and probabilistic, for California, the Western Pacific, and worldwide. We have reduced testing latency, implemented prototype evaluation of M8 forecasts, and are currently developing formats and procedures to evaluate externally-hosted forecasts and predictions. These efforts are related to CSEP support of the USGS program in operational earthquake forecasting and a DHS project to register and test external forecast procedures from experts outside seismology. A retrospective experiment for the 2010-2012 Canterbury earthquake sequence has been completed, and the results indicate that some physics-based and hybrid models outperform purely statistical (e.g., ETAS) models. The experiment also demonstrates the power of the CSEP cyberinfrastructure for retrospective testing. Our current development includes evaluation strategies that increase computational efficiency for high-resolution global experiments, such as the evaluation of the Global Earthquake Activity Rate (GEAR) model. We describe the open-source CSEP software that is available to researchers as they develop their forecast models (http://northridge.usc.edu/trac/csep/wiki/MiniCSEP). We also discuss applications of CSEP infrastructure to geodetic transient detection and how CSEP procedures are being
O. V. Russkov
2015-01-01
Full Text Available The article considers a hot issue to forecast electric power demand amounts and prices for the entities of wholesale electricity market (WEM, which are in capacity of a large user with production technology requirements prevailing over hourly energy planning ones. An electric power demand of such entities is on irregular schedule. The article analyses mathematical models, currently applied to forecast demand amounts and prices. It describes limits of time-series models and fundamental ones in case of hourly forecasting an irregular demand schedule of the electricity market entity. The features of electricity trading at WEM are carefully analysed. Factors that influence on irregularity of demand schedule of the metallurgical plant are shown. The article proposes method for the qualitative forecast of market price ratios as a tool to reduce a dependence on the accuracy of forecasting an irregular schedule of demand. It describes the differences between the offered method and the similar ones considered in research studies and scholarly works. The correlation between price ratios and relaxation in the requirements for the forecast accuracy of the electric power consumption is analysed. The efficiency function of forecast method is derived. The article puts an increased focus on description of the mathematical model based on the method of qualitative forecast. It shows main model parameters and restrictions the electricity market imposes on them. The model prototype is described as a programme module. Methods to assess an effectiveness of the proposed forecast model are examined. The positive test results of the model using JSC «Volzhsky Pipe Plant» data are given. A conclusion is drawn concerning the possibility to decrease dependence on the forecast accuracy of irregular schedule of entity’s demand at WEM. The effective trading tool has been found for the entities of irregular demand schedule at WEM. The tool application allows minimizing cost
Visualizing the 2009 Samoan and Sumatran Earthquakes using Google Earth-based COLLADA models
de Paor, D. G.; Brooks, W. D.; Dordevic, M.; Ranasinghe, N. R.; Wild, S. C.
2009-12-01
Earthquake hazards are generally analyzed by a combination of graphical focal mechanism or centroid moment tensor solutions (aka geophysical beach balls), contoured fault plane maps, and shake maps or tsunami damage surveys. In regions of complex micro-plate tectonics, it can be difficult to visualize spatial and temporal relations among earthquakes, aftershocks, and associated tectonic and volcanic structures using two-dimensional maps and cross sections alone. Developing the techniques originally described by D.G. De Paor & N.R. Williams (EOS Trans. AGU S53E-05, 2006), we can view the plate tectonic setting, geophysical parameters, and societal consequences of the 2009 Samoan and Sumatran earthquakes on the Google Earth virtual globe. We use xml-based COLLADA models to represent the subsurface structure and standard KML to overlay map data on the digital terrain model. Unlike traditional geophysical beach ball figures, our models are three dimensional and located at correct depth, and they optionally show nodal planes which are useful in relating the orientation of one earthquake to the hypo-centers of its neighbors. With the aid of the new Google Earth application program interface (GE API), we can use web page-based Javascript controls to lift structural models from the subsurface in Google Earth and generate serial sections along strike. Finally, we use the built-in features of the Google Earth web browser plug-in to create a virtual tour of damage sites with hyperlinks to web-based field reports. These virtual globe visualizations may help complement existing KML and HTML resources of the USGS Earthquake Hazards Program and The Global CMT Project.
Maruya, Hiroaki
For most Japanese companies and organizations, the enormous damage of the Great East Japan Earthquake was more than expected. In addition to great tsunami and earthquake motion, the lack of electricity and fuel disturbed to business activities seriously, and they should be considered important constraint factors in future earthquakes. Furthermore, disruption of supply chains also led considerable decline of production in many industries across Japan and foreign countries. Therefore it becomes urgent need for Japanese government and industries to utilize the lessons of the Great Earthquake and execute effective countermeasures, considering great earthquakes such as Tonankai & Nankai earthquakes and Tokyo Inland Earthquakes. Obviously most basic step is improving earthquake-resistant ability of buildings and facilities. In addition the spread of BCP and BCM to enterprises and organizations is indispensable. Based on the lessons, the BCM should include the point of view of the supply chain management more clearly, and emphasize "substitute strate