Addressing the Challenges of Distributed Hydrologic Modeling for Operational Forecasting
Butts, M. B.; Yamagata, K.; Kobor, J.; Fontenot, E.
2008-05-01
Operational forecasting systems must provide reliable, accurate and timely flood forecasts for a range of catchments from small rapidly responding mountain catchments and urban areas to large, complex but more slowly responding fluvial systems. Flood forecasting systems have evolved from simple forecasting for flood mitigation to real-time decision support systems for real-time reservoir operations for water supply, navigation, hydropower, for managing environmental flows and habitat protection, cooling water and water quality forecasting. These different requirements lead to a number of challenges in applying distributed modelling in an operational context. These challenges include, the often short time available for forecasting that requires a trade-off between model complexity and accuracy on the one hand and on the other hand the need for efficient calculations to reduce the computation times. Limitations in the data available in real-time require modelling tools that can not only operate on a minimum of data but also take advantage of new data sources such as weather radar, satellite remote sensing, wireless sensors etc. Finally, models must not only accurately predict flood peaks but also forecast low flows and surface water-groundwater interactions, water quality, water temperature, optimal reservoir levels, and inundated areas. This paper shows how these challenges are being addressed in a number of case studies. The central strategy has been to develop a flexible modelling framework that can be adapted to different data sources, different levels of complexity and spatial distribution and different modelling objectives. The resulting framework allows amongst other things, optimal use of grid-based precipitation fields from weather radar and numerical weather models, direct integration of satellite remote sensing, a unique capability to treat a range of new forecasting problems such as flooding conditioned by surface water-groundwater interactions. Results
Real-Time Analysis and Forecasting of Multisite River Flow Using a Distributed Hydrological Model
Directory of Open Access Journals (Sweden)
Mingdong Sun
2014-01-01
Full Text Available A spatial distributed hydrological forecasting system was developed to promote the analysis of river flow dynamic state in a large basin. The research presented the real-time analysis and forecasting of multisite river flow in the Nakdong River Basin using a distributed hydrological model with radar rainfall forecast data. A real-time calibration algorithm of hydrological distributed model was proposed to investigate the particular relationship between the water storage and basin discharge. Demonstrate the approach of simulating multisite river flow using a distributed hydrological model couple with real-time calibration and forecasting of multisite river flow with radar rainfall forecasts data. The hydrographs and results exhibit that calibrated flow simulations are very approximate to the flow observation at all sites and the accuracy of forecasting flow is gradually decreased with lead times extending from 1 hr to 3 hrs. The flow forecasts are lower than the flow observation which is likely caused by the low estimation of radar rainfall forecasts. The research has well demonstrated that the distributed hydrological model is readily applicable for multisite real-time river flow analysis and forecasting in a large basin.
Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A
2014-08-01
Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate
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Y. Xuan
2009-03-01
Full Text Available Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1 The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2 The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3 the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.
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Lisbet Sneftrup Hansen
2014-07-01
Full Text Available There is a growing requirement to generate more precise model simulations and forecasts of flows in urban drainage systems in both offline and online situations. Data assimilation tools are hence needed to make it possible to include system measurements in distributed, physically-based urban drainage models and reduce a number of unavoidable discrepancies between the model and reality. The latter can be achieved partly by inserting measured water levels from the sewer system into the model. This article describes how deterministic updating of model states in this manner affects a simulation, and then evaluates and documents the performance of this particular updating procedure for flow forecasting. A hypothetical case study and synthetic observations are used to illustrate how the Update method works and affects downstream nodes. A real case study in a 544 ha urban catchment furthermore shows that it is possible to improve the 20-min forecast of water levels in an updated node and the three-hour forecast of flow through a downstream node, compared to simulations without updating. Deterministic water level updating produces better forecasts when implemented in large networks with slow flow dynamics and with measurements from upstream basins that contribute significantly to the flow at the forecast location.
Noh, S.J.; Rakovec, O.; Weerts, A.H.; Tachikawa, Y.
2014-01-01
We investigate the effects of noise specification on the quality of hydrological forecasts via an advanced data assimilation (DA) procedure using a distributed hydrological model driven by numerical weather predictions. The sequential DA procedure is based on (1) a multivariate rainfall ensemble
DEFF Research Database (Denmark)
Hansen, Lisbeth S.; Borup, Morten; Møller, A.;
2011-01-01
the performance of the updating procedure for flow forecasting. Measured water levels in combination with rain gauge input are used as basis for the evaluation. When compared to simulations without updating, the results show that it is possible to obtain an improvement in the 20 minute forecast of the water level...... to eliminate some of the unavoidable discrepancies between model and reality. The latter can partly be achieved by using the commercial tool MOUSE UPDATE, which is capable of inserting measured water levels from the system into the distributed, physically based MOUSE model. This study evaluates and documents...
DEFF Research Database (Denmark)
Lunde, Asger; Olesen, Kasper Vinther
frequency and method have significant implications for model fit in-sample. Finally, we consider an extensive out-of-sample exercise to forecast the conditional return distribution. The out-of-sample results for the Realized GARCH forecasts suggest a limited added value from using “traditional” realized......We explore intraday transaction records from NASDAQ OMX Commodities Europe from January 2006 to October 2013. We analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH framework for the joint modeling of returns and realized...... measures of volatility. An influential bias in these measures is documented, which motivates the use of a flexible and robust methodology such as the Realized GARCH. Within this framework, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional...
Uncertainty in flood forecasting: A distributed modeling approach in a sparse data catchment
Mendoza, Pablo A.; McPhee, James; Vargas, Ximena
2012-09-01
Data scarcity has traditionally precluded the application of advanced hydrologic techniques in developing countries. In this paper, we evaluate the performance of a flood forecasting scheme in a sparsely monitored catchment based on distributed hydrologic modeling, discharge assimilation, and numerical weather predictions with explicit validation uncertainty analysis. For the hydrologic component of our framework, we apply TopNet to the Cautin River basin, located in southern Chile, using a fully distributed a priori parameterization based on both literature-suggested values and data gathered during field campaigns. Results obtained from this step indicate that the incremental effort spent in measuring directly a set of model parameters was insufficient to represent adequately the most relevant hydrologic processes related to spatiotemporal runoff patterns. Subsequent uncertainty validation performed over a six month ensemble simulation shows that streamflow uncertainty is better represented during flood events, due to both the increase of state perturbation introduced by rainfall and the flood-oriented calibration strategy adopted here. Results from different assimilation configurations suggest that the upper part of the basin is the major source of uncertainty in hydrologic process representation and hint at the usefulness of interpreting assimilation results in terms of model input and parameterization inadequacy. Furthermore, in this case study the violation of Markovian state properties by the Ensemble Kalman filter did affect the numerical results, showing that an explicit treatment of the time delay between the generation of surface runoff and the arrival at the basin outlet is required in the assimilation scheme. Peak flow forecasting results demonstrate that there is a major problem with the Weather Research and Forecasting model outputs, which systematically overestimate precipitation over the catchment. A final analysis performed for a large flooding
Forecasting the behaviour of complex landslides with a spatially distributed hydrological model
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J.-P. Malet
2005-01-01
Full Text Available The relationships between rainfall, hydrology and landslide movement are often difficult to establish. In this context, ground-water flow analyses and dynamic modelling can help to clarify these complex relations, simulate the landslide hydrological behaviour in real or hypothetical situations, and help to forecast future scenarios based on environmental change. The primary objective of this study is to investigate the possibility of including more temporal and spatial information in landslide hydrology forecasting, by using a physically based spatially distributed model. Results of the hydrological and geomorphological investigation of the Super-Sauze earthflow, one of the persistently active landslide occurring in clay-rich material of the French Alps, are presented. Field surveys, continuous monitoring and interpretation of the data have shown that, in such material, the groundwater level fluctuates on a seasonal time scale, with a strong influence of the unsaturated zone. Therefore a coupled unsaturated/saturated model, incorporating Darcian saturated flow, fissure flow and meltwater flow is needed to adequately represent the landslide hydrology. The conceptual model is implemented in a 2.5-D spatially distributed hydrological model. The model is calibrated and validated on a multi-parameters database acquired on the site since 1997. The complex time-dependent and three-dimensional groundwater regime is well described, in both the short- and long-term. The hydrological model is used to forecast the future hydrological behaviour of the earthflow in response to potential environmental changes.
Updating Known Distribution Models for Forecasting Climate Change Impact on Endangered Species
Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo
2013-01-01
To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli's Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species' distribution, instead of building new models that are based on climate change variables only. PMID:23840330
Updating known distribution models for forecasting climate change impact on endangered species.
Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo
2013-01-01
To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli's Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species' distribution, instead of building new models that are based on climate change variables only.
Large-watershed flood forecasting with high-resolution distributed hydrological model
Chen, Yangbo; Li, Ji; Wang, Huanyu; Qin, Jianming; Dong, Liming
2017-02-01
A distributed hydrological model has been successfully used in small-watershed flood forecasting, but there are still challenges for the application in a large watershed, one of them being the model's spatial resolution effect. To cope with this challenge, two efforts could be made; one is to improve the model's computation efficiency in a large watershed, the other is implementing the model on a high-performance supercomputer. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil and land use are downloaded from the website freely, and the model structure with a high resolution of 200 m × 200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the Particle Swarm Optimization (PSO) algorithm; the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m × 200 m model resolution is proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500 m × 500 m grid cell, but the model spatial resolution with a 200 m
Indian Academy of Sciences (India)
Wayan Suparta; Gusrizal
2014-08-01
We implement a hierarchical Bayesian spatiotemporal (HBST) model to forecast the daily trapped particle flux distribution over the South Atlantic Anomaly (SAA) region. The National Oceanic and Atmospheric Administration (NOAA)-15 data from 1–30 March 2008 with particle energies as < 30 keV (mep0e1) and < 300 keV (mep0e3) for electrons and 80–240 keV (mep0p2) and < 6900 keV (mep0p6) for protons were used as the model input to forecast the flux values on 31 March 2008. Data were transformed into logarithmic values and gridded in a 5° × 5° longitude and latitude size to fulfill the modeling precondition. A Monte Carlo Markov chain (MCMC) was then performed to solve the HBST Gaussian Process (GP) model by using the Gibbs sampling method. The result for this model was interpolated by a Kriging technique to achieve the whole distribution figure over the SAA region. Statistical results of the root mean square error (RMSE), mean absolute percentage error (MAPE), and bias (BIAS) showed a good indicator of the HBST method. The statistical validation also indicated the high variability of particle flux values in the SAA core area. The visual validation showed a powerful combination of HBST-GP model with Kriging interpolation technique. The Kriging also produced a good quality of the distribution map of particle flux over the SAA region as indicated by its small variance value. This suggests that the model can be applied in the development of a Low Earth Orbit (LEO)-Equatorial satellite for monitoring trapped particle radiation hazard.
Adjusting particle-size distributions to account for aggregation in tephra-deposit model forecasts
Mastin, Larry G.; Van Eaton, Alexa; Durant, A.J.
2016-01-01
Volcanic ash transport and dispersion (VATD) models are used to forecast tephra deposition during volcanic eruptions. Model accuracy is limited by the fact that fine-ash aggregates (clumps into clusters), thus altering patterns of deposition. In most models this is accounted for by ad hoc changes to model input, representing fine ash as aggregates with density ρagg, and a log-normal size distribution with median μagg and standard deviation σagg. Optimal values may vary between eruptions. To test the variance, we used the Ash3d tephra model to simulate four deposits: 18 May 1980 Mount St. Helens; 16–17 September 1992 Crater Peak (Mount Spurr); 17 June 1996 Ruapehu; and 23 March 2009 Mount Redoubt. In 192 simulations, we systematically varied μagg and σagg, holding ρagg constant at 600 kg m−3. We evaluated the fit using three indices that compare modeled versus measured (1) mass load at sample locations; (2) mass load versus distance along the dispersal axis; and (3) isomass area. For all deposits, under these inputs, the best-fit value of μagg ranged narrowly between ∼ 2.3 and 2.7φ (0.20–0.15 mm), despite large variations in erupted mass (0.25–50 Tg), plume height (8.5–25 km), mass fraction of fine ( operational model forecasts. Further research may indicate whether this narrow range also reflects physical constraints on processes in the evolving cloud.
Adjusting particle-size distributions to account for aggregation in tephra-deposit model forecasts
Mastin, Larry G.; Van Eaton, Alexa R.; Durant, Adam J.
2016-07-01
Volcanic ash transport and dispersion (VATD) models are used to forecast tephra deposition during volcanic eruptions. Model accuracy is limited by the fact that fine-ash aggregates (clumps into clusters), thus altering patterns of deposition. In most models this is accounted for by ad hoc changes to model input, representing fine ash as aggregates with density ρagg, and a log-normal size distribution with median μagg and standard deviation σagg. Optimal values may vary between eruptions. To test the variance, we used the Ash3d tephra model to simulate four deposits: 18 May 1980 Mount St. Helens; 16-17 September 1992 Crater Peak (Mount Spurr); 17 June 1996 Ruapehu; and 23 March 2009 Mount Redoubt. In 192 simulations, we systematically varied μagg and σagg, holding ρagg constant at 600 kg m-3. We evaluated the fit using three indices that compare modeled versus measured (1) mass load at sample locations; (2) mass load versus distance along the dispersal axis; and (3) isomass area. For all deposits, under these inputs, the best-fit value of μagg ranged narrowly between ˜ 2.3 and 2.7φ (0.20-0.15 mm), despite large variations in erupted mass (0.25-50 Tg), plume height (8.5-25 km), mass fraction of fine ( water content between these eruptions. This close agreement suggests that aggregation may be treated as a discrete process that is insensitive to eruptive style or magnitude. This result offers the potential for a simple, computationally efficient parameterization scheme for use in operational model forecasts. Further research may indicate whether this narrow range also reflects physical constraints on processes in the evolving cloud.
Cole, Steven J.; Moore, Robert J.; Robson, Alice J.; Mattingley, Paul S.
2014-05-01
Across Britain, floods in rapidly responding catchments are a major concern and regularly cause significant damage (e.g. Boscastle 2004, Morpeth 2008, Cornwall 2010 and Comrie 2012). Typically these catchments have a small area and are characterised by steep slopes and/or significant suburban/urban land-cover. The meteorological drivers can be of convective origin or frontal with locally intense features (e.g. embedded convection or orographic enhancement); saturated catchments can amplify the flood response. Both rainfall and flood forecasting for Rapid Response Catchments (RRCs)are very challenging due to the often small-scale nature of the intense rainfall which is of most concern, the small catchment areas, and the short catchment response times. Over the last 3 to 4 years, new countrywide Flood Forecasting Systems based on the Grid-to-Grid (G2G) distributed hydrological (rainfall-runoff and routing) model have been implemented across Britain for use by the Flood Forecasting Centre and Scottish Flood Forecasting Service. This has achieved a step-change in operational capability with forecasts of flooding several days ahead "everywhere" on a 1 km grid now possible. The modelling and forecasting approach underpins countrywide Flood Guidance Statements out to 5 days which are used by emergency response organisations for planning and preparedness. The initial focus of these systems has been to provide a countrywide overview of flood risk. However, recent research has explored the potential of the G2G approach to support more frequent and detailed alerts relevant to flood warning in RRCs. Integral to this activity is the use of emerging high-resolution (~1.5km) rainfall forecast products, in deterministic and ensemble form. High spatial resolutions are required to capture some of the small-scale processes and intense rainfall features such as orographic enhancement and convective storm evolution. Even though a deterministic high-resolution numerical weather
Continuum: a distributed hydrological model for water management and flood forecasting
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F. Silvestro
2012-06-01
Full Text Available Full process description and distributed hydrological models are very useful tools in hydrology as they can be applied in different contexts and for a wide range of aims such as flood and drought forecasting, water management, prediction of impact on the hydrologic cycle due to natural and human changes to catchment features in present and changing climates. Since they must mimic a variety of physical processes they can be very complex and with a high degree of parameterization. This complexity can be increased by the need to relate the state variables to observations in order to allow data assimilation.
In this work a model, aiming at balancing the need to reproduce the physical processes with the practical goal of avoiding over-parameterization, is presented. The model is designed to be implemented in different contexts with a special focus on data scarce environments.
All the main hydrological phenomena are modeled in a distributed way. Mass balance and energy balance are solved explicitly. Land surface temperature, which is particularly suited to being extensively observed and assimilated, is an explicit state variable.
An objective performance evaluation, based on both traditional and satellite derived data, is presented with a specific reference to the application in an Italian catchment. The model has been calibrated and validated using different data sets on two nested outlet sections and the capability of the model in reproducing both the stream-flow measurements and the land surface temperature retrieved by satellite measurements, has been investigated.
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Energy Technology Data Exchange (ETDEWEB)
Ajami, N K; Duan, Q; Gao, X; Sorooshian, S
2005-04-11
This paper examines several multi-model combination techniques: the Simple Multi-model Average (SMA), the Multi-Model Super Ensemble (MMSE), Modified Multi-Model Super Ensemble (M3SE) and the Weighted Average Method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multi-model combination results were obtained using uncalibrated DMIP model outputs and were compared against the best uncalibrated as well as the best calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the skill levels of the multi-model predictions. This study revealed that the multi-model predictions obtained from uncalibrated single model predictions are generally better than any single member model predictions, even the best calibrated single model predictions. Furthermore, more sophisticated multi-model combination techniques that incorporated bias correction steps work better than simple multi-model average predictions or multi-model predictions without bias correction.
Energy Technology Data Exchange (ETDEWEB)
Ajami, N; Duan, Q; Gao, X; Sorooshian, S
2006-05-08
This paper examines several multi-model combination techniques: the Simple Multimodel Average (SMA), the Multi-Model Super Ensemble (MMSE), Modified Multi-Model Super Ensemble (M3SE) and the Weighted Average Method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multi-model combination results were obtained using uncalibrated DMIP model outputs and were compared against the best uncalibrated as well as the best calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the skill levels of the multi-model predictions. This study revealed that the multi-model predictions obtained from uncalibrated single model predictions are generally better than any single member model predictions, even the best calibrated single model predictions. Furthermore, more sophisticated multi-model combination techniques that incorporated bias correction steps work better than simple multi-model average predictions or multi-model predictions without bias correction.
Rowe, M. D.; Anderson, E. J.; Wynne, T. T.; Stumpf, R. P.; Fanslow, D. L.; Kijanka, K.; Vanderploeg, H. A.; Strickler, J. R.; Davis, T. W.
2016-07-01
Cyanobacterial harmful algal blooms (CHABs) are a problem in western Lake Erie, and in eutrophic fresh waters worldwide. Western Lake Erie is a large (3000 km2), shallow (8 m mean depth), freshwater system. CHABs occur from July to October, when stratification is intermittent in response to wind and surface heating or cooling (polymictic). Existing forecast models give the present location and extent of CHABs from satellite imagery, then predict two-dimensional (surface) CHAB movement in response to meteorology. In this study, we simulated vertical distribution of buoyant Microcystis colonies, and 3-D advection, using a Lagrangian particle model forced by currents and turbulent diffusivity from the Finite Volume Community Ocean Model (FVCOM). We estimated the frequency distribution of Microcystis colony buoyant velocity from measured size distributions and buoyant velocities. We evaluated several random-walk numerical schemes to efficiently minimize particle accumulation artifacts. We selected the Milstein scheme, with linear interpolation of the diffusivity profile in place of cubic splines, and varied the time step at each particle and step based on the curvature of the local diffusivity profile to ensure that the Visser time step criterion was satisfied. Inclusion of vertical mixing with buoyancy significantly improved model skill statistics compared to an advection-only model, and showed greater skill than a persistence forecast through simulation day 6, in a series of 26 hindcast simulations from 2011. The simulations and in situ observations show the importance of subtle thermal structure, typical of a polymictic lake, along with buoyancy in determining vertical and horizontal distribution of Microcystis.
Gorham, T. A.; Boyle, D. P.; McConnell, J. R.; Lamorey, G. W.; Markstrom, S.; Viger, R.; Leavesley, G.
2001-12-01
Approximately two-thirds of the runoff in the Rio Grande begins as seasonal snowpack in the headwaters above the USGS stream gaging stations at several points (nodes) above Albuquerque, New Mexico. Resource managers in the Rio Grande Basin rely on accurate short and long term forecasts of water availability and flow at these nodes to make important decisions aimed at achieving a balance among many different and competing water uses such as municipal, fish and wildlife, agricultural, and water quality. In this study, a distributed, physically based hydrologic model is used to investigate the degree of spatial and temporal distribution of snow and the processes that control snowmelt necessary to accurately simulate streamflow at seven of these nodes. Specifically, snow distribution and surface runoff are estimated using a combination of the USGS Modular Modeling System (MMS), GIS Weasel, Precipitation-Runoff Modeling System (PRMS), and XYZ snow distribution model. This highly collaborative work between researchers at the Desert Research Institute and the USGS is an important part of SAHRA (Sustainability of semi-Arid Hydrology and Riparian Areas) efforts aimed at improving models of snow distribution and snowmelt processes.
Bell, V. A.; Moore, R. J.
A simple distributed rainfall-runoff model, configured on a square grid to make best use of weather radar data, was developed in Part 1 (Bell and Moore, 1998). The simple form of the basic model, referred to as the Simple Grid Model or SGM, allows a number of model variants to be introduced, including probability-distributed storage and topographic index representations of runoff production and formulations which use soil survey and land use data. These models are evaluated here on three catchments in the UK: the Rhondda in south Wales, the Wyre in north-west England and the Mole in the Thames Basin near London. Assessment is initially carried out in simulation mode to focus on the conversion of rainfall to runoff as influenced by (i) use of radar or raingauge input, (ii) choice of model variant, and (iii) use of a lumped or distributed model formulation. Weather radar data, in grid square and catchment average form, and raingauge data are used as alternative estimates of rainfall input to the model. Results show that when radar data are of good quality, significant model improvement may be obtained by replacing data from a single raingauge by 2 km grid square radar data. The performance of the Simple Grid Model with optimised isochrones is only marginally improved through the use of different model variants and is generally preferred on account of its simplicity. A more traditional lumped rainfall-runoff model, the Probability-Distributed Moisture model or PDM, is used as a benchmark against which to assess the performance of the distributed models. This proves hard to better, although the distributed formulation of the Grid model proves more reliable for some storm and catchment combinations where spatial effects on runoff response are evident. Assessment is then carried out in updating mode to emulate a real-time forecasting environment. First, a state updating form of the Grid Model is developed and then assessed against an ARMA error-prediction technique. Both
Energy Technology Data Exchange (ETDEWEB)
Narayana, P.B. [Green Life Energy Solutions LLP, Secunderabad (India); Rao, S.S. [National Institute of Technology. Dept. of Mechanical Engineering, Warangal (India); Reddy, K.H. [JNT Univ.. Dept. of Mechanical Engineering, Anantapur (India)
2012-07-01
Economics of wind power projects largely depend on the availability of wind power density. Wind resource assessment is a study estimating wind speeds and wind power densities in the region under consideration. The accuracy and reliability of data sets comprising of wind speeds and wind power densities at different heights per topographic region characterized by elevation or mean sea level, is important for wind power projects. Indian Wind Resource Assessment program conducted in 80's consisted of wind data measured by monitoring stations at different topographies in order to measure wind power density values at 25 and 50 meters above the ground level. In this paper, an attempt has been made to assess wind resource at a given location using artificial neural networks. Existing wind resource data has been used to train the neural networks. Location topography (characterized by longitude, latitude and mean sea level), air density, mean annual wind speed (MAWS) are used as inputs to the neural network. Mean annual wind power density (MAWPD) in watt/m{sup 2} is predicted for a new topographic location. Simple back propagation based neural network has been found to be sufficient for predicting these values with suitable accuracy. This model is closely linked to the problem of wind energy forecasting considering the variations of specific atmospheric variables with time horizons. This model will help the wind farm developers to have an initial estimation of the wind energy potential at a particular topography. (Author)
Bell, V. A.; Moore, R. J.
A practical methodology for distributed rainfall-runoff modelling using grid square weather radar data is developed for use in real-time flood forecasting. The model, called the Grid Model, is configured so as to share the same grid as used by the weather radar, thereby exploiting the distributed rainfall estimates to the full. Each grid square in the catchment is conceptualised as a storage which receives water as precipitation and generates water by overflow and drainage. This water is routed across the catchment using isochrone pathways. These are derived from a digital terrain model assuming two fixed velocities of travel for land and river pathways which are regarded as model parameters to be optimised. Translation of water between isochrones is achieved using a discrete kinematic routing procedure, parameterised through a single dimensionless wave speed parameter, which advects the water and incorporates diffusion effects through the discrete space-time formulation. The basic model routes overflow and drainage separately through a parallel system of kinematic routing reaches, characterised by different wave speeds but using the same isochrone-based space discretisation; these represent fast and slow pathways to the basin outlet, respectively. A variant allows the slow pathway to have separate isochrones calculated using Darcy velocities controlled by the hydraulic gradient as estimated by the local gradient of the terrain. Runoff production within a grid square is controlled by its absorption capacity which is parameterised through a simple linkage function to the mean gradient in the square, as calculated from digital terrain data. This allows absorption capacity to be specified differently for every grid square in the catchment through the use of only two regional parameters and a DTM measurement of mean gradient for each square. An extension of this basic idea to consider the distribution of gradient within the square leads analytically to a Pareto
Directory of Open Access Journals (Sweden)
V. A. Bell
1998-01-01
Full Text Available A practical methodology for distributed rainfall-runoff modelling using grid square weather radar data is developed for use in real-time flood forecasting. The model, called the Grid Model, is configured so as to share the same grid as used by the weather radar, thereby exploiting the distributed rainfall estimates to the full. Each grid square in the catchment is conceptualised as a storage which receives water as precipitation and generates water by overflow and drainage. This water is routed across the catchment using isochrone pathways. These are derived from a digital terrain model assuming two fixed velocities of travel for land and river pathways which are regarded as model parameters to be optimised. Translation of water between isochrones is achieved using a discrete kinematic routing procedure, parameterised through a single dimensionless wave speed parameter, which advects the water and incorporates diffusion effects through the discrete space-time formulation. The basic model routes overflow and drainage separately through a parallel system of kinematic routing reaches, characterised by different wave speeds but using the same isochrone-based space discretisation; these represent fast and slow pathways to the basin outlet, respectively. A variant allows the slow pathway to have separate isochrones calculated using Darcy velocities controlled by the hydraulic gradient as estimated by the local gradient of the terrain. Runoff production within a grid square is controlled by its absorption capacity which is parameterised through a simple linkage function to the mean gradient in the square, as calculated from digital terrain data. This allows absorption capacity to be specified differently for every grid square in the catchment through the use of only two regional parameters and a DTM measurement of mean gradient for each square. An extension of this basic idea to consider the distribution of gradient within the square leads analytically
DEFF Research Database (Denmark)
Hansen, Lisbet Sneftrup; Borup, Morten; Moller, Arne
2014-01-01
, and then evaluates and documents the performance of this particular updating procedure for flow forecasting. A hypothetical case study and synthetic observations are used to illustrate how the Update method works and affects downstream nodes. A real case study in a 544 ha urban catchment furthermore shows...
Real time flood forecasting in the Nan Basin, Thailand, by using a distributed Xin'anjiang Model
Chen, Xiaohong; Qiu, Xiaobin
2015-04-01
Taking Nan basin in Thailand as a research case, on the basis of DEM, this paper extracts the digital information of Nan basin and divides it into ten sub-basins, considering the land usage and terrain distribution, to construct the distributed Xinanjiang model. Before the model simulation, various digital basin information is established involving elevation matrix, river net matrix, direction matrix and so on. The three-water-source Xinanjiang model is adopted in the grids to calculate the runoff yield under a specific precipitation in grids, and then all the water flows of the grids are convoluted to the sub-basin's outlet to synthesize the runoff process of a subbasin. Eventually the subbasin runoff is routed to the basin outlet to obtain runoff process of the whole basin with real-time correction. The model parameters are calibrated by using the trial and error method. The sensitivity and uncertainty of the parameters are analyzed. The main achievements of this paper are as follows. (1) The basin information is extracted and the digital NAN basin is constructed on the basis of DEM data. As a result, a series of basin information matrix and digital Nan basin are generated. (2) The constant flow in grids and isochrones concept are used to replace the unit hydrograph of sub-basins. The basin discharge process is obtained through calculating the grid runoff yield and subbasin runoff convolution and routing the subbasin runoffs to the basin outlet. (3) The model is calibrated on more than 50 historical flood processes. The sensitivity and uncertainty of model parameters are analyzed by the perturbation analysis method, showing that some parameters, including KC, KKG, KKSS, WUM, KG, WLM, KSS and WDM are more sensitive. At the same time, the model uncertainty is analyed by the GLUE method and the results illustrate that the simulation effect depends on the values of parameter group while the observed runoff is in the uncertainty range. (4) The calculated discharge
Shrestha, D. L.; Robertson, D.; Bennett, J.; Ward, P.; Wang, Q. J.
2012-12-01
Through the water information research and development alliance (WIRADA) project, CSIRO is conducting research to improve flood and short-term streamflow forecasting services delivered by the Australian Bureau of Meteorology. WIRADA aims to build and test systems to generate ensemble flood and short-term streamflow forecasts with lead times of up to 10 days by integrating rainfall forecasts from Numerical Weather Prediction (NWP) models and hydrological modelling. Here we present an overview of the latest progress towards developing this system. Rainfall during the forecast period is a major source of uncertainty in streamflow forecasting. Ensemble rainfall forecasts are used in streamflow forecasting to characterise the rainfall uncertainty. In Australia, NWP models provide forecasts of rainfall and other weather conditions for lead times of up to 10 days. However, rainfall forecasts from Australian NWP models are deterministic and often contain systematic errors. We use a simplified Bayesian joint probability (BJP) method to post-process rainfall forecasts from the latest generation of Australian NWP models. The BJP method generates reliable and skilful ensemble rainfall forecasts. The post-processed rainfall ensembles are then used to force a semi-distributed conceptual rainfall runoff model to produce ensemble streamflow forecasts. The performance of the ensemble streamflow forecasts is evaluated on a number of Australian catchments and the benefits of using post processed rainfall forecasts are demonstrated.
A Forecast Model for Unemployment by Education
DEFF Research Database (Denmark)
Tranæs, Torben; Larsen, Anders Holm; Groes, Niels
1994-01-01
We present a dynamic forecast model for the labour market: demand for labour by education and the distribution of labour by education among industries are determined endogenously with overall demand by industry given exogenously. The model is derived from a simple behavioural equation based...... on a strong relationship between the “strength” in the struggle for jobs of an educational group, and the change in relative supply. This relationship proves to be significant in the data. Furthermore, when used to forecast employment by education on real data, the model predicts reasonably well even...... for educational groups, where the initial forecast year is a change point for unemployment....
Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Pons, Frederic; Moncoulon, David
2016-04-01
Hydrometeorological forecasting is an essential component of real-time flood management. The information it provides is of great help for crisis managers to anticipate the inundations and the associated risks. In the particular case of flash-floods, which may affect a large amount of small watersheds spread over the territory (up to 300 000 km of waterways considering a drained area of 5 km² minimum in France), appropriate flood forecasting systems are still under development. In France, highly distributed hydrological models have been implemented, enabling a real-time assessment of the potential intensity of flash-floods from the records of weather radars: AIGA-hydro system (Lavabre et al., 2005; Javelle et al., 2014), PreDiFlood project (Naulin et al., 2013). The approach presented here aims to go one step further by offering a direct assessment of the potential impacts of the simulated floods on inhabited areas. This approach is based on an a priori analysis of the study area in order (1) to evaluate with a simplified hydraulic approach (DTM treatment) the potentially flooded areas for different discharge levels, and (2) to identify the associated buildings and/or population at risk from geographic databases. This preliminary analysis enables to build an impact model (discharge-impact curve) on each river reach, which is then used to directly estimate the potentially affected assets based on a distributed rainfall runoff model. The overall principle of this approach was already presented at the 8th Hymex workshop. Therefore, the presentation will be here focused on the first validation results in terms of (1) accuracy of flooded areas simulated from DTM treatments, and (2) relevance of estimated impacts. The inundated areas simulated were compared to the European Directive cartography results (where available), showing an overall good correspondence in a large majority of cases, but also very significant errors for approximatively 10% of the river reaches
Directory of Open Access Journals (Sweden)
Christopher Bennett
2014-04-01
Full Text Available This paper set out to identify the significant variables which affect residential low voltage (LV network demand and develop next day total energy use (NDTEU and next day peak demand (NDPD forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX and neural network (NN techniques. The data used for this research was collected from a LV transformer serving 128 residential customers. It was observed that temperature accounted for half of the residential LV network demand. The inclusion of the double exponential smoothing algorithm, autoregressive terms, relative humidity and day of the week dummy variables increased model accuracy. In terms of R2 and for each modelling technique and phase, NDTEU hindcast accuracy ranged from 0.77 to 0.87 and forecast accuracy ranged from 0.74 to 0.84. NDPD hindcast accuracy ranged from 0.68 to 0.74 and forecast accuracy ranged from 0.56 to 0.67. The NDTEU models were more accurate than the NDPD models due to the peak demand time series being more variable in nature. The NN models had slight accuracy gains over the ARIMAX models. A hybrid model was developed which combined the best traits of the ARIMAX and NN techniques, resulting in improved hindcast and forecast fits across the all three phases.
Martínez, Brezo; Arenas, Francisco; Trilla, Alba; Viejo, Rosa M; Carreño, Francisco
2015-04-01
Species distribution models (SDM) are a useful tool for predicting species range shifts in response to global warming. However, they do not explore the mechanisms underlying biological processes, making it difficult to predict shifts outside the environmental gradient where the model was trained. In this study, we combine correlative SDMs and knowledge on physiological limits to provide more robust predictions. The thermal thresholds obtained in growth and survival experiments were used as proxies of the fundamental niches of two foundational marine macrophytes. The geographic projections of these species' distributions obtained using these thresholds and existing SDMs were similar in areas where the species are either absent-rare or frequent and where their potential and realized niches match, reaching consensus predictions. The cold-temperate foundational seaweed Himanthalia elongata was predicted to become extinct at its southern limit in northern Spain in response to global warming, whereas the occupancy of southern-lusitanic Bifurcaria bifurcata was expected to increase. Combined approaches such as this one may also highlight geographic areas where models disagree potentially due to biotic factors. Physiological thresholds alone tended to over-predict species prevalence, as they cannot identify absences in climatic conditions within the species' range of physiological tolerance or at the optima. Although SDMs tended to have higher sensitivity than threshold models, they may include regressions that do not reflect causal mechanisms, constraining their predictive power. We present a simple example of how combining correlative and mechanistic knowledge provides a rapid way to gain insight into a species' niche resulting in consistent predictions and highlighting potential sources of uncertainty in forecasted responses to climate change. © 2014 John Wiley & Sons Ltd.
Updating known distribution models for forecasting climate change impact on endangered species
National Research Council Canada - National Science Library
Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo
2013-01-01
To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change...
National Research Council Canada - National Science Library
Antonio-Román Muñoz; Ana Luz Márquez; Raimundo Real
2013-01-01
To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change...
Directory of Open Access Journals (Sweden)
D. E. Robertson
2013-05-01
Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.
Directory of Open Access Journals (Sweden)
D. E. Robertson
2013-09-01
Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short
Uncertainty Analysis of Multi-Model Flood Forecasts
Directory of Open Access Journals (Sweden)
Erich J. Plate
2015-12-01
Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.
Vivoni, E. R.; Grassotti, C.; Ivanov, V. Y.; Van Horne, M.; Bras, R. L.; Entekhabi, D.; Hoffman, R. N.
2001-12-01
The principal reasons motivating the use of meteorological radar for hydrologic modeling have been the potential for extending the spatial and temporal coverage of rainfall data as compared to sparse rain gauge networks. NEXRAD reflectivity measurements and derived rainfall products open the door to real-time availability of extensive rainfall coverage over watersheds in the United States. For hydrologic modeling purposes, the value of radar rainfall data is increased with the use of distributed hydrologic models capable of ingesting this new data source and taking full advantage of its spatial and temporal variability. This study presents preliminary results of applying a TIN-based distributed model with quantitative precipitation estimates (QPEs) and short-term quantitative precipitation forecasts (QPFs) derived from two radar rainfall products (operational Stage III estimates produced by the Arkansas-Red Basin River Forecast Center, and commercially available NOWrad estimates marketed by WSI, Inc.). Although both are based on NEXRAD reflectivity measurements, the NEXRAD Stage III and the WSI rainfall products can at times differ considerably in their estimation of the values and distribution of rainfall. Comparisons will be presented of the two radar rainfall products for a selected set of storm events in multiple basins within the Arkansas Red-River watershed. In addition, the difference in the forecasted rainfall fields (nowcasts product) derived from the MIT Lincoln Lab Storm Growth and Decay Model will be presented. Hydrologic modeling predictions from the use of the TIN-based, Real-time Integrated Basin Simulator (tRIBS) with the rainfall estimates and forecasts will be also be discussed in light of the differences in the rainfall inputs. Through this study, the strengths and/or weaknesses of two different radar rainfall sources and their corresponding short-term extrapolations will be highlighted as they relate to the interior hydrologic response and
Initial Distribution Spread: A density forecasting approach
Machete, Reason L
2012-01-01
Ensemble forecasting of nonlinear systems involves the use of a model to run forward a discrete ensemble (or set) of initial states. Data assimilation techniques tend to focus on estimating the true state of the system, even though model error limits the value of such efforts. This paper argues for choosing the initial ensemble in order to optimise forecasting performance rather than estimate the true state of the system. Density forecasting and choosing the initial ensemble are treated as one problem. Forecasting performance can be quantified by some scoring rule. In the case of the logarithmic scoring rule, theoretical arguments and empirical results are presented. It turns out that, if the underlying noise dominates model error, we can diagnose the noise spread.
Forecasting the Unit Cost of a Product with Some Linear Fuzzy Collaborative Forecasting Models
Directory of Open Access Journals (Sweden)
Toly Chen
2012-10-01
Full Text Available Forecasting the unit cost of every product type in a factory is an important task. However, it is not easy to deal with the uncertainty of the unit cost. Fuzzy collaborative forecasting is a very effective treatment of the uncertainty in the distributed environment. This paper presents some linear fuzzy collaborative forecasting models to predict the unit cost of a product. In these models, the experts’ forecasts differ and therefore need to be aggregated through collaboration. According to the experimental results, the effectiveness of forecasting the unit cost was considerably improved through collaboration.
DEFF Research Database (Denmark)
Lunde, Asger; Olesen, Kasper Vinther
We explore intraday transaction records from NASDAQ OMX Commodities Europe from January 2006 to October 2013. We analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH framework for the joint modeling of returns and realized ...
National Oceanic and Atmospheric Administration, Department of Commerce — 3D Marine Nowcast/Forecast System for the New York Bight NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.
Cleary, P. A.; Fuhrman, N.; Schulz, L.; Schafer, J.; Fillingham, J.; Bootsma, H.; McQueen, J.; Tang, Y.; Langel, T.; McKeen, S.; Williams, E. J.; Brown, S. S.
2015-05-01
Air quality forecast models typically predict large summertime ozone abundances over water relative to land in the Great Lakes region. While each state bordering Lake Michigan has dedicated monitoring systems, offshore measurements have been sparse, mainly executed through specific short-term campaigns. This study examines ozone abundances over Lake Michigan as measured on the Lake Express ferry, by shoreline differential optical absorption spectroscopy (DOAS) observations in southeastern Wisconsin and as predicted by the Community Multiscale Air Quality (CMAQ) model. From 2008 to 2009 measurements of O3, SO2, NO2 and formaldehyde were made in the summertime by DOAS at a shoreline site in Kenosha, WI. From 2008 to 2010 measurements of ambient ozone were conducted on the Lake Express, a high-speed ferry that travels between Milwaukee, WI, and Muskegon, MI, up to six times daily from spring to fall. Ferry ozone observations over Lake Michigan were an average of 3.8 ppb higher than those measured at shoreline in Kenosha, with little dependence on position of the ferry or temperature and with greatest differences during evening and night. Concurrent 1-48 h forecasts from the CMAQ model in the upper Midwestern region surrounding Lake Michigan were compared to ferry ozone measurements, shoreline DOAS measurements and Environmental Protection Agency (EPA) station measurements. The bias of the model O3 forecast was computed and evaluated with respect to ferry-based measurements. Trends in the bias with respect to location and time of day were explored showing non-uniformity in model bias over the lake. Model ozone bias was consistently high over the lake in comparison to land-based measurements, with highest biases for 25-48 h after initialization.
Directory of Open Access Journals (Sweden)
Z. Liu
2005-01-01
Full Text Available TOPKAPI is a physically-based, fully distributed hydrological model with a simple and parsimonious parameterisation. The original TOPKAPI is structured around five modules that represent evapotranspiration, snowmelt, soil water, surface water and channel water, respectively. Percolation to deep soil layers was ignored in the old version of the TOPKAPI model since it was not important in the basins to which the model was originally applied. Based on published literature, this study developed a new version of the TOPKAPI model, in which the new modules of interception, infiltration, percolation, groundwater flow and lake/reservoir routing are included. This paper presents an application study that makes a first attempt to derive information from public domains through the internet on the topography, soil and land use types for a case study Chinese catchment - the Upper Xixian catchment in Huaihe River with an area of about 10000 km2, and apply a new version of TOPKAPI to the catchment for flood simulation. A model parameter value adjustment was performed using six months of the 1998 dataset. Calibration did not use a curve fitting process, but was chiefly based upon moderate variations of parameter values from those estimated on physical grounds, as is common in traditional calibration. The hydrometeorological dataset of 2002 was then used to validate the model, both against the outlet discharge as well as at an internal gauging station. Finally, to complete the model performance analysis, parameter uncertainty and its effects on predictive uncertainty were also assessed by estimating a posterior parameter probability density via Bayesian inference.
de Almeida Bressiani, D.; Srinivasan, R.; Mendiondo, E. M.
2013-12-01
The use of distributed or semi-distributed models to represent the processes and dynamics of a watershed in the last few years has increased. These models are important tools to predict and forecast the hydrological responses of the watersheds, and they can subside disaster risk management and planning. However they usually have a lot of parameters, of which, due to the spatial and temporal variability of the processes, are not known, specially in developing countries; therefore a robust and sensible calibration is very important. This study conduced a sub-daily calibration and parameterization of the Soil & Water Assessment Tool (SWAT) for a 12,600 km2 watershed in southeast Brazil, and uses ensemble forecasts to evaluate if the model can be used as a tool for flood forecasting. The Piracicaba Watershed, in São Paulo State, is mainly rural, but has about 4 million of population in highly relevant urban areas, and three cities in the list of critical cities of the National Center for Natural Disasters Monitoring and Alerts. For calibration: the watershed was divided in areas with similar hydrological characteristics, for each of these areas one gauge station was chosen for calibration; this procedure was performed to evaluate the effectiveness of calibrating in fewer places, since areas with the same group of groundwater, soil, land use and slope characteristics should have similar parameters; making calibration a less time-consuming task. The sensibility analysis and calibration were performed on the software SWAT-CUP with the optimization algorithm: Sequential Uncertainly Fitting Version 2 (SUFI-2), which uses Latin hypercube sampling scheme in an iterative process. The performance of the models to evaluate the calibration and validation was done with: Nash-Sutcliffe efficiency coefficient (NSE), determination coefficient (r2), root mean square error (RMSE), and percent bias (PBIAS), with monthly average values of NSE around 0.70, r2 of 0.9, normalized RMSE of 0
Application of hydrologic forecast model.
Hua, Xu; Hengxin, Xue; Zhiguo, Chen
2012-01-01
In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional TSK (Takagi-Sugeno-Kang) fuzzy inference training, this paper attempts to consider the TSK fuzzy system modeling approach based on the visual system principle and the Weber law. This approach not only utilizes the strong capability of identifying objects of human eyes, but also considers the distribution structure of the training data set in parameter regulation. In order to overcome the shortcoming of it adopting the gradient learning algorithm with slow convergence rate, a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The main advantage of this method lies in its very good optimization, very strong noise immunity and very good interpretability. The new method is applied to long-term hydrological forecasting examples. The simulation results show that the method is feasible and effective, the new method not only inherits the advantages of traditional visual TSK fuzzy models but also has the better global convergence and accuracy than the traditional model.
Load forecasting method considering temperature effect for distribution network
Directory of Open Access Journals (Sweden)
Meng Xiao Fang
2016-01-01
Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.
Evaluation of statistical models for forecast errors from the HBV model
Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur
2010-04-01
SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.
Guidance on the Choice of Threshold for Binary Forecast Modeling
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
This paper proposes useful guidance on the choice of threshold for binary forecasts. In weather forecast systems, the probabilistic forecast cannot be used directly when estimated too smoothly. In this case, the binary forecast, whether a meteorological event will occur or not, is preferable to the probabilistic forecast.A threshold is needed to generate a binary forecast, and the guidance in this paper encompasses the use of skill scores for the choice of threshold according to the forecast pattern. The forecast pattern consists of distribution modes of estimated probabilities, occurrence rates of observations, and variation modes.This study is performed via Monte-Carlo simulation, with 48 forecast patterns considered. Estimated probabilities are generated by random variate sampling from five distributions separately. Varying the threshold from 0 to 1, binary forecasts are generated by threshold. For the assessment of binary forecast models, a 2×2 contingency table is used and four skill scores (Heidke skill score, hit rate, true skill statistic,and threat score) are compared for each forecast pattern. As a result, guidance on the choice of skill score to find the optimal threshold is proposed.
On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models
Fay, D; Ringwood, John; Condon, M.
2004-01-01
Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...
Modeling and Forecasting Volatility of the Malaysian Stock Markets
Directory of Open Access Journals (Sweden)
Ahmed Shamiri
2009-01-01
Full Text Available Problem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for volatility models. Approach: In this study, we used high-frequency to facilitate meaningful comparison of volatility forecast models. We compared the performance of symmetric GARCH, asymmetric EGARCH and non leaner asymmetric NAGARCH models with six error distributions (normal, skew normal, student-t, skew student-t, generalized error distribution and normal inverse Gaussian. Results: The results suggested that allowing for a heavy-tailed error distribution leads to significant improvements in variance forecasts compared to using normal distribution. It was also found that allowing for skewness in the higher moments of the distribution did not further improve forecasts. Conclusion: Successful volatility model forecast depended much more heavily on the choice of error distribution than the choice of GARCH models.
2016-03-01
TVCN consensus model comprised of at least two of: GFDI, AVNI, NGPI, UKMI, and GFNI xiv UKMET United Kingdom Meteorological UKMI interpolated UKMET... models : GFS (Global Forecast System), ECMWF (EC in the legend of Figure 3), UKMET (United Kingdom Meteorology ), FNMOC (Fleet Numerical Meteorology ...forecast GPCE value ( calculated using the CONU consensus model ) for a given forecast hour. The resulting terciles represented TC forecasts with low
Environmental forecasting and turbulence modeling
Hunt, J. C. R.
This review describes the fundamental assumptions and current methodologies of the two main kinds of environmental forecast; the first is valid for a limited period of time into the future and over a limited space-time ‘target’, and is largely determined by the initial and preceding state of the environment, such as the weather or pollution levels, up to the time when the forecast is issued and by its state at the edges of the region being considered; the second kind provides statistical information over long periods of time and/or over large space-time targets, so that they only depend on the statistical averages of the initial and ‘edge’ conditions. Environmental forecasts depend on the various ways that models are constructed. These range from those based on the ‘reductionist’ methodology (i.e., the combination of separate, scientifically based, models for the relevant processes) to those based on statistical methodologies, using a mixture of data and scientifically based empirical modeling. These are, as a rule, focused on specific quantities required for the forecast. The persistence and predictability of events associated with environmental and turbulent flows and the reasons for variation in the accuracy of their forecasts (of the first and second kinds) are now better understood and better modeled. This has partly resulted from using analogous results of disordered chaotic systems, and using the techniques of calculating ensembles of realizations, ideally involving several different models, so as to incorporate in the probabilistic forecasts a wider range of possible events. The rationale for such an approach needs to be developed. However, other insights have resulted from the recognition of the ordered, though randomly occurring, nature of the persistent motions in these flows, whose scales range from those of synoptic weather patterns (whether storms or ‘blocked’ anticyclones) to small scale vortices. These eigen states can be predicted
H.K. Preisler; R.E. Burgan; J.C. Eidenshink; J.M. Klaver; R.W. Klaver
2009-01-01
The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i)...
Modelling and Forecasting Multivariate Realized Volatility
DEFF Research Database (Denmark)
Halbleib, Roxana; Voev, Valeri
2011-01-01
This paper proposes a methodology for dynamic modelling and forecasting of realized covariance matrices based on fractionally integrated processes. The approach allows for flexible dependence patterns and automatically guarantees positive definiteness of the forecast. We provide an empirical appl...
Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint
Energy Technology Data Exchange (ETDEWEB)
Dall' Anese, Emiliano; Baker, Kyri; Summers, Tyler
2016-12-01
The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.
Optimal Power Flow for Distribution Systems under Uncertain Forecasts
Energy Technology Data Exchange (ETDEWEB)
Dall' Anese, Emiliano; Baker, Kyri; Summers, Tyler
2016-12-29
The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.
When mechanism matters: Bayesian forecasting using models of ecological diffusion
Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.
2017-01-01
Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.
Directory of Open Access Journals (Sweden)
D. Cane
2013-02-01
Full Text Available In this work, we compare the performance of an hydrological model when driven by probabilistic rain forecast derived from two different post-processing techniques. The region of interest is Piemonte, northwestern Italy, a complex orography area close to the Mediterranean Sea where the forecast are often a challenge for weather models. The May 2008 flood is here used as a case study, and the very dense weather station network allows us for a very good description of the event and initialization of the hydrological model. The ensemble probabilistic forecasts of the rainfall fields are obtained with the Bayesian model averaging, with the classical poor man ensemble approach and with a new technique, the Multimodel SuperEnsemble Dressing. In this case study, the meteo-hydrological chain initialized with the Multimodel SuperEnsemble Dressing is able to provide more valuable discharge ranges with respect to the one initialized with Bayesian model averaging multi-model.
Novel grey forecast model and its application
Institute of Scientific and Technical Information of China (English)
丁洪发; 舒双焰; 段献忠
2003-01-01
The advancement of grey system theory provides an effective analytic tool for power system load fore-cast. All kinds of presently available grey forecast models can be well used to deal with the short-term load fore-cast. However, they make big errors for medium or long-term load forecasts, and the load that does not satisfythe approximate exponential increasing law in particular. A novel grey forecast model that is capable of distin-guishing the increasing law of load is adopted to forecast electric power consumption (EPC) of Shanghai. Theresults show that this model can be used to greatly improve the forecast precision of EPC for a secondary industryor the whole society.
A Simple Fuzzy Time Series Forecasting Model
DEFF Research Database (Denmark)
Ortiz-Arroyo, Daniel
2016-01-01
In this paper we describe a new ﬁrst order fuzzy time series forecasting model. We show that our automatic fuzzy partitioning method provides an accurate approximation to the time series that when combined with rule forecasting and an OWA operator improves forecasting accuracy. Our model does...... not attempt to provide the best results in comparison with other forecasting methods but to show how to improve ﬁrst order models using simple techniques. However, we show that our ﬁrst order model is still capable of outperforming some more complex higher order fuzzy time series models....
Short-term load forecasting based on a multi-model
Energy Technology Data Exchange (ETDEWEB)
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)
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 ...
Evaluation Of Statistical Models For Forecast Errors From The HBV-Model
Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.
2009-04-01
Three statistical models for the forecast errors for inflow to the Langvatn reservoir in Northern Norway have been constructed and tested according to how well the distribution and median values of the forecasts errors fit to the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order autoregressive model was constructed for the forecast errors. The parameters were conditioned on climatic conditions. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order autoregressive model was constructed for the forecast errors. For the last model positive and negative errors were modeled separately. The errors were first NQT-transformed before a model where the mean values were conditioned on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: We wanted a) the median values to be close to the observed values; b) the forecast intervals to be narrow; c) the distribution to be correct. The results showed that it is difficult to obtain a correct model for the forecast errors, and that the main challenge is to account for the auto-correlation in the errors. Model 1 and 2 gave similar results, and the main drawback is that the distributions are not correct. The 95% forecast intervals were well identified, but smaller forecast intervals were over-estimated, and larger intervals were under-estimated. Model 3 gave a distribution that fits better, but the median values do not fit well since the auto-correlation is not properly accounted for. If the 95% forecast interval is of interest, Model 2 is recommended. If the whole distribution is of interest, Model 3 is recommended.
Forecasting with nonlinear time series models
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Teräsvirta, Timo
and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...... applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic...
Wind Power Forecasting Error Distributions: An International Comparison; Preprint
Energy Technology Data Exchange (ETDEWEB)
Hodge, B. M.; Lew, D.; Milligan, M.; Holttinen, H.; Sillanpaa, S.; Gomez-Lazaro, E.; Scharff, R.; Soder, L.; Larsen, X. G.; Giebel, G.; Flynn, D.; Dobschinski, J.
2012-09-01
Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations.
Stochastic model of forecasting spare parts demand
Directory of Open Access Journals (Sweden)
Ivan S. Milojević
2012-01-01
hypothesis of the existence of phenomenon change trends, the next step in the methodology of forecasting is the determination of a specific growth curve that describes the regularity of the development in time. These curves of growth are obtained by the analytical representation (expression of dynamic lines. There are two basic stages in the process of expression and they are: - The choice of the type of curve the shape of which corresponds to the character of the dynamic order variation - the determination of the number of values (evaluation of the curve parameters. The most widespread method of forecasting is the trend extrapolation. The basis of the trend extrapolation is the continuing of past trends in the future. The simplicity of the trend extrapolation process, on the one hand, and the absence of other information on the other hand, are the main reasons why the trend extrapolation is used for forecasting. The trend extrapolation is founded on the following assumptions: - The phenomenon development can be presented as an evolutionary trajectory or trend, - General conditions that influenced the trend development in the past will not undergo substantial changes in the future. Spare parts demand forecasting is constantly being done in all warehouses, workshops, and at all levels. Without demand forecasting, neither planning nor decision making can be done. Demand forecasting is the input for determining the level of reserve, size of the order, ordering cycles, etc. The question that arises is the one of the reliability and accuracy of a forecast and its effects. Forecasting 'by feeling' is not to be dismissed if there is nothing better, but in this case, one must be prepared for forecasting failures that cause unnecessary accumulation of certain spare parts, and also a chronic shortage of other spare parts. All this significantly increases costs and does not provide a satisfactory supply of spare parts. The main problem of the application of this model is that each
DEFF Research Database (Denmark)
Quinonero, Joaquin; Girard, Agathe; Larsen, Jan
2003-01-01
The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaus....... The capability of the method is demonstrated for forecasting of time-series and compared to approximate methods.......The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models...... such as the Gaussian process and the relevance vector machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting...
Institute of Scientific and Technical Information of China (English)
Xiaoling; HAO; Ruixia; SUO
2015-01-01
Agricultural machinery total power is an important index to reflect and evaluate the level of agricultural mechanization. Firstly,we respectively made use of exponential model,grey forecasting and BP neural network to construct models depending on historical data of agricultural machinery total power of Heilongjiang Province; secondly,we constructed the combined forecasting models that respectively based on divergence coefficient method,quadratic programming and weight distribution of Shapley value. Fitting results showed that the various combination forecasting model is superior to the single models. Finally,we applied the combination forecasting model which based on the weight distribution method of Shapley value to forecast Heilongjiang agricultural machinery total power,and it would provide some reference to the development and program for power of agriculture machinery.
Pollen Forecast and Dispersion Modelling
Costantini, Monica; Di Giuseppe, Fabio; Medaglia, Carlo Maria; Travaglini, Alessandro; Tocci, Raffaella; Brighetti, M. Antonia; Petitta, Marcello
2014-05-01
The aim of this study is monitoring, mapping and forecast of pollen distribution for the city of Rome using in-situ measurements of 10 species of common allergenic pollens and measurements of PM10. The production of daily concentration maps, associated to a mobile phone app, are innovative compared to existing dedicated services to people who suffer from respiratory allergies. The dispersal pollen is one of the most well-known causes of allergic disease that is manifested by disorders of the respiratory functions. Allergies are the third leading cause of chronic disease and it is estimated that tens millions of people in Italy suffer from it. Recent works reveal that during the last few years there was a progressive increase of affected subjects, especially in urban areas. This situation may depend: on the ability to transport of pollutants, on the ability to react between pollutants and pollen and from a combination of other irritants, existing in densely populated and polluted urban areas. The methodology used to produce maps is based on in-situ measurements time series relative to 2012, obtained from networks of air quality and pollen stations in the metropolitan area of Rome. The monitoring station aerobiological of University of Rome "Tor Vergata" is located at the Department of Biology. The instrument used to pollen monitoring is a volumetric sampler type Hirst (Hirst 1952), Model 2000 VPPS Lanzoni; the data acquisition is carried out as reported in Standard UNI 11008:2004 - "Qualità dell'aria - Metodo di campionamento e conteggio dei granuli pollinici e delle spore fungine aerodisperse" - the protocol that describes the procedure for measuring of the concentration of pollen grains and fungal spores dispersed into the atmosphere, and reported in the "Manuale di gestione e qualità della R.I.M.A" (Travaglini et. al. 2009). All 10 allergenic pollen are monitored since 1996. At Tor Vergata university is also operating a meteorological station (SP2000, CAE
Forecasting with periodic autoregressive time series models
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1999-01-01
textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption
Forecasting with periodic autoregressive time series models
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1999-01-01
textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption
Coupling meteorological and hydrological models for flood forecasting
Directory of Open Access Journals (Sweden)
Bartholmes
2005-01-01
Full Text Available This paper deals with the problem of analysing the coupling of meteorological meso-scale quantitative precipitation forecasts with distributed rainfall-runoff models to extend the forecasting horizon. Traditionally, semi-distributed rainfall-runoff models have been used for real time flood forecasting. More recently, increased computer capabilities allow the utilisation of distributed hydrological models with mesh sizes from tenths of metres to a few kilometres. On the other hand, meteorological models, providing the quantitative precipitation forecast, tend to produce average values on meshes ranging from slightly less than 10 to 200 kilometres. Therefore, to improve the quality of flood forecasts, the effects of coupling the meteorological and the hydrological models at different scales were analysed. A distributed hydrological model (TOPKAPI was developed and calibrated using a 1x1 km mesh for the case of the river Po closed at Ponte Spessa (catchment area c. 37000 km2. The model was then coupled with several other European meteorological models ranging from the Limited Area Models (provided by DMI and DWD with resolutions from 0.0625° * 0.0625°, to the ECMWF ensemble predictions with a resolution of 1.85° * 1.85°. Interesting results, describing the coupled model behaviour, are available for a meteorological extreme event in Northern Italy (Nov. 1994. The results demonstrate the poor reliability of the quantitative precipitation forecasts produced by meteorological models presently available; this is not resolved using the Ensemble Forecasting technique, when compared with results obtainable with measured rainfall.
PETRA. The Forecast Model. Synthesis report
Energy Technology Data Exchange (ETDEWEB)
NONE
1998-09-01
The aim of the PETRA project was to develop a model that could recreate the main aspects involved in the demand for travel. The attainment of this objective requires that the model system should retain a high degree of detail and be based on disaggregate models. This was both to ensure an accurate representation of the underlying behavioural intentions, and allow analysis of the underlying travel demand and related aspects across a number of dimensions. This has been achieved in all main respects. The model system is capable of close reproduction of the observed behaviour and generally responds as expected to changes, exhibiting consistent and plausible reactions. The dis-aggregation of the forecast population, according to the various criteria, allows the model to clearly illustrates the behavioural differences between different population segments. Thus, it seems reasonable to conclude that PETRA is capable of detailed analyses of the distributional and behavioural effects of policy changes. (au) EFP-94. 20 refs.
Mohammed, Touseef Ahmed Faisal
Distribution System Simulator developed by Electric Power Research Institute, to simulate grid voltage profile with a large scale PV system under quasi-static time series considering variations of PV output in seconds, minutes, and the average daily load variations. A 13 bus IEEE distribution feeder model is utilized with distributed residential and commercial scale PV at different buses for simulation studies. Time series simulations are discussed for various modes of operation considering dynamic PV penetration at different time periods in a day. In addition, this thesis demonstrates simulations taking into account the presence of moving cloud for solar forecasting studies.
Network Bandwidth Utilization Forecast Model on High Bandwidth Network
Energy Technology Data Exchange (ETDEWEB)
Yoo, Wucherl; Sim, Alex
2014-07-07
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Modelling and forecasting WIG20 daily returns
DEFF Research Database (Denmark)
Amado, Cristina; Silvennoinen, Annestiina; Terasvirta, Timo
of the model is that the deterministic component is specified before estimating the multiplicative conditional variance component. The resulting model is subjected to misspecification tests and its forecasting performance is compared with that of commonly applied models of conditional heteroskedasticity....
Midway Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Midway Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite...
Bermuda Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Bermuda Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
Introducing distributed learning approaches in wind power forecasting
Pinson, Pierre
2016-01-01
Renewable energy forecasting is now of core interest to both academics, who continuously propose new forecast methodologies, and forecast users for optimal operations and participation in electricity markets. In view of the increasing amount of data being collected at power generation sites, thanks to substantial deployment of generating capacities and increased temporal resolution, it may now be possible to build large models accounting for all space-time dependencies. This will eventually a...
Operational, regional-scale, chemical weather forecasting models in Europe
Kukkonen, J.; Balk, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.H.; Poupkou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, K.
2011-01-01
Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed
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.
Grey-Markov Model for Road Accidents Forecasting
Institute of Scientific and Technical Information of China (English)
李相勇; 严余松; 蒋葛夫
2003-01-01
In order to improve the forecasting precision of road accidents, by introducing Markov chains forecasting method, a grey-Markov model for forecasting road accidents is established based on grey forecasting method. The model combines the advantages of both grey forecasting method and Markov chains forecasting method, overcomes the influence of random fluctuation data on forecasting precision and widens the application scope of the grey forecasting. An application example is conducted to evaluate the grey-Markov model, which shows that the precision of the grey-Markov model is better than that of grey model in forecasting road accidents.
Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics
Scheuerer, Michael
2013-01-01
Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method that generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS). We model precipitation amounts by a generalized extreme value distribution that is left-censored at zero. This distribution permits modelling precipitation on the original scale without prior transformation of the data. A closed form expression for its continuous rank probability score can be derived and permits computationally efficient model fitting. We discuss an extension of our approach that incorporates further statistics characterizing the spatial variability of precipitation amounts in the vicinity of the location of interest. The proposed EMOS method is applied to daily 18-h forecasts of 6-h accumulated precipitation over Germany in 2011 using the COSMO-DE ensemble prediction system operated by the Germa...
Forecasting elections in Europe: Synthetic models
Directory of Open Access Journals (Sweden)
Michael S. Lewis-Beck
2015-01-01
Full Text Available Scientific work on national election forecasting has become most developed for the United States case, where three dominant approaches can be identified: Structuralists, Aggregators, and Synthesizers. For European cases, election forecasting models remain almost exclusively Structuralist. Here we join together structural modeling and aggregate polling results, to form a hybrid, which we label a Synthetic Model. This model contains a political economy core, to which poll numbers are added (to tap omitted variables. We apply this model to a sample of three Western European countries: Germany, Ireland, and the United Kingdom. This combinatory strategy appears to offer clear forecasting gains, in terms of lead and accuracy.
Econometric Models for Forecasting of Macroeconomic Indices
Sukhanova, Elena I.; Shirnaeva, Svetlana Y.; Mokronosov, Aleksandr G.
2016-01-01
The urgency of the research topic was stipulated by the necessity to carry out an effective controlled process by the economic system which can hardly be imagined without indices forecasting characteristic of this system. An econometric model is a safe tool of forecasting which makes it possible to take into consideration the trend of indices…
NAVO NCOM Relocatable Model: Fukushima Regional Forecast
National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary NCOM Relocatable 1km forecast model for Fukushima Region. USERS ARE REMINDED TO USE THE FUKUSHIMA 1KM NCOM DATA WITH CAUTION. THE MODEL WAS INITIATED ON...
Modelling and Forecasting Multivariate Realized Volatility
DEFF Research Database (Denmark)
Chiriac, Roxana; Voev, Valeri
This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions....... We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies...
Comparison of Wind Power and Load Forecasting Error Distributions: Preprint
Energy Technology Data Exchange (ETDEWEB)
Hodge, B. M.; Florita, A.; Orwig, K.; Lew, D.; Milligan, M.
2012-07-01
The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent System Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.
The Joint Calibration Model in probabilistic weather forecasting: some preliminary issues
Directory of Open Access Journals (Sweden)
Patrizia Agati
2013-05-01
Full Text Available Ensemble Prediction Systems play today a fundamental role in weather forecasting. They can represent and measure uncertainty, thereby allowing distributional forecasting as well as deterministic-style forecasts. In this context, we show how the Joint Calibration Model (Agati et al., 2007 – based on a modelization of the Probability Integral Transform distribution – can provide a solution to the problem of information combining in probabilistic forecasting of continuous variables. A case study is presented, where the potentialities of the method are explored and the accuracy of deterministic-style forecasts from JCM is compared with that from Bayesian Model Averaging (Raftery et al., 2005.
Forecasting elections in Europe: Synthetic models
Michael S. Lewis-Beck; Ruth Dassonneville
2015-01-01
Scientific work on national election forecasting has become most developed for the United States case, where three dominant approaches can be identified: Structuralists, Aggregators, and Synthesizers. For European cases, election forecasting models remain almost exclusively Structuralist. Here we join together structural modeling and aggregate polling results, to form a hybrid, which we label a Synthetic Model. This model contains a political economy core, to which poll numbers are added (to ...
Tide forecasting method based on dynamic weight distribution for operational evaluation
Institute of Scientific and Technical Information of China (English)
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.
Combining SKU-level sales forecasts from models and experts
Ph.H.B.F. Franses (Philip Hans); R. Legerstee (Rianne)
2009-01-01
textabstractWe study the performance of SKU-level sales forecasts which linearly combine statistical model forecasts and expert forecasts. Using a large and unique database containing model forecasts for monthly sales of various pharmaceutical products and forecasts given by about fifty experts, we
Directory of Open Access Journals (Sweden)
P. A. Cleary
2014-09-01
Full Text Available Air quality forecast models typically predict large ozone abundances over water relative to land in the Great Lakes region. While each state bordering Lake Michigan has dedicated monitoring systems, offshore measurements have been sparse, mainly executed through specific short-term campaigns. This study examines ozone abundances over Lake Michigan as measured on the Lake Express ferry, by shoreline Differential Optical Absorption Spectroscopy (DOAS observations in southeastern Wisconsin, and as predicted by the National Air Quality Forecast System. From 2008–2009 measurements of O3, SO2, NO2 and formaldehyde were made in the summertime by DOAS at a shoreline site in Kenosha, WI. From 2008–2010 measurements of ambient ozone conducted on the Lake Express, a high-speed ferry that travels between Milwaukee, WI and Muskegon, MI up to 6 times daily from spring to fall. Ferry ozone observations over Lake Michigan were an average of 3.8 ppb higher than those measured at shoreline in Kenosha with little dependence on position of the ferry or temperature but with highest differences during evening and night. Concurrent ozone forecast images from National Weather System's National Air Quality Forecast System in the upper Midwestern region surrounding Lake Michigan were saved over the ferry ozone sampling period in 2009. The bias of the model O3 forecast was computed and evaluated with respect to ferry-based measurements. The model 1 and 8 h ozone mean biases were both 12 ppb higher than observed ozone, and maximum daily 1 h ozone mean bias was 10 ppb, indicating substantial ozone over-prediction over water. Trends in the bias with respect to location and time of day or month were also explored showing non-uniformity in model bias. Extreme ozone events were predicted by the model but not observed by ferry measurements.
Cleary, P. A.; Fuhrman, N.; Schulz, L.; Schafer, J.; Fillingham, J.; Bootsma, H.; Langel, T.; Williams, E. J.; Brown, S. S.
2014-09-01
Air quality forecast models typically predict large ozone abundances over water relative to land in the Great Lakes region. While each state bordering Lake Michigan has dedicated monitoring systems, offshore measurements have been sparse, mainly executed through specific short-term campaigns. This study examines ozone abundances over Lake Michigan as measured on the Lake Express ferry, by shoreline Differential Optical Absorption Spectroscopy (DOAS) observations in southeastern Wisconsin, and as predicted by the National Air Quality Forecast System. From 2008-2009 measurements of O3, SO2, NO2 and formaldehyde were made in the summertime by DOAS at a shoreline site in Kenosha, WI. From 2008-2010 measurements of ambient ozone conducted on the Lake Express, a high-speed ferry that travels between Milwaukee, WI and Muskegon, MI up to 6 times daily from spring to fall. Ferry ozone observations over Lake Michigan were an average of 3.8 ppb higher than those measured at shoreline in Kenosha with little dependence on position of the ferry or temperature but with highest differences during evening and night. Concurrent ozone forecast images from National Weather System's National Air Quality Forecast System in the upper Midwestern region surrounding Lake Michigan were saved over the ferry ozone sampling period in 2009. The bias of the model O3 forecast was computed and evaluated with respect to ferry-based measurements. The model 1 and 8 h ozone mean biases were both 12 ppb higher than observed ozone, and maximum daily 1 h ozone mean bias was 10 ppb, indicating substantial ozone over-prediction over water. Trends in the bias with respect to location and time of day or month were also explored showing non-uniformity in model bias. Extreme ozone events were predicted by the model but not observed by ferry measurements.
Modeling olive-crop forecasting in Tunisia
Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji
2016-01-01
Tunisia is the world's second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia's five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993-2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.
Aerosol Radiative Forcing and Weather Forecasts in the ECMWF Model
Bozzo, A.; Benedetti, A.; Rodwell, M. J.; Bechtold, P.; Remy, S.
2015-12-01
Aerosols play an important role in the energy balance of the Earth system via direct scattering and absorpiton of short-wave and long-wave radiation and indirect interaction with clouds. Diabatic heating or cooling by aerosols can also modify the vertical stability of the atmosphere and influence weather pattern with potential impact on the skill of global weather prediction models. The Copernicus Atmosphere Monitoring Service (CAMS) provides operational daily analysis and forecast of aerosol optical depth (AOD) for five aerosol species using a prognostic model which is part of the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS). The aerosol component was developed during the research project Monitoring Atmospheric Composition and Climate (MACC). Aerosols can have a large impact on the weather forecasts in case of large aerosol concentrations as found during dust storms or strong pollution events. However, due to its computational burden, prognostic aerosols are not yet feasible in the ECMWF operational weather forecasts, and monthly-mean climatological fields are used instead. We revised the aerosol climatology used in the operational ECMWF IFS with one derived from the MACC reanalysis. We analyse the impact of changes in the aerosol radiative effect on the mean model climate and in medium-range weather forecasts, also in comparison with prognostic aerosol fields. The new climatology differs from the previous one by Tegen et al 1997, both in the spatial distribution of the total AOD and the optical properties of each aerosol species. The radiative impact of these changes affects the model mean bias at various spatial and temporal scales. On one hand we report small impacts on measures of large-scale forecast skill but on the other hand details of the regional distribution of aerosol concentration have a large local impact. This is the case for the northern Indian Ocean where the radiative impact of the mineral
Nambe Pueblo Water Budget and Forecasting model.
Energy Technology Data Exchange (ETDEWEB)
Brainard, James Robert
2009-10-01
This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.
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.
Modelling and forecasting Australian domestic tourism
2006-01-01
In this paper, we model and forecast Australian domestic tourism demand. We use a regression framework to estimate important economic relationships for domestic tourism demand. We also identify the impact of world events such as the 2000 Sydney Olympics and the 2002 Bali bombings on Australian domestic tourism. To explore the time series nature of the data, we use innovation state space models to forecast the domestic tourism demand. Combining these two frameworks, we build innovation state s...
Hydrological model calibration for enhancing global flood forecast skill
Hirpa, Feyera A.; Beck, Hylke E.; Salamon, Peter; Thielen-del Pozo, Jutta
2016-04-01
Early warning systems play a key role in flood risk reduction, and their effectiveness is directly linked to streamflow forecast skill. The skill of a streamflow forecast is affected by several factors; among them are (i) model errors due to incomplete representation of physical processes and inaccurate parameterization, (ii) uncertainty in the model initial conditions, and (iii) errors in the meteorological forcing. In macro scale (continental or global) modeling, it is a common practice to use a priori parameter estimates over large river basins or wider regions, resulting in suboptimal streamflow estimations. The aim of this work is to improve flood forecast skill of the Global Flood Awareness System (GloFAS; www.globalfloods.eu), a grid-based forecasting system that produces flood forecast unto 30 days lead, through calibration of the distributed hydrological model parameters. We use a combination of in-situ and satellite-based streamflow data for automatic calibration using a multi-objective genetic algorithm. We will present the calibrated global parameter maps and report the forecast skill improvements achieved. Furthermore, we discuss current challenges and future opportunities with regard to global-scale early flood warning systems.
Brodie, Stephanie; Hobday, Alistair J.; Smith, James A.; Spillman, Claire M.; Hartog, Jason R.; Everett, Jason D.; Taylor, Matthew D.; Gray, Charles A.; Suthers, Iain M.
2017-06-01
Seasonal forecasting of environmental conditions and marine species distribution has been used as a decision support tool in commercial and aquaculture fisheries. These tools may also be applicable to species targeted by the recreational fisheries sector, a sector that is increasing its use of marine resources, and making important economic and social contributions to coastal communities around the world. Here, a seasonal forecast of the habitat and density of dolphinfish (Coryphaena hippurus), based on sea surface temperatures, was developed for the east coast of New South Wales (NSW), Australia. Two prototype forecast products were created; geographic spatial forecasts of dolphinfish habitat and a latitudinal summary identifying the location of fish density peaks. The less detailed latitudinal summary was created to limit the resolution of habitat information to prevent potential resource over-exploitation by fishers in the absence of total catch controls. The forecast dolphinfish habitat model was accurate at the start of the annual dolphinfish migration in NSW (December) but other months (January - May) showed poor performance due to spatial and temporal variability in the catch data used in model validation. Habitat forecasts for December were useful up to five months ahead, with performance decreasing as forecast were made further into the future. The continued development and sound application of seasonal forecasts will help fishery industries cope with future uncertainty and promote dynamic and sustainable marine resource management.
The AviaDem forecasting model: illustration of a forecasting case at Amsterdam Schiphol Airport
Veldhuis, J.; Lieshout, R.
2010-01-01
The paper describes an aviation market forecasting model which focuses on market forecasts for airports. Most forecasting models in use today assess aviation trends resulting from macroeconomic trends. The model described in this paper has this feature built in, but the added value of this model is
Directory of Open Access Journals (Sweden)
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.
Modeling and forecasting petroleum futures volatility
Energy Technology Data Exchange (ETDEWEB)
Sadorsky, Perry [York Univ., Schulich School of Business, Toronto, ON (Canada)
2006-07-15
Forecasts of oil price volatility are important inputs into macroeconometric models, financial market risk assessment calculations like value at risk, and option pricing formulas for futures contracts. This paper uses several different univariate and multivariate statistical models to estimate forecasts of daily volatility in petroleum futures price returns. The out-of-sample forecasts are evaluated using forecast accuracy tests and market timing tests. The TGARCH model fits well for heating oil and natural gas volatility and the GARCH model fits well for crude oil and unleaded gasoline volatility. Simple moving average models seem to fit well in some cases provided the correct order is chosen. Despite the increased complexity, models like state space, vector autoregression and bivariate GARCH do not perform as well as the single equation GARCH model. Most models out perform a random walk and there is evidence of market timing. Parametric and non-parametric value at risk measures are calculated and compared. Non-parametric models outperform the parametric models in terms of number of exceedences in backtests. These results are useful for anyone needing forecasts of petroleum futures volatility. (author)
Introducing distributed learning approaches in wind power forecasting
DEFF Research Database (Denmark)
Pinson, Pierre
2016-01-01
Renewable energy forecasting is now of core interest to both academics, who continuously propose new forecast methodologies, and forecast users for optimal operations and participation in electricity markets. In view of the increasing amount of data being collected at power generation sites, thanks...... to substantial deployment of generating capacities and increased temporal resolution, it may now be possible to build large models accounting for all space-time dependencies. This will eventually allow to significantly improve the quality of short-term renewable power forecasts. However, in practice, it is often...... to ever share any data, by decomposing the original large learning problem into a number of small learning problems that can be solved in a decentralized manner. As an example, emphasis is placed on Lasso-type estimation of autoregressive models with offsite observations. Different applications on medium...
A Bayesian Combination Forecasting Model for Retail Supply Chain Coordination
Directory of Open Access Journals (Sweden)
W.J. Wang
2014-04-01
Full Text Available Retailing plays an important part in modern economic development, and supply chain coordination is the research focus in retail operations management. This paper reviews the collaborative forecasting process within the framework of the collaborative planning, forecasting and replenishment of retail supply chain. A Bayesian combination forecasting model is proposed to integrate multiple forecasting resources and coordinate forecasting processes among partners in the retail supply chain. Based on simulation results for retail sales, the effectiveness of this combination forecasting model is demonstrated for coordinating the collaborative forecasting processes, resulting in an improvement of demand forecasting accuracy in the retail supply chain.
Meteoroid Environment Modeling: the Meteoroid Engineering Model and Shower Forecasting
Moorhead, Althea V.
2017-01-01
The meteoroid environment is often divided conceptually into meteor showers plus a sporadic background component. The sporadic complex poses the bulk of the risk to spacecraft, but showers can produce significant short-term enhancements of the meteoroid flux. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. Both MEM and the forecast are used by multiple manned spaceflight projects in their meteoroid risk evaluation, and both tools are being revised to incorporate recent meteor velocity, density, and timing measurements. MEM describes the sporadic meteoroid complex and calculates the flux, speed, and directionality of the meteoroid environment relative to a user-supplied spacecraft trajectory, taking the spacecraft's motion into account. MEM is valid in the inner solar system and offers near-Earth and cis-lunar environments. While the current version of MEM offers a nominal meteoroid environment corresponding to a single meteoroid bulk density, the next version of MEMR3 will offer both flux uncertainties and a density distribution in addition to a revised near-Earth environment. We have updated the near-Earth meteor speed distribution and have made the first determination of uncertainty in this distribution. We have also derived a meteor density distribution from the work of Kikwaya et al. (2011). The annual meteor shower forecast takes the form of a report and data tables that can be used in conjunction with an existing MEM assessment. Fluxes are typically quoted to a constant limiting kinetic energy in order to comport with commonly used ballistic limit equations. For the 2017 annual forecast, the MEO substantially revised the list of showers and their characteristics using 14 years of meteor flux measurements from the Canadian Meteor Orbit Radar (CMOR). Defunct or insignificant showers were removed and the temporal profiles of many showers
Semi-distributed flood forecasting system for the Middle Vistula reach
Romanowicz, Renata; Karamuz, Emilia; Osuch, Marzena
2014-05-01
The aim of this study is the development of an integrated forecasting system for the middle reach of the River Vistula. The system consists of combined in series lumped parameter Stochastic Transfer Function models. In order to prolong the forecast lead-time, the system was extended to include gauging stations situated upstream of Zawichost. There is a number of tributaries located along the studied reach. The largest are Kamienna, Pilica and Wieprz. Therefore apart from Single- Input -Single-Output models (SISO), multiple input models were also developed (MISO). The system is based on water levels instead of flows, in order to avoid errors related to rating curve transformation. The problem of the nonlinear transformation of system inputs in order to separate the nonlinearity of the flow process to obtain the linear model dynamics is equally important for the accuracy of forecasts. The possibility of linearizing the flow routing process was investigated using a State Dependent Parameter approach. The nonparametric relationship was parameterised using a power function. This procedure allowed the application of a model with a nonlinear transformation of input in the forecasting mode. It is important to note that the applied methods are stochastic in nature and the structure of the models and their parameters are estimated from available observations, taking into account inherent observation and model approximation errors. As a result, forecasts are estimated together with uncertainty bands. We apply a Kalman filter updating of model predictions as a data assimilation procedure. The procedure involves formulating the forecasting problem in a state space form. Validation of the developed forecasting system shows that the quality of forecasts obtained using a semi-distributed lumped parameter model is comparable with the forecasts obtained using a distributed model with the advantage of obtaining forecast uncertainty by the former. This work was supported by the
Modelling and Forecasting Multivariate Realized Volatility
DEFF Research Database (Denmark)
Chiriac, Roxana; Voev, Valeri
. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies...
Forecasting Models in the State Education System
Directory of Open Access Journals (Sweden)
Gintautas DZEMYDA
2003-04-01
Full Text Available This paper presents model-based assessment and forecasting of the Lithuanian education system in the period of 2001-2010. In order to obtain satisfactory forecasting results, constructing of models used for these aims should be grounded on some interactive data mining. Data mining of data stored in the system of the Lithuanian teacher's database and of data from other sources representing the state of education system and the demographic changes in Lithuania was used. The models cover the estimation of data quality in the databases, the analysis of flow of teachers and pupils, the clustering of schools, the model of dynamics of pedagogical staff and pupils, and the quality analysis of teachers. The main results of forecasting and integrated analysis of the Lithuanian teachers' database with other data reflecting the state of the education system and demographic changes in Lithuania are presented.
Institute of Scientific and Technical Information of China (English)
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.
Forecasting Exchange Rates with Mixed Models
Directory of Open Access Journals (Sweden)
Laura Maria Badea
2013-06-01
Full Text Available Gaining accuracy in exchange rate forecasting applications provides true benefits for financial activities. Supported today by the advancements in computing power, machine learning techniques provide good alternatives to traditional time series estimation methods. Very approached in time series forecasting are Artificial Neural Networks (ANNs which offer robust results and allow a flexible data manipulation. When integrating both, the “white-box” feature of conventional methods and the complexity of machine learning techniques, forecasting models perform even better in terms of generated errors. In this study, input variables (independent variables are selected using an ARIMA technique and are further employed in differently configured multilayered feed-forward neural networks using Broyden-Fletcher-Goldfarb-Shanno (BFGS optimization algorithm to perform predictions on EUR/RON and CHF/RON exchange rates. Results in terms of mean squared error highlight good results when using mixed models.
An Econometric Model for Forecasting Income and Employment in Hawaii.
Chau, Laurence C.
This report presents the methodology for short-run forecasting of personal income and employment in Hawaii. The econometric model developed in the study is used to make actual forecasts through 1973 of income and employment, with major components forecasted separately. Several sets of forecasts are made, under different assumptions on external…
Forecasting Workload for Defense Logistics Agency Distribution
2014-12-01
methods” (Orchowsky, Kirchoff, Rider, & Kem, 1986, p. 7). An example of a simple technique is exponential smoothing. A 2006 summary by Gardner of all...AGENCY DISTRIBUTION 5. FUNDING NUMBERS 6. AUTHOR(S) Aaron W. Chonko, Padraic T. Heiliger, and Travis W. Rudge 7. PERFORMING ORGANIZATION NAME(S...AND ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943-5000 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING
Preisler, H.K.; Burgan, R.E.; Eidenshink, J.C.; Klaver, Jacqueline M.; Klaver, R.W.
2009-01-01
The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i) number of ignitions; (ii) number of fires above a given size; (iii) conditional probabilities of fires greater than a specified size, given ignition. As an illustration, we used the methods to study the skill of the Fire Potential Index an index that incorporates satellite and surface observations to map fire potential at a national scale in forecasting distributions of large fires. ?? 2009 IAWF.
Towards Disaggregate Dynamic Travel Forecasting Models
Institute of Scientific and Technical Information of China (English)
Moshe Ben-Akiva; Jon Bottom; Song Gao; Haris N. Koutsopoulos; Yang Wen
2007-01-01
The authors argue that travel forecasting models should be dynamic and disaggregate in their representation of demand, supply, and supply-demand interactions, and propose a framework for such models.The proposed framework consists of disaggregate activity-based representation of travel choices of individual motorists on the demand side integrated with disaggregate dynamic modeling of network performance,through vehicle-based traffic simulation models on the supply side. The demand model generates individual members of the population and assigns to them socioeconomic characteristics. The generated motorists maintain these characteristics when they are loaded on the network by the supply model. In an equilibrium setting, the framework lends itself to a fixed-point formulation to represent and resolve demand-supply interactions. The paper discusses some of the remaining development challenges and presents an example of an existing travel forecasting model system that incorporates many of the proposed elements.
Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.
2015-12-01
Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.
Forecasting characteristic earthquakes in a minimalist model
DEFF Research Database (Denmark)
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...
Applications products of aviation forecast models
Garthner, John P.
1988-01-01
A service called the Optimum Path Aircraft Routing System (OPARS) supplies products based on output data from the Naval Oceanographic Global Atmospheric Prediction System (NOGAPS), a model run on a Cyber-205 computer. Temperatures and winds are extracted from the surface to 100 mb, approximately 55,000 ft. Forecast winds are available in six-hour time steps.
Meteoroid Environment Modeling: The Meteoroid Engineering Model and Shower Forecasting
Moorhead, Althea V.
2017-01-01
The meteoroid environment is often divided conceptually into meteor showers and the sporadic meteor background. It is commonly but incorrectly assumed that meteoroid impacts primarily occur during meteor showers; instead, the vast majority of hazardous meteoroids belong to the sporadic complex. Unlike meteor showers, which persist for a few hours to a few weeks, sporadic meteoroids impact the Earth's atmosphere and spacecraft throughout the year. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. The sporadic complex, despite its year-round activity, is not isotropic in its directionality. Instead, their apparent points of origin, or radiants, are organized into groups called "sources". The speed, directionality, and size distribution of these sporadic sources are modeled by the Meteoroid Engineering Model (MEM), which is currently in its second major release version (MEMR2) [Moorhead et al., 2015]. MEM provides the meteoroid flux relative to a user-provided spacecraft trajectory; it provides the total flux as well as the flux per angular bin, speed interval, and on specific surfaces (ram, wake, etc.). Because the sporadic complex dominates the meteoroid flux, MEM is the most appropriate model to use in spacecraft design. Although showers make up a small fraction of the meteoroid environment, they can produce significant short-term enhancements of the meteoroid flux. Thus, it can be valuable to consider showers when assessing risks associated with vehicle operations that are brief in duration. To assist with such assessments, the MEO issues an annual forecast that reports meteor shower fluxes as a function of time and compares showers with the time-averaged total meteoroid flux. This permits missions to do quick assessments of the increase in risk posed by meteor showers. Section II describes MEM in more detail and describes our current efforts
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.
Improving species distribution models: the value of data on abundance
National Research Council Canada - National Science Library
Howard, Christine; Stephens, Philip A; Pearce‐Higgins, James W; Gregory, Richard D; Willis, Stephen G; McPherson, Jana
2014-01-01
Species distribution models (SDMs) are important tools for forecasting the potential impacts of future environmental changes but debate remains over the most robust modelling approaches for making projections...
Sales Forecasting System for Newspaper Distribution Companies in Turkey
Directory of Open Access Journals (Sweden)
Gencay İncesu
2012-07-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE st1\\:*{behavior:url(#ieooui } /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Newspapers are like goods with a shelf life of one day and they have to be distributed daily basis to the sales points. A problem that most newspaper companies encounter daily is how to predict the right number of newspapers to print and distribute among distinct sales points. The aim is to predict newspaper demand as accurately as possible to meet customer need with minimum number of returns, missed sales and oversupply. This makes it necessary to develop a short-term forecasting system. The data taken from one of the largest distribution companies in Turkey is time dependent. Therefore, time series analysis is used to forecast newspaper circulation. In this paper, the newspaper sales system is examined for Turkey. Various types of forecasting techniques which are applicable to newspaper circulation planning are compared and a nonlinear approach for returns is applied.
Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting
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Jelena Fiosina
2017-01-01
Full Text Available Forecasting in big datasets is a common but complicated task, which cannot be executed using the well-known parametric linear regression. However, nonparametric and semiparametric methods, which enable forecasting by building nonlinear data models, are computationally intensive and lack sufficient scalability to cope with big datasets to extract successful results in a reasonable time. We present distributed parallel versions of some nonparametric and semiparametric regression models. We used MapReduce paradigm and describe the algorithms in terms of SPARK data structures to parallelize the calculations. The forecasting accuracy of the proposed algorithms is compared with the linear regression model, which is the only forecasting model currently having parallel distributed realization within the SPARK framework to address big data problems. The advantages of the parallelization of the algorithm are also provided. We validate our models conducting various numerical experiments: evaluating the goodness of fit, analyzing how increasing dataset size influences time consumption, and analyzing time consumption by varying the degree of parallelism (number of workers in the distributed realization.
Directory of Open Access Journals (Sweden)
D. L. Shrestha
2013-05-01
Full Text Available The quality of precipitation forecasts from four Numerical Weather Prediction (NWP models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT and regional (ACCESS-R NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.
Hybrid grey model to forecast monitoring series with seasonality
Institute of Scientific and Technical Information of China (English)
WANG Qi-jie; LIAO Xin-hao; ZHOU Yong-hong; ZOU Zheng-rong; ZHU Jian-jun; PENG Yue
2005-01-01
The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.
Directory of Open Access Journals (Sweden)
Chung-Ho Su
2010-12-01
Full Text Available To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs and genetic algorithms (GAs have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1 the rules generated by NNs and GAs are difficult to apply in investment decisions; and (2 the time complexity of the algorithms to produce forecasting outcomes is very high. Therefore, to provide understandable rules for investors and to reduce the time complexity of forecasting algorithms, this paper proposes a novel model for the forecasting process, which combines two granulating methods (the minimize entropy principle approach and the cumulative probability distribution approach and a rough set algorithm. The model verification demonstrates that the proposed model surpasses the three listed conventional fuzzy time-series models and a multiple regression model (MLR in forecast accuracy.
Mesoscale Modeling, Forecasting and Remote Sensing Research.
remote sensing , cyclonic scale diagnostic studies and mesoscale numerical modeling and forecasting are summarized. Mechanisms involved in the release of potential instability are discussed and simulated quantitatively, giving particular attention to the convective formulation. The basic mesoscale model is documented including the equations, boundary condition, finite differences and initialization through an idealized frontal zone. Results of tests including a three dimensional test with real data, tests of convective/mesoscale interaction and tests with a detailed
EXPENSES FORECASTING MODEL IN UNIVERSITY PROJECTS PLANNING
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Sergei A. Arustamov
2016-11-01
Full Text Available The paper deals with mathematical model presentation of cash flows in project funding. We describe different types of expenses linked to university project activities. Problems of project budgeting that contribute most uncertainty have been revealed. As an example of the model implementation we consider calculation of vacation allowance expenses for project participants. We define problems of forecast for funds reservation: calculation based on methodology established by the Ministry of Education and Science calculation according to the vacation schedule and prediction of the most probable amount. A stochastic model for vacation allowance expenses has been developed. We have proposed methods and solution of the problems that increase the accuracy of forecasting for funds reservation based on 2015 data.
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Samoa
National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the islands of Samoa at...
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Guam
National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the island of Guam at...
Weather Research and Forecasting (WRF) Regional Atmospheric Model: CNMI
National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Commonwealth of the Northern...
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Oahu
National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 3.5-day hourly forecast for the region surrounding the Hawaiian island of Oahu at...
Directory of Open Access Journals (Sweden)
V. A. Bell
2000-01-01
Full Text Available A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid within the higher resolution model (5 km grid. A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii advected, steady-state predictions from the dynamic model, using 'inferred updraughts', provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment
Forecasting telecommunications data with linear models
Madden, Gary G; Tan, Joachim
2007-01-01
For telecommunication companies to successfully manage their business, companies rely on mapping future trends and usage patterns. However, the evolution of telecommunications technology and systems in the provision of services renders imperfections in telecommunications data and impinges on a company’s’ ability to properly evaluate and plan their business. ITU Recommendation E.507 provides a selection of econometric models for forecasting these trends. However, no specific guidance is given....
Nonlinear Dynamical Modeling and Forecast of ENSO Variability
Feigin, Alexander; Mukhin, Dmitry; Gavrilov, Andrey; Seleznev, Aleksey; Loskutov, Evgeny
2017-04-01
New methodology of empirical modeling and forecast of nonlinear dynamical system variability [1] is applied to study of ENSO climate system. The methodology is based on two approaches: (i) nonlinear decomposition of data [2], that provides low-dimensional embedding for further modeling, and (ii) construction of empirical model in the form of low dimensional random dynamical ("stochastic") system [3]. Three monthly data sets are used for ENSO modeling and forecast: global sea surface temperature anomalies, troposphere zonal wind speed, and thermocline depth; all data sets are limited by 30 S, 30 N and have horizontal resolution 10x10 . We compare results of optimal data decomposition as well as prognostic skill of the constructed models for different combinations of involved data sets. We also present comparative analysis of ENSO indices forecasts fulfilled by our models and by IRI/CPC ENSO Predictions Plume. [1] A. Gavrilov, D. Mukhin, E. Loskutov, A. Feigin, 2016: Construction of Optimally Reduced Empirical Model by Spatially Distributed Climate Data. 2016 AGU Fall Meeting, Abstract NG31A-1824. [2] D. Mukhin, A. Gavrilov, E. Loskutov , A.Feigin, J.Kurths, 2015: Principal nonlinear dynamical modes of climate variability, Scientific Reports, rep. 5, 15510; doi: 10.1038/srep15510. [3] Ya. Molkov, D. Mukhin, E. Loskutov, A. Feigin, 2012: Random dynamical models from time series. Phys. Rev. E, Vol. 85, n.3.
NEW CAR DEMAND MODELING AND FORECASTING USING BASS DIFFUSION MODEL
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Zuhaimy Ismail
2013-01-01
Full Text Available Forecasting model of new product demand has been developed and applied to forecast new vehicle demand in Malaysia. Since the publication of the Bass model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. The building of Bass diffusion model for forecasting new product within the Malaysian society is presented in this study. The proposed model represents the spread level of new Proton car among a given set of the society in terms of a simple mathematical function that elapsed since the introduction of the new car. With the limited amount of data available for the new car, a robust Bass model was developed to forecast the sales volume. A procedure of the proposed diffusion model was designed and the parameters were estimated. Results obtained by applying the proposed model and numerical calculation shows that the proposed diffusion model is robust and effective for forecasting demand of new Proton car. The proposed diffusion model is shown to forecast more effectively and accurately even with insufficient previous data on the new product.
Acheson, Emily Sohanna; Kerr, Jeremy Thomas
2015-03-01
Arthropod disease vectors, most notably mosquitoes, ticks, tsetse flies, and sandflies, are strongly influenced by environmental conditions and responsible for the vast majority of global vector-borne human diseases. The most widely used statistical models to predict future vector distributions model species niches and project the models forward under future climate scenarios. Although these methods address variations in vector distributions through space, their capacity to predict changing distributions through time is far less certain. Here, we review modeling methods used to validate and forecast future distributions of arthropod vectors under the effects of climate change and outline the uses or limitations of these techniques. We then suggest a validation approach specific to temporal extrapolation models that is gaining momentum in macroecological modeling and has great potential for epidemiological modeling of disease vectors. We performed systematic searches in the Web of Science, ScienceDirect, and Google Scholar to identify peer-reviewed English journal articles that model arthropod disease vector distributions under future environment scenarios. We included studies published up to and including June, 2014. We identified 29 relevant articles for our review. The majority of these studies predicted current species niches and projected the models forward under future climate scenarios without temporal validation. Historically calibrated forecast models improve predictions of changing vector distributions by tracking known shifts through recently observed time periods. With accelerating climate change, accurate predictions of shifts in disease vectors are crucial to target vector control interventions where needs are greatest.
The Red Sea Modeling and Forecasting System
Hoteit, Ibrahim
2015-04-01
Despite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling system compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research Forecasting (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling system (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We
Time series models to simulate and forecast hourly averaged wind speeds in Quetta, Pakistan
Energy Technology Data Exchange (ETDEWEB)
Lalarukh Kamal [Balochistan University, Quetta (Pakistan). Dept. of Mathematics; Yasmin Zahra Jafri [Balochistan University, Quetta (Pakistan). Dept. of Statistics
1997-07-01
Stochastic simulation and forecast models of hourly average wind speeds are presented. Time series models take into account several basic features of wind speed data including autocorrelation, non-Gaussian distribution and diurnal nonstationarity. The positive correlation between consecutive wind speed observations is taken into account by fitting an ARMA (p,q) process to wind speed data transformed to make their distribution approximately Gaussian and standardized to remove scattering of transformed data. Diurnal variations have been taken into account to observe forecasts and its dependence on lead times. We find the ARMA (p,q) model suitable for prediction intervals and probability forecasts. (author)
Towards operational modeling and forecasting of the Iberian shelves ecosystem.
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Martinho Marta-Almeida
Full Text Available There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a Nutrients-Phytoplankton-Zooplankton-Detritus biogeochemical module (NPZD. In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmol N m(-3. Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill.
operational modelling and forecasting of the Iberian shelves ecosystem
Marta-Almeida, M.; Reboreda, R.; Rocha, C.; Dubert, J.; Nolasco, R.; Cordeiro, N.; Luna, T.; Rocha, A.; Silva, J. Lencart e.; Queiroga, H.; Peliz, A.; Ruiz-Villarreal, M.
2012-04-01
There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a NPZD biogeochemical module. In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmolN m-3). Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill.
Kalman filter estimation model in flood forecasting
Husain, Tahir
Elementary precipitation and runoff estimation problems associated with hydrologic data collection networks are formulated in conjunction with the Kalman Filter Estimation Model. Examples involve the estimation of runoff using data from a single precipitation station and also from a number of precipitation stations. The formulations demonstrate the role of state-space, measurement, and estimation equations of the Kalman Filter Model in flood forecasting. To facilitate the formulation, the unit hydrograph concept and antecedent precipitation index is adopted in the estimation model. The methodology is then applied to estimate various flood events in the Carnation Creek of British Columbia.
Limited Area Forecasting and Statistical Modelling for Wind Energy Scheduling
DEFF Research Database (Denmark)
Rosgaard, Martin Haubjerg
forecast accuracy for operational wind power scheduling. Numerical weather prediction history and scales of atmospheric motion are summarised, followed by a literature review of limited area wind speed forecasting. Hereafter, the original contribution to research on the topic is outlined. The quality...... control of wind farm data used as forecast reference is described in detail, and a preliminary limited area forecasting study illustrates the aggravation of issues related to numerical orography representation and accurate reference coordinates at ne weather model resolutions. For the o shore and coastal...... sites studied limited area forecasting is found to deteriorate wind speed prediction accuracy, while inland results exhibit a steady forecast performance increase with weather model resolution. Temporal smoothing of wind speed forecasts is shown to improve wind power forecast performance by up to almost...
Evaluation of Coupled Model Forecasts of Ethiopian Highlands Summer Climate
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Mark R. Jury
2014-01-01
Full Text Available This study evaluates seasonal forecasts of rainfall and maximum temperature across the Ethiopian highlands from coupled ensemble models in the period 1981–2006, by comparison with gridded observational products (NMA + GPCC/CRU3. Early season forecasts from the coupled forecast system (CFS are steadier than European community medium range forecast (ECMWF. CFS and ECMWF April forecasts of June–August (JJA rainfall achieve significant fit (r2=0.27, 0.25, resp., but ECMWF forecasts tend to have a narrow range with drought underpredicted. Early season forecasts of JJA maximum temperature are weak in both models; hence ability to predict water resource gains may be better than losses. One aim of seasonal climate forecasting is to ensure that crop yields keep pace with Ethiopia’s growing population. Farmers using prediction technology are better informed to avoid risk in dry years and generate surplus in wet years.
Real-time Social Internet Data to Guide Forecasting Models
Energy Technology Data Exchange (ETDEWEB)
Del Valle, Sara Y. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-09-20
Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematical approaches and heterogeneous data streams.
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.
Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.
2016-10-01
Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.
Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.
2017-08-01
Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.
Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors
DEFF Research Database (Denmark)
Halbleib, Roxana; Voev, Valeri
2011-01-01
This paper analyzes the forecast accuracy of the multivariate realized volatility model introduced by Chiriac and Voev (2010), subject to different degrees of model parametrization and economic evaluation criteria. Bymodelling the Cholesky factors of the covariancematrices, the model generates...... positive definite, but biased covariance forecasts. In this paper, we provide empirical evidence that parsimonious versions of the model generate the best covariance forecasts in the absence of bias correction. Moreover, we show by means of stochastic dominance tests that any risk averse investor......, regardless of the type of utility function or return distribution, would be better-off from using this model than from using some standard approaches....
Regional probabilistic fertility forecasting by modeling between-country correlations
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Bailey Fosdick
2014-04-01
Full Text Available Background: The United Nations (UN Population Division constructs probabilistic projections for the total fertility rate (TFR using the Bayesian hierarchical model of Alkema et al. (2011, which produces predictive distributions of the TFR for individual countries. The UN is interested in publishing probabilistic projections for aggregates of countries, such as regions and trading blocs. This requires joint probabilistic projections of future countryspecific TFRs, taking account of the correlations between them. Objective: We propose an extension of the Bayesian hierarchical model that allows for probabilistic projection of aggregate TFR for any set of countries. Methods: We model the correlation between country forecast errors as a linear function of time invariant covariates, namely whether the countries are contiguous, whether they had a common colonizer after 1945, and whether they are in the same UN region. The resulting correlation model is incorporated into the Bayesian hierarchical model's error distribution. Results: We produce predictive distributions of TFR for 1990-2010 for each of the UN's primary regions. We find that the proportions of the observed values that fall within the prediction intervals from our method are closer to their nominal levels than those produced by the current model. Conclusions: Our results suggest that a substantial proportion of the correlation between forecast errors for TFR in different countries is due to the countries' geographic proximity to one another, and that if this correlation is accounted for, the quality of probabilistic projections of TFR for regions and other aggregates is improved.
Constrained regression models for optimization and forecasting
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P.J.S. Bruwer
2003-12-01
Full Text Available Linear regression models and the interpretation of such models are investigated. In practice problems often arise with the interpretation and use of a given regression model in spite of the fact that researchers may be quite "satisfied" with the model. In this article methods are proposed which overcome these problems. This is achieved by constructing a model where the "area of experience" of the researcher is taken into account. This area of experience is represented as a convex hull of available data points. With the aid of a linear programming model it is shown how conclusions can be formed in a practical way regarding aspects such as optimal levels of decision variables and forecasting.
With string model to time series forecasting
Pinčák, Richard; Bartoš, Erik
2015-01-01
Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial ma...
PV power forecast using a nonparametric PV model
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quant...
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.
Forecast of future aviation fuels: The model
Ayati, M. B.; Liu, C. Y.; English, J. M.
1981-01-01
A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.
Neural network versus classical time series forecasting models
Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam
2017-05-01
Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.
Kitahara, Takeshi; Furuya, Hiroki; Nakamura, Hajime
Since traffic in IP access networks is less aggregated than in backbone networks, its variance could be significant and its distribution may be long-tailed rather than Gaussian in nature. Such characteristics make it difficult to forecast traffic volume in IP access networks for appropriate capacity planning. This paper proposes a traffic forecasting method that includes a function to control residual error distribution in IP access networks. The objective of the proposed method is to grasp the statistical characteristics of peak traffic variations, while conventional methods focus on average rather than peak values. In the proposed method, a neural network model is built recursively while weighting residual errors around the peaks. This enables network operators to control the trade-off between underestimation and overestimation errors according to their planning policy. Evaluation with a total of 136 daily traffic volume data sequences measured in actual IP access networks demonstrates the performance of the proposed method.
A Stochastic-Dynamic Model for Real Time Flood Forecasting
Chow, K. C. A.; Watt, W. E.; Watts, D. G.
1983-06-01
A stochastic-dynamic model for real time flood forecasting was developed using Box-Jenkins modelling techniques. The purpose of the forecasting system is to forecast flood levels of the Saint John River at Fredericton, New Brunswick. The model consists of two submodels: an upstream model used to forecast the headpond level at the Mactaquac Dam and a downstream model to forecast the water level at Fredericton. Inputs to the system are recorded values of the water level at East Florenceville, the headpond level and gate position at Mactaquac, and the water level at Fredericton. The model was calibrated for the spring floods of 1973, 1974, 1977, and 1978, and its usefulness was verified for the 1979 flood. The forecasting results indicated that the stochastic-dynamic model produces reasonably accurate forecasts for lead times up to two days. These forecasts were then compared to those from the existing forecasting system and were found to be as reliable as those from the existing system.
Forecasting the Polish zloty with non-linear models
Michal Rubaszek; Pawel Skrzypczynski; Grzegorz Koloch
2011-01-01
The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical economic models or the use of advanced econometric techniques can help explain future movements in exchange rates. The results of the empirical studies for major world currencies show that forecasts from a naive random walk tend to be comparable or even better than forecasts from more sophisticated models. In the case of the Polish zloty, the discussion in the literature on ex...
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)
Directory of Open Access Journals (Sweden)
X. Chen
2013-09-01
Full Text Available A Hierarchal Bayesian model for forecasting regional summer rainfall and streamflow season-ahead using exogenous climate variables for East Central China is presented. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multilevel structure with regression coefficients modeled from a common multivariate normal distribution results in partial-pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include Receiver Operating Characteristic, Reduction of Error, Coefficient of Efficiency, Rank Probability Skill Scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast regional summer rainfall and streamflow season-ahead offers potential for developing adaptive water risk management strategies.
Bulk meteorological parameters for diagnosing cloudiness in the stochastic cloud forecast model
Leach, Ryan N.
2006-01-01
The three dimensional distribution of clouds is of great interest to the Air Force, and to the aviation community in general. The Stochastic Cloud Forecast Model (SCFM) is a novel, global cloud model currently operated at the Air Force Weather Agency (AFWA) which diagnoses cloud cover statistically using a minimal set of predictors from global numerical forecasts. Currently the four predictors are pressure, temperature, vertical velocity, and relative humidity. In this thesis, 330 sets of...
Verification of precipitation forecasts by the DWD limited area model LME over Cyprus
Directory of Open Access Journals (Sweden)
K. Savvidou
2007-01-01
Full Text Available A comparison is made between the precipitation forecasts by the non-hydrostatic limited area model LME of the German Weather Service (DWD and observations from a network of rain gauges in Cyprus. This is a first attempt to carry out a preliminary verification and evaluation of the LME precipitation forecasts over the area of Cyprus. For the verification, model forecasts and observations were used covering an eleven month period, from 1/2/2005 till 31/12/2005. The observations were made by three Automatic Weather Observing Systems (AWOS located at Larnaka and Paphos airports and at Athalassa synoptic station, as well as at 6, 6 and 8 rain gauges within a radius of about 30 km around these stations, respectively. The observations were compared with the model outputs, separately for each of the three forecast days. The "probability of detection" (POD of a precipitation event and the "false alarm rate" (FAR were calculated. From the selected cases of the forecast precipitation events, the average forecast precipitation amounts in the area around the three stations were compared with the measured ones. An attempt was also made to evaluate the model's skill in predicting the spatial distribution of precipitation and, in this respect, the geographical position of the maximum forecast precipitation amount was contrasted to the position of the corresponding observed maximum. Maps with monthly precipitation totals observed by a local network of 150 rain gauges were compared with the corresponding forecast precipitation maps.
Forecasting natural gas consumption in China by Bayesian Model Averaging
Directory of Open Access Journals (Sweden)
Wei Zhang
2015-11-01
Full Text Available With rapid growth of natural gas consumption in China, it is in urgent need of more accurate and reliable models to make a reasonable forecast. Considering the limitations of the single model and the model uncertainty, this paper presents a combinative method to forecast natural gas consumption by Bayesian Model Averaging (BMA. It can effectively handle the uncertainty associated with model structure and parameters, and thus improves the forecasting accuracy. This paper chooses six variables for forecasting the natural gas consumption, including GDP, urban population, energy consumption structure, industrial structure, energy efficiency and exports of goods and services. The results show that comparing to Gray prediction model, Linear regression model and Artificial neural networks, the BMA method provides a flexible tool to forecast natural gas consumption that will have a rapid growth in the future. This study can provide insightful information on natural gas consumption in the future.
Wu, Zhiyong; Wu, Juan; Lu, Guihua
2016-09-01
Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling system, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.
A Simple Hybrid Model for Short-Term Load Forecasting
Directory of Open Access Journals (Sweden)
Suseelatha Annamareddi
2013-01-01
Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.
Dekić, L.; Mihalović, A.; Jovičić, I.; Vladiković, D.; Jerinić, J.; Ivković, M.
2012-04-01
This paper examines two episodes of heavy rainfall and significantly increased water levels. The first case relates to the period including the beginning and the end of the third decade of June 2010 at the Kolubara river basin, where extreme rainfall led to two big flood waves on the Kolubara river, whereat water levels exceeded both regular and extraordinary flood defence and approached their historical maximum. The second case relates to the period including the end of November and the beginning of December 2010 at the Jadar river basin, where heavier precipitation caused the water levels of the basin to reach and surpass the occurrence limit (warning level). The HBV (Hydrological Bureau Waterbalance-section) rainfall/snowmelt - runoff model installed at the RHMSS uses gridded quantitative precipitation and air temperature forecast for 72 hours in advance based on meteorological weather forecast WRF-NMM mesoscale model. Nonhydrostatic Mesoscale Model (NMM) core of the Weather Research and Forecasting (WRF) system is flexible state-of-the-art numerical weather prediction model capable to describe and estimate powerful nonhydrostatic mechanism in convective clouds that cause heavy rain. The HBV model is a semi-distributed conceptual catchment model in which the spatial structure of a catchment area is not explicitly modelled. Instead, the sub-basin represents a primary modelling unit while the basin is characterised by area-elevation distribution and classification of vegetation cover and land use distributed by height zone. WRF-NMM forecast shows very good agreement with observations in terms of timing, location and amount of precipitation. They are used as input for HBV model, forecasted discharges at the output profile of the selected river basin represent model output for consideration. 1 Republic Hydrometeorological Service of Serbia
Energy Technology Data Exchange (ETDEWEB)
Love, C G
1976-08-23
These appendixes are referenced in Volume II of this report. They contain the detailed electrical distribution equipment requirements and input material requirements forecasts. Forecasts are given for three electric energy usage scenarios. Also included are data on worldwide reserves and demand for 30 raw materials required for the manufacture of electrical distribution equipment.
SARS epidemical forecast research in mathematical model
Institute of Scientific and Technical Information of China (English)
DING Guanghong; LIU Chang; GONG Jianqiu; WANG Ling; CHENG Ke; ZHANG Di
2004-01-01
The SIJR model, simplified from the SEIJR model, is adopted to analyze the important parameters of the model of SARS epidemic such as the transmission rate, basic reproductive number. And some important parameters are obtained such as the transmission rate by applying this model to analyzing the situation in Hong Kong, Singapore and Canada at the outbreak of SARS. Then forecast of the transmission of SARS is drawn out here by the adjustment of parameters (such as quarantined rate) in the model. It is obvious that inflexion lies on the crunode of the graph, which indicates the big difference in transmission characteristics between the epidemic under control and not under control. This model can also be used in the comparison of the control effectiveness among different regions. The results from this model match well with the actual data in Hong Kong, Singapore and Canada and as a by-product, the index of the effectiveness of control in the later period can be acquired. It offers some quantitative indexes, which may help the further research in epidemic diseases.
Factor Model Forecasting of Inflation in Croatia
Directory of Open Access Journals (Sweden)
Davor Kunovac
2007-12-01
Full Text Available This paper tests whether information derived from 144 economic variables (represented by only a few constructed factors can be used for the forecasting of consumer prices in Croatia. The results obtained show that the use of one factor enhances the precision of the benchmark model’s ability to forecast inflation. The methodology used is sufficiently general to be able to be applied directly for the forecasting of other economic variables.
Factor Model Forecasts of Exchange Rates
Charles Engel; Nelson C. Mark; Kenneth D. West
2012-01-01
We construct factors from a cross section of exchange rates and use the idiosyncratic deviations from the factors to forecast. In a stylized data generating process, we show that such forecasts can be effective even if there is essentially no serial correlation in the univariate exchange rate processes. We apply the technique to a panel of bilateral U.S. dollar rates against 17 OECD countries. We forecast using factors, and using factors combined with any of fundamentals suggested by Taylor r...
RESULTS OF INTERBANK EXCHANGE RATES FORECASTING USING STATE SPACE MODEL
Directory of Open Access Journals (Sweden)
Muhammad Kashif
2008-07-01
Full Text Available This study evaluates the performance of three alternative models for forecasting daily interbank exchange rate of U.S. dollar measured in Pak rupees. The simple ARIMA models and complex models such as GARCH-type models and a state space model are discussed and compared. Four different measures are used to evaluate the forecasting accuracy. The main result is the state space model provides the best performance among all the models.
Institute of Scientific and Technical Information of China (English)
陈翔; 李丽星; 洪嘉铭; 周怡
2014-01-01
目的以肇庆市第一人民医院2003～2006年住院病例死因顺位转移情况为依据，预测2007～2012年该院住院病例死因顺位分布转移情况，探讨2005年实施《全国高血压社区规范化管理》项目以来肇庆地区高血压防治取得的效果。方法应用Markov状态转移矩阵预测高血压病防控对死因顺位分布的影响。结果Markov模型预测该院住院患者死因顺位分布转移与实际情况吻合较好，前三位主要死因分别从2003～2004年的肿瘤、神经系统和循环系统疾病，变为2005年开始的肿瘤、循环系统和神经系统疾病，神经系统脑意外死亡减少，到2012年且各病种的住院病例平均死亡年龄增长了4.1岁。结论从该院十年来死因顺位变化看，实施《全国高血压社区规范化管理》在广东肇庆地区可能有较为明显的效果，Markov模型用于预测各种疾病状态转移趋势显示出较好的优势。%Objective To forecast the distribution of hospital rank order for death causes from 2007 to 2012, which is based on the transition condition of death cause from 2003 to 2006 in the first People’s hospital of Zhaoqing city; investigate the effect of hypertension preventing and controlling in Zhaoqing district since 2005 to implement the standardized management of hypertension based on community. Methods Using Markov state transition matrix to forecast the influence of hypertension preventing and controlling for rank order for death causes distribution. Results The condition of distribution and transition of rank order for death causes with Markov model forecasting was in good accordance with the true condition. The first three causes of death were tumor, nervous system disease and circulatory disease from 2003 to 2004; then there were tumor, circulatory disease and nervous system disease since 2005. The average death age of each disease increased 4.1 years old until 2012. Conclusion An observation at the
Shemya, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Shemya, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...
Palm Beach, Florida Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Palm Beach, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Haleiwa, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Haleiwa, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Key West, Florida Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Key West, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Sitka, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Sitka, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...
Monterey, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Monterey, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Ponce, Puerto Rico Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Ponce, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Port Alexander, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Port Alexander, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Port Orford, Oregon Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Port Orford, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Seward, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Seward, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...
Nawiliwili, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Nawiliwili, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Montauk, New York Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Montauk, New York Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
San Juan, Puerto Rico Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The San Juan, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Arecibo, Puerto Rico Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Arecibo, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Toke Point, Washington Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Toke Point, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Hilo, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Hilo, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
Ocean City, Maryland Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Ocean City, Maryland Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Keauhou, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Keauhou, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Honolulu, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Honolulu, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
San Diego, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The San Diego, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Adak, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Adak, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
Garibaldi, Oregon Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Garibaldi, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Kihei, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Kihei, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...
Kahului, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Kahului, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Daytona Beach, Florida Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Daytona Beach, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Mayaguez, Puerto Rico Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Mayaguez, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Savannah, Georgia Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Savannah, Georgia Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Homer, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Homer, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...
King Cove, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The King Cove, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Portland, Maine Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Portland, Maine Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
La Push, Washington Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The La Push, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Seaside, Oregon Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Seaside, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Fajardo, Puerto Rico Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Fajardo, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Kawaihae, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Kawaihae, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Nikolski, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Nikolski, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Kodiak, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Kodiak, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...
Sand Point, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Sand Point, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Pearl Harbor, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Pearl Harbor, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Florence, Oregon Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Florence, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Hanalei, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Hanalei, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Lahaina, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Lahaina, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Wake Island Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Wake Island Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
Kailua-Kona, Hawaii Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Kailua-Kona, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Apra Harbor, Guam Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Apra Harbor, Guam Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Westport, Washington Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Westport, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Neah Bay, Washington Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Neah Bay, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....
Quantifying Uncertainty in Distributed Flash Flood Forecasting for a Semiarid Region
Samadi, S.; Pourreza Bilondi, M.; Ghahraman, B.; Akhoond-Ali, A. M.
2015-12-01
Reliability of semiarid flood forecasting is affected by several factors, including rainfall forcing, the system input-state-output behavior, initial soil moisture conditions and model parameters and structure. This study employed Bayesian frameworks to enable the explicit description and assessment of parameter and predictive uncertainty for convective rainfall-runoff modeling of a semiarid watershed system in Iran. We examined the performance and uncertainty analysis of a mixed conceptual and physical based rainfall-runoff model (AFFDEF) linked with three Markov chain Monte Carlo (MCMC) samplers: the DiffeRential Evolution Adaptive Metropolis (DREAM), the Shuffled Complex Evolution Metropolis (SCEM-UA), and DREAM- ZS, to forecast four potential semiarid convective events with varying rainfall duration (20 mm). Calibration results demonstrated that model predictive uncertainty was heavily dominated by error and bias in the soil water storage capacity which reflect inadequate representation of the upper soil zone processes by hydrological model. Furthermore, parameters associated with infiltration and interception capacity along with contributing area threshold for digital river network were identified the key model parameters and more influential on the modeled flood hydrograph. In addition, parameter inference in the DREAM model showed a consistent behavior with the priori assumption by closely matching the inferred error distribution to the empirical distribution of the model residual, indicating that model parameters are well identified. DREAM result further revealed that the uncertainty associated with rainfall of lower magnitudes was higher than rainfall of higher magnitudes. Uncertainty quantification of semiarid convective events provided significant insights into the mathematical relationship and characteristics of short-term forecast error and may be applicable to other semiarid watershed systems with the similar rainfall-runoff processes.
Regional Model Nesting Within GFS Daily Forecasts Over West Africa
Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben
2010-01-01
The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger
Planetary Kp index forecast using autoregressive models
Gonzalez, Arian Ojeda; Odriozola, Siomel Savio; Rosa, Reinaldo Roberto; Mendes, Odim
2014-01-01
The geomagnetic Kp index is derived from the K index measurements obtained from thirteen stations located around the Earth geomagnetic latitudes between $48^\\circ$ and $63^\\circ$. This index is processed every three hours, is quasi-logarithmic and estimates the geomagnetic activity. The Kp values fall within a range of 0 to 9 and are organized as a set of 28 discrete values. The data set is important because it is used as one of the many input parameters of magnetospheric and ionospheric models. The objective of this work is to use historical data from the Kp index to develop a methodology to make a prediction in a time interval of at least three hours. Five different models to forecast geomagnetic indices Kp and ap are tested. Time series of values of Kp index from 1932 to 15/12/2012 at 21:00 UT are used as input to the models. The purpose of the model is to predict the three measured values after the last measured value of the Kp index (it means the next 9 hours values). The AR model provides the lowest com...
A review of operational, regional-scale, chemical weather forecasting models in Europe
Kukkonen, J.; Olsson, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.-H.; Poupkou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.E.J.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, K.
2012-01-01
Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed
Skaugen, Thomas; Haddeland, Ingjerd
2014-05-01
A new parameter-parsimonious rainfall-runoff model, DDD (Distance Distribution Dynamics) has been run operationally at the Norwegian Flood Forecasting Service for approximately a year. DDD has been calibrated for, altogether, 104 catchments throughout Norway, and provide runoff forecasts 8 days ahead on a daily temporal resolution driven by precipitation and temperature from the meteorological forecast models AROME (48 hrs) and EC (192 hrs). The current version of DDD differs from the standard model used for flood forecasting in Norway, the HBV model, in its description of the subsurface and runoff dynamics. In DDD, the capacity of the subsurface water reservoir M, is the only parameter to be calibrated whereas the runoff dynamics is completely parameterised from observed characteristics derived from GIS and runoff recession analysis. Water is conveyed through the soils to the river network by waves with celerities determined by the level of saturation in the catchment. The distributions of distances between points in the catchment to the nearest river reach and of the river network give, together with the celerities, distributions of travel times, and, consequently unit hydrographs. DDD has 6 parameters less to calibrate in the runoff module than the HBV model. Experiences using DDD show that especially the timing of flood peaks has improved considerably and in a comparison between DDD and HBV, when assessing timeseries of 64 years for 75 catchments, DDD had a higher hit rate and a lower false alarm rate than HBV. For flood peaks higher than the mean annual flood the median hit rate is 0.45 and 0.41 for the DDD and HBV models respectively. Corresponding number for the false alarm rate is 0.62 and 0.75 For floods over the five year return interval, the median hit rate is 0.29 and 0.28 for the DDD and HBV models, respectively with false alarm rates equal to 0.67 and 0.80. During 2014 the Norwegian flood forecasting service will run DDD operationally at a 3h temporal
NWP model forecast skill optimization via closure parameter variations
Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.
2012-04-01
We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.
Forecasting Financial Time Series Using Model Averaging
F. Ravazzolo (Francesco)
2007-01-01
textabstractIn almost all cases a decision maker cannot identify ex ante the true process. This observation has led researchers to introduce several sources of uncertainty in forecasting exercises. In this context, the research reported in these pages finds an increase of forecasting power o
Garris, Heath W; Mitchell, Randall J; Fraser, Lauchlan H; Barrett, Linda R
2015-02-01
Shifting precipitation patterns brought on by climate change threaten to alter the future distribution of wetlands. We developed a set of models to understand the role climate plays in determining wetland formation on a landscape scale and to forecast changes in wetland distribution for the Midwestern United States. These models combined 35 climate variables with 21 geographic and anthropogenic factors thought to encapsulate other major drivers of wetland distribution for the Midwest. All models successfully recreated a majority of the variation in current wetland area within the Midwest, and showed that wetland area was significantly associated with climate, even when controlling for landscape context. Inferential (linear) models identified a consistent negative association between wetland area and isothermality. This is likely the result of regular inundation in areas where precipitation accumulates as snow, then melts faster than drainage capacity. Moisture index seasonality was identified as a key factor distinguishing between emergent and forested wetland types, where forested wetland area at the landscape scale is associated with a greater seasonal variation in water table depth. Forecasting models (neural networks) predicted an increase in potential wetland area in the coming century, with areas conducive to forested wetland formation expanding more rapidly than areas conducive to emergent wetlands. Local cluster analyses identified Iowa and Northeastern Missouri as areas of anticipated wetland expansion, indicating both a risk to crop production within the Midwest Corn Belt and an opportunity for wetland conservation, while Northern Minnesota and Michigan are potentially at risk of wetland losses under a future climate.
O'Brien, Enda; McKinstry, Alastair; Ralph, Adam
2015-04-01
Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.
Drift dynamics in a coupled model initialized for decadal forecasts
Sanchez-Gomez, Emilia; Cassou, Christophe; Ruprich-Robert, Yohan; Fernandez, Elodie; Terray, Laurent
2016-03-01
, can be viewed for the coupled model as an efficient way to rapidly adjust to its own biased climate mean state. Weak cold ENSO events tend to occur the second year of the forecast due to the so-called discharge-recharge mechanism while the spurious oscillatory behavior is progressively damped. The latter mechanism is much more pronounced in retrospective forecasts initialized from the NOEQ configuration for which the ENSO flip-flop is still detectable at leadtime 4 year. Associated atmospheric teleconnections interfere worldwide with regional drifts, especially in the North Pacific and more remotely in the North Atlantic. In the latter basin, the drift can be interpreted as the model response to intrinsic atmospheric circulation biases found in the stand-alone atmosphere component of the model, which project onto the negative phase of the North Atlantic Oscillation. A fast adjustment (up to ~5-year leadtime) occurs leading to a rapid slackening of both the vertical (Atlantic meridional overturning circulation) and horizontal circulations, especially in the subpolar gyre. Slower adjustment of the entire water masses distribution in the North Atlantic then takes over involving several mechanisms. We show that a weak feedback is locally present between the atmospheric circulation and the ocean drift that controls the timescale of the setting of the coupled model biases.
Forecasting project schedule performance using probabilistic and deterministic models
Directory of Open Access Journals (Sweden)
S.A. Abdel Azeem
2014-04-01
Full Text Available Earned value management (EVM was originally developed for cost management and has not widely been used for forecasting project duration. In addition, EVM based formulas for cost or schedule forecasting are still deterministic and do not provide any information about the range of possible outcomes and the probability of meeting the project objectives. The objective of this paper is to develop three models to forecast the estimated duration at completion. Two of these models are deterministic; earned value (EV and earned schedule (ES models. The third model is a probabilistic model and developed based on Kalman filter algorithm and earned schedule management. Hence, the accuracies of the EV, ES and Kalman Filter Forecasting Model (KFFM through the different project periods will be assessed and compared with the other forecasting methods such as the Critical Path Method (CPM, which makes the time forecast at activity level by revising the actual reporting data for each activity at a certain data date. A case study project is used to validate the results of the three models. Hence, the best model is selected based on the lowest average percentage of error. The results showed that the KFFM developed in this study provides probabilistic prediction bounds of project duration at completion and can be applied through the different project periods with smaller errors than those observed in EV and ES forecasting models.
Study on Population Forecast Model in Planning of Land Use
Institute of Scientific and Technical Information of China (English)
2011-01-01
On the basis of describing characteristics and condition of application of natural growth model of population,weighted average growth model,regression forecast model and GM(1,1) forecast model,taking Gushi County in Henan Province as an example,according to the statistics of population in Gushi County Statistical Yearbook from 1991 to 2007,we establish four models to conduct fitting on population change respectively,and meanwhile,we predict population size from 2008 to 2009 and conduct preciseness test on the population size.The test results show that the preciseness of forecast results of natural growth model is not high,and the preciseness of forecast results of weighted average growth model is not scientific when the total size of population is unstable.The results of GM(1,1) forecast model and regression forecast model largely conform to the actual data,so we can take the mean of the two as the final forecast result.
Operational forecasting based on a modified Weather Research and Forecasting model
Energy Technology Data Exchange (ETDEWEB)
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.
Testing probabilistic adaptive real-time flood forecasting models
Smith, P.J.; Beven, K.J.; Leedal, D.; Weerts, A.H.; Young, P.C.
2014-01-01
Operational flood forecasting has become a complex and multifaceted task, increasingly being treated in probabilistic ways to allow for the inherent uncertainties in the forecasting process. This paper reviews recent applications of data-based mechanistic (DBM) models within the operational UK Natio
Combined forecasts from linear and nonlinear time series models
N. Terui (Nobuhiko); H.K. van Dijk (Herman)
1999-01-01
textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally (non)line
Forecasts of time averages with a numerical weather prediction model
Roads, J. O.
1986-01-01
Forecasts of time averages of 1-10 days in duration by an operational numerical weather prediction model are documented for the global 500 mb height field in spectral space. Error growth in very idealized models is described in order to anticipate various features of these forecasts and in order to anticipate what the results might be if forecasts longer than 10 days were carried out by present day numerical weather prediction models. The data set for this study is described, and the equilibrium spectra and error spectra are documented; then, the total error is documented. It is shown how forecasts can immediately be improved by removing the systematic error, by using statistical filters, and by ignoring forecasts beyond about a week. Temporal variations in the error field are also documented.
Multilayer stock forecasting model using fuzzy time series.
Javedani Sadaei, Hossein; Lee, Muhammad Hisyam
2014-01-01
After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.
Forecasting the Marine Corps’ Enlisted Classification Plan: Assessment of An Alternative Model
2012-03-01
Figure 8. Example GAR Requirements Over the FYDP.................................................24 Figure 9. Models’ MAPE Distribution...Marine Air-Ground Task Force MAPE Mean Absolute Percent Error MCT Marine Combat Training MOE Measure of Effectiveness MOS Military Occupational...Figure 9. Models’ MAPE Distribution However, an analysis of the steady-state model reveals that produced a forecast error mean of 40 percentage
Comparison of point forecast accuracy of model averaging methods in hydrologic applications
Diks, C.G.H.; Vrugt, J.A.
2010-01-01
Multi-model averaging is currently receiving a surge of attention in the atmospheric, hydrologic, and statistical literature to explicitly handle conceptual model uncertainty in the analysis of environmental systems and derive predictive distributions of model output. Such density forecasts are nece
Boonyuen, Pakornpop; Wu, Falin; Phunthirawuth, Parwapath; Zhao, Yan
2016-10-01
In this research, satellite observation data were assimilated into Weather Research and Forecasting Model (WRF) by using Three-dimensional Variational Data Assimilation System (3DVAR) to analyze its impacts on heavy rainfall forecasts. The weather case for this research was during 13-18 September 2015. Tropical cyclone VAMCO, forming in South China Sea near with Vietnam, moved on west direction to the Northeast of Thailand. After passed through Vietnam, the tropical cyclone was become to depression and there was heavy rainfall throughout the area of Thailand. Observation data, used in this research, included microwave radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A), infrared radiance observations from Infrared Atmospheric Sounding Interferometer (IASI), and GPS radio occultation (RO) from the COSMIC and CHAMP missions. The experiments were designed in five cases, namely, 1) without data assimilation (CTRL); 2) with only RO data (RO); 3) with only AMSU-A data (AMSUA); 4) with only IASI data (IASI); and 5) with all of RO, AMSU-A and IASI data assimilation (ALL). Then all experiment results would be compared with both NCEP FNL (Final) Operational Global Analysis and the observation data from Thai Meteorological Department weather stations. The experiments result demonstrated that with microwave (AMSU-A), infrared (IASI) and GPS radio occultation (RO) data assimilation can produce the positive impact on analyses and forecast. All of satellite data assimilations have corresponding positive effects in term of temperature and humidity forecasting, and the GPS-RO assimilation produces the best of temperature and humidity forecast biases. The satellite data assimilation has a good impact on temperature and humidity in lower troposphere and vertical distribution that very helpful for heavy rainfall forecast improvement.
Humphrey, Greer B.; Gibbs, Matthew S.; Dandy, Graeme C.; Maier, Holger R.
2016-09-01
Monthly streamflow forecasts are needed to support water resources decision making in the South East of South Australia, where baseflow represents a significant proportion of the total streamflow and soil moisture and groundwater are important predictors of runoff. To address this requirement, the utility of a hybrid monthly streamflow forecasting approach is explored, whereby simulated soil moisture from the GR4J conceptual rainfall-runoff model is used to represent initial catchment conditions in a Bayesian artificial neural network (ANN) statistical forecasting model. To assess the performance of this hybrid forecasting method, a comparison is undertaken of the relative performances of the Bayesian ANN, the GR4J conceptual model and the hybrid streamflow forecasting approach for producing 1-month ahead streamflow forecasts at three key locations in the South East of South Australia. Particular attention is paid to the quantification of uncertainty in each of the forecast models and the potential for reducing forecast uncertainty by using the hybrid approach is considered. Case study results suggest that the hybrid models developed in this study are able to take advantage of the complementary strengths of both the ANN models and the GR4J conceptual models. This was particularly the case when forecasting high flows, where the hybrid models were shown to outperform the two individual modelling approaches in terms of the accuracy of the median forecasts, as well as reliability and resolution of the forecast distributions. In addition, the forecast distributions generated by the hybrid models were up to 8 times more precise than those based on climatology; thus, providing a significant improvement on the information currently available to decision makers.
Port San Luis, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Port San Luis, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Charlotte Amalie, Virgin Islands Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Charlotte Amalie, Virgin Islands Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami...
Pago Pago, American Samoa Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Pago Pago, American Samoa Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Point Reyes, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Point Reyes, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Morehead City, North Carolina Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Morehead City, North Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Cordova, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Cordova, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
Craig, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Craig, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
Virginia Beach Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Virginia Beach, Virginia Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Unalaska, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Unalaska, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
A simulation model for forecasting downhill ski participation
Daniel J. Stynes; Daniel M. Spotts
1980-01-01
The purpose of this paper is to describe progress in the development of a general computer simulation model to forecast future levels of outdoor recreation participation. The model is applied and tested for downhill skiing in Michigan.
Santa Barbara, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Santa Barbara, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Elfin Cove, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Elfin Cove, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
Los Angeles, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Los Angeles, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
British Columbia, Canada Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The British Columbia, Canada Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Cape Hatteras, North Carolina Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Cape Hatteras, North Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Myrtle Beach, South Carolina Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Myrtle Beach, South Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
San Francisco, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The San Francisco, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Santa Monica, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Santa Monica, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Atlantic City, New Jersey Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Atlantic City, New Jersey Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Atka, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Atka, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite...
Nantucket, Massachusetts Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Nantucket, Massachusetts Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Crescent City, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Crescent City, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Chignik, Alaska Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Chignik, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
Port Angeles, Washington Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Port Angeles, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Christiansted, Virgin Islands Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Christiansted, Virgin Islands Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...
Eureka, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Eureka, California Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...
2008-10-01
Remote sensing of ocean color provides synoptic surface ocean bio -optical properties but is limited to real-time or climatological applications. Many...this, we couple satellite imagery with numerical circulation models to provide short-term (24-48 hr) forecasts of bio -optical properties. These are...physical processes control the bio -optical distribution patterns. We compare optical forecast results from three Navy models and two advection
Directory of Open Access Journals (Sweden)
M. P. Mittermaier
2008-05-01
Full Text Available A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used.
The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.
Mittermaier, M. P.
2008-05-01
A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used. The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang; Zhang, Yingchen
2016-11-14
This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.
With string model to time series forecasting
Pinčák, Richard; Bartoš, Erik
2015-10-01
Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.
Sea Fog Forecasting with Lagrangian Models
Lewis, J. M.
2014-12-01
In 1913, G. I. Taylor introduced us to a Lagrangian view of sea fog formation. He conducted his study off the coast of Newfoundland in the aftermath of the Titanic disaster. We briefly review Taylor's classic work and then apply these same principles to a case of sea fog formation and dissipation off the coast of California. The resources used in this study consist of: 1) land-based surface and upper-air observations, 2) NDBC (National Data Buoy Center) observations from moored buoys equipped to measure dew point temperature as well as the standard surface observations at sea (wind, sea surface temperature, pressure, and air temperature), 3) satellite observations of cloud, and 4) a one-dimensional (vertically directed) boundary layer model that tracks with the surface air motion and makes use of sophisticated turbulence-radiation parameterizations. Results of the investigation indicate that delicate interplay and interaction between the radiation and turbulence processes makes accurate forecasts of sea fog onset unlikely in the near future. This pessimistic attitude stems from inadequacy of the existing network of observations and uncertainties in modeling dynamical processes within the boundary layer.
The Impact of Lightning on Hurricane Rapid Intensification Forecasts Using the HWRF Model
Rosado, K.; Tallapragada, V.; Jenkins, G. S.
2016-12-01
In 2010, the National Oceanic and Atmospheric Administration (NOAA) created the Hurricane Forecast Improvement Project (HFIP) with the main goal of improving the tropical cyclone intensity and track forecasts by 50% in ten years. One of the focus areas is the improvement of the tropical cyclone rapid intensification (RI) forecasts. In order to contribute to this task, the role of lightning during the life cycle of a tropical cyclone using the NCEP operational HWRF hurricane model has been investigated. We ask two key research questions: (1) What is the functional relationship between atmospheric moisture content, lightning, and intensity in the HWRF model? and (2) How well does the HWRF model forecast the spatial distributions of lightning before, during, and after tropical cyclone intensification, especially for RI events? In order to address those questions, a lightning parameterization scheme called the Lightning Potential Index (LPI) was implemented into the HWRF model. The selected study cases to test the LPI implementation on the 2015 HWRF (operational version) are: Earl and Joaquin (North Atlantic), Haiyan (Western North Pacific), and Patricia (Eastern North Pacific). Five-day forecasts was executed on each case study with emphasis on rapid intensification periods. An extensive analysis between observed "best track" intensity, model intensity forecast, and potential for lightning forecast was performed. Preliminary results show that: (1) strong correlation between lightning and intensity changes does exists; and (2) the potential for lightning increases to its maximum peak a few hours prior to the peak intensity of the tropical cyclone. LPI peak values could potentially serve as indicator for future rapid intensification periods. Results from this investigation are giving us a better understanding of the mechanism behind lightning as a proxy for tropical cyclone steady state intensification and tropical cyclone rapid intensification processes. Improvement of
Decision Making Models Using Weather Forecast Information
Hiramatsu, Akio; Huynh, Van-Nam; Nakamori, Yoshiteru
2007-01-01
The quality of weather forecast has gradually improved, but weather information such as precipitation forecast is still uncertainty. Meteorologists have studied the use and economic value of weather information, and users have to translate weather information into their most desirable action. To maximize the economic value of users, the decision maker should select the optimum course of action for his company or project, based on an appropriate decision strategy under uncertain situations. In...
A Model of Debris Flow Forecast Based on the Water-Soil Coupling Mechanism
Institute of Scientific and Technical Information of China (English)
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.
基于PDL模型的城市化水平预测%Forecasting Urbanization Level Based on PDL Model
Institute of Scientific and Technical Information of China (English)
丁刚; 赵萍萍
2005-01-01
In the previous research, when the economic factor forecasting solution of Urbanization was used into practice, the users almost always ignored the effects of the lagged economic development factors. The accuracy of the forecasting should be decreased seriously by this way. In this paper, taking Gansu province as an example, the authors try to improve the forecasting accuracy on the basis of the unrestricted PDL (Polynomial Distributed Lag')Models. In the context, the ARIMA Models and CPPS software are also applied to finish the forecasting more accurately.
Dynamic Hybrid Model for Short-Term Electricity Price Forecasting
Directory of Open Access Journals (Sweden)
Marin Cerjan
2014-05-01
Full Text Available Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.
Modeling and Forecasting the Volatility of Eastern European Emerging Markets
Directory of Open Access Journals (Sweden)
Sang Hoon Kang
2009-06-01
Full Text Available This study has attempted to seek a volatility forecasting model that can reflect sufficiently the long memory characteristic in the volatility of four Eastern European emerging stock markets, naThis study has attempted to seek a volatility forecasting model that can reflect sufficiently the long memory characteristic in the volatility of four Eastern European emerging stock markets, namely, Hungary, Poland, Russia, and Slovakia. From the results of our empirical analysis, we found that the FIGARCH model is better equipped to capture the long memory property in the volatility of these markets than the GARCH and IGARCH models. More importantly, the FIGARCH model is found to provide superior performance in one-day-ahead volatility forecasts. Thus, this study recommends researchers, portfolio managers, and traders to use the long memory FIGARCH model in analyzing and forecasting the volatility dynamics of Eastern European emerging markets.
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
Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection
DEFF Research Database (Denmark)
Bork, Lasse; Møller, Stig Vinther
2015-01-01
We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...
Verification of short lead time forecast models: applied to Kp and Dst forecasting
Wintoft, Peter; Wik, Magnus
2016-04-01
In the ongoing EU/H2020 project PROGRESS models that predicts Kp, Dst, and AE from L1 solar wind data will be used as inputs to radiation belt models. The possible lead times from L1 measurements are shorter (10s of minutes to hours) than the typical duration of the physical phenomena that should be forecast. Under these circumstances several metrics fail to single out trivial cases, such as persistence. In this work we explore metrics and approaches for short lead time forecasts. We apply these to current Kp and Dst forecast models. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637302.
Implications of the Regional Earthquake Likelihood Models test of earthquake forecasts in California
Directory of Open Access Journals (Sweden)
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
Improving of local ozone forecasting by integrated models.
Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš
2016-09-01
This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.
TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS: SELECTING OR COMBINING?
Institute of Scientific and Technical Information of China (English)
YU Lean; WANG Shouyang; K. K. Lai; Y.Nakamori
2005-01-01
Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this study.The results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.
A Modeler's Perspective on Space Weather Forecasting (Invited)
Wiltberger, M. J.
2010-12-01
Space physics is moving into a new era where numerical models originally developed for answering science questions are used as the basis for making operational space weather forecasts. Answering this challenge requires developments on multiple fronts requiring collaborations across space physics disciplines and between the research and operations communities. Since space weather in geospace is driven by the solar wind conditions a natural solution to improving the forecast lead time is to couple geospace models to heliospheric models. The quality of these forecast is dependent upon the ability of the heliospheric models to accurately model IMF Bz. Another challenge presented by moving into the forecasting arena is preparing the models for real-time operation which includes both computational performance and data redundancy issues. Moving into operations also presents modelers with an opportunity to assess their models performance over calculation intervals unprecedented duration. A key collaboration in the transition of models to operation is the discussion between forecasters and developers on what forecast parameters can accurately be predicted by the current generation of numerical models. This collaboration naturally includes a discussion of the definition of the best metrics to be used in quantitatively assessing performance.
Comparative Election Forecasting: Further Insights from Synthetic Models
Michael S. Lewis-Beck; Dassonneville, Ruth
2015-01-01
As an enterprise, election forecasting has spread and grown. Initial work began in the 1980s in the United States, eventually travelling to Western Europe, where it finds a current outlet in the most of the region’s democracies. However, that work has been confined to traditional approaches – statistical modeling or poll-watching. We import a new approach, which we call synthetic modeling. These forecasts come from hybrid models blending structural knowledge with contemporary p...
A model to forecast magma chamber rupture
Browning, John; Drymoni, Kyriaki; Gudmundsson, Agust
2016-04-01
An understanding of the amount of magma available to supply any given eruption is useful for determining the potential eruption magnitude and duration. Geodetic measurements and inversion techniques are often used to constrain volume changes within magma chambers, as well as constrain location and depth, but such models are incapable of calculating total magma storage. For example, during the 2012 unrest period at Santorini volcano, approximately 0.021 km3 of new magma entered a shallow chamber residing at around 4 km below the surface. This type of event is not unusual, and is in fact a necessary condition for the formation of a long-lived shallow chamber. The period of unrest ended without culminating in eruption, i.e the amount of magma which entered the chamber was insufficient to break the chamber and force magma further towards the surface. Using continuum-mechanics and fracture-mechanics principles, we present a model to calculate the amount of magma contained at shallow depth beneath active volcanoes. Here we discuss our model in the context of Santorini volcano, Greece. We demonstrate through structural analysis of dykes exposed within the Santorini caldera, previously published data on the volume of recent eruptions, and geodetic measurements of the 2011-2012 unrest period, that the measured 0.02% increase in volume of Santorini's shallow magma chamber was associated with magmatic excess pressure increase of around 1.1 MPa. This excess pressure was high enough to bring the chamber roof close to rupture and dyke injection. For volcanoes with known typical extrusion and intrusion (dyke) volumes, the new methodology presented here makes it possible to forecast the conditions for magma-chamber failure and dyke injection at any geodetically well-monitored volcano.
Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold
Directory of Open Access Journals (Sweden)
Qianjin Dong
2015-11-01
Full Text Available It is essential to consider the acceptable threshold in the assessment of a hydrological model because of the scarcity of research in the hydrology community and errors do not necessarily cause risk. Two forecast errors, including rainfall forecast error and peak flood forecast error, have been studied based on the reliability theory. The first order second moment (FOSM and bound methods are used to identify the reliability. Through the case study of the Dahuofang (DHF Reservoir, it is shown that the correlation between these two errors has great influence on the reliability index of hydrological model. In particular, the reliability index of the DHF hydrological model decreases with the increasing correlation. Based on the reliability theory, the proposed performance evaluation framework incorporating the acceptable forecast error threshold and correlation among the multiple errors can be used to evaluate the performance of a hydrological model and to quantify the uncertainties of a hydrological model output.
Forecast of useful energy for the TIMES-Norway model
Energy Technology Data Exchange (ETDEWEB)
Rosenberg, Eva
2012-07-25
A regional forecast of useful energy demand in seven Norwegian regions is calculated based on an earlier work with a national forecast. This forecast will be input to the energy system model TIMES-Norway and analyses will result in forecasts of energy use of different energy carriers with varying external conditions (not included in this report). The forecast presented here describes the methodology used and the resulting forecast of useful energy. lt is based on information of the long-term development of the economy by the Ministry of Finance, projections of population growths from Statistics Norway and several other studies. The definition of a forecast of useful energy demand is not absolute, but depends on the purpose. One has to be careful not to include parts that are a part of the energy system model, such as energy efficiency measures. In the forecast presented here the influence of new building regulations and the prohibition of production of incandescent light bulbs in EU etc. are included. Other energy efficiency measures such as energy management, heat pumps, tightening of leaks etc. are modelled as technologies to invest in and are included in the TIMES-Norway model. The elasticity between different energy carriers are handled by the TIMES-Norway model and some elasticity is also included as the possibility to invest in energy efficiency measures. The forecast results in an increase of the total useful energy from 2006 to 2050 by 18 o/o. The growth is expected to be highest in the regions South and East. The industry remains at a constant level in the base case and increased or reduced energy demand is analysed as different scenarios with the TIMES-Norway model. The most important driver is the population growth. Together with the assumptions made it results in increased useful energy demand in the household and service sectors of 25 o/o and 57 % respectively.(au)
Evaluation of induced seismicity forecast models in the Induced Seismicity Test Bench
Király, Eszter; Gischig, Valentin; Zechar, Jeremy; Doetsch, Joseph; Karvounis, Dimitrios; Wiemer, Stefan
2016-04-01
Induced earthquakes often accompany fluid injection, and the seismic hazard they pose threatens various underground engineering projects. Models to monitor and control induced seismic hazard with traffic light systems should be probabilistic, forward-looking, and updated as new data arrive. Here, we propose an Induced Seismicity Test Bench to test and rank such models. We apply the test bench to data from the Basel 2006 and Soultz-sous-Forêts 2004 geothermal stimulation projects, and we assess forecasts from two models that incorporate a different mix of physical understanding and stochastic representation of the induced sequences: Shapiro in Space (SiS) and Hydraulics and Seismics (HySei). SiS is based on three pillars: the seismicity rate is computed with help of the seismogenic index and a simple exponential decay of the seismicity; the magnitude distribution follows the Gutenberg-Richter relation; and seismicity is distributed in space based on smoothing seismicity during the learning period with 3D Gaussian kernels. The HySei model describes seismicity triggered by pressure diffusion with irreversible permeability enhancement. Our results show that neither model is fully superior to the other. HySei forecasts the seismicity rate well, but is only mediocre at forecasting the spatial distribution. On the other hand, SiS forecasts the spatial distribution well but not the seismicity rate. The shut-in phase is a difficult moment for both models in both reservoirs: the models tend to underpredict the seismicity rate around, and shortly after, shut-in. Ensemble models that combine HySei's rate forecast with SiS's spatial forecast outperform each individual model.
A refined fuzzy time series model for stock market forecasting
Jilani, Tahseen Ahmed; Burney, Syed Muhammad Aqil
2008-05-01
Time series models have been used to make predictions of stock prices, academic enrollments, weather, road accident casualties, etc. In this paper we present a simple time-variant fuzzy time series forecasting method. The proposed method uses heuristic approach to define frequency-density-based partitions of the universe of discourse. We have proposed a fuzzy metric to use the frequency-density-based partitioning. The proposed fuzzy metric also uses a trend predictor to calculate the forecast. The new method is applied for forecasting TAIEX and enrollments’ forecasting of the University of Alabama. It is shown that the proposed method work with higher accuracy as compared to other fuzzy time series methods developed for forecasting TAIEX and enrollments of the University of Alabama.
Forecasting electricity usage using univariate time series models
Hock-Eam, Lim; Chee-Yin, Yip
2014-12-01
Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.
Improving statistical forecasts of seasonal streamflows using hydrological model output
Directory of Open Access Journals (Sweden)
D. E. Robertson
2013-02-01
Full Text Available Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1 when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2 when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3 when the initial catchment condition is near saturation intermittently throughout the historical record.
Improving statistical forecasts of seasonal streamflows using hydrological model output
Robertson, D. E.; Pokhrel, P.; Wang, Q. J.
2013-02-01
Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.
Forecasting Models for Hydropower Unit Stability Using LS-SVM
Directory of Open Access Journals (Sweden)
Liangliang Qiao
2015-01-01
Full Text Available This paper discusses a least square support vector machine (LS-SVM approach for forecasting stability parameters of Francis turbine unit. To achieve training and testing data for the models, four field tests were presented, especially for the vibration in Y-direction of lower generator bearing (LGB and pressure in draft tube (DT. A heuristic method such as a neural network using Backpropagation (NNBP is introduced as a comparison model to examine the feasibility of forecasting performance. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to the NNBP, which is of significant importance to better monitor the unit safety and potential faults diagnosis.
A forecasting model of gaming revenues in Clark County, Nevada
Energy Technology Data Exchange (ETDEWEB)
Edwards, B.; Bando, A.; Bassett, G.; Rosen, A. [Argonne National Lab., IL (United States); Carlson, J.; Meenan, C. [Science Applications International Corp., Las Vegas, NV (United States)
1992-04-01
This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry, an identify the exogenous variables that affect gaming revenues. This model will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming related economic activity resulting from future events like the siting of a permanent high-level radioactive waste repository at Yucca Mountain.
Li, Ming; Wang, Q. J.; Bennett, James C.; Robertson, David E.
2016-09-01
This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.
Uncertainty calculation in transport models and forecasts
DEFF Research Database (Denmark)
Manzo, Stefano; Prato, Carlo Giacomo
in a four-stage transport model related to different variable distributions (to be used in a Monte Carlo simulation procedure), assignment procedures and levels of congestion, at both the link and the network level. The analysis used as case study the Næstved model, referring to the Danish town of Næstved2...... the uncertainty propagation pattern over time specific for key model outputs becomes strategically important. 1 Manzo, S., Nielsen, O. A. & Prato, C. G. (2014). The Effects of uncertainty in speed-flow curve parameters on a large-scale model. Transportation Research Record, 1, 30-37. 2 Manzo, S., Nielsen, O. A...
Directory of Open Access Journals (Sweden)
Miao Tian
2016-08-01
Full Text Available This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI. About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR data were selected to develop the ARIMA models from the erecting stage to the maturity stage of winter wheat (early March to late May in each year at a ten-day interval of the years from 2000 to 2009. We take the study area overlying on the administration map around the study area, and divide the study area into 17 parts where at least one weather station is located in each part. The pixels where the 17 weather stations are located are firstly chosen and studied for their fitting models, and then the best models for all pixels of the whole area are determined. According to the procedures for the models’ development, the selected best models for the 17 pixels are identified and the forecast is done with three steps. The forecasting results of the ARIMA models were compared with the monitoring ones. The results show that with reference to the categorized VTCI drought monitoring results, the categorized forecasting results of the ARIMA models are in good agreement with the monitoring ones. The categorized drought forecasting results of the ARIMA models are more severity in the northeast of the Plain in April 2009, which are in good agreements with the monitoring ones. The absolute errors of the AR(1 models are lower than the SARIMA models, both in the frequency distributions and in the statistic results. However, the ability of SARIMA models to detect the changes of the drought situation is better than the AR(1 models. These results indicate that the ARIMA models can better forecast the category and extent of droughts and can be applied to forecast droughts in the Plain.
Medium Range Forecast (MRF) and Nested Grid Model (NGM)
National Oceanic and Atmospheric Administration, Department of Commerce — The Nested Grid Model (NGM) and Medium Range Forecast (MRF) Archive is historical digital data set DSI-6140, archived at the NOAA National Centers for Environmental...
Formation of an Integrated Stock Price Forecast Model in Lithuania
Directory of Open Access Journals (Sweden)
Audrius Dzikevičius
2016-12-01
Full Text Available Technical and fundamental analyses are widely used to forecast stock prices due to lack of knowledge of other modern models and methods such as Residual Income Model, ANN-APGARCH, Support Vector Machine, Probabilistic Neural Network and Genetic Fuzzy Systems. Although stock price forecast models integrating both technical and fundamental analyses are currently used widely, their integration is not justified comprehensively enough. This paper discusses theoretical one-factor and multi-factor stock price forecast models already applied by investors at a global level and determines possibility to create and apply practically a stock price forecast model which integrates fundamental and technical analysis with the reference to the Lithuanian stock market. The research is aimed to determine the relationship between stock prices of the 14 Lithuanian companies listed in the Main List by the Nasdaq OMX Baltic and various fundamental variables. Based on correlation and regression analysis results and application of c-Squared Test, ANOVA method, a general stock price forecast model is generated. This paper discusses practical implications how the developed model can be used to forecast stock prices by individual investors and suggests additional check measures.
Bennett, James C.; Wang, Q. J.; Li, Ming; Robertson, David E.; Schepen, Andrew
2016-10-01
We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean-atmosphere general circulation model (CGCM). The CGCM forecasts are post-processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post-processed CGCM forecasts are used to force the Wapaba monthly rainfall-runoff model. Uncertainty in the hydrological modeling is accounted for with a three-stage error model. Stage 1 applies the log-sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias-correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead-times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12 months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead-times. Forecast skill with respect to climatology is evident to lead-times of about 3 months. At longer lead-times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead-times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12 month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers.
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.
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.
Modeling And Forecasting Exchange-Rate Shocks
Andreou, A. S.; Zombanakis, George A.; Likothanassis, S. D.; Georgakopoulos, E.
1998-01-01
This paper considers the extent to which the application of neural networks methodology can be used in order to forecast exchange-rate shocks. Four major foreign currency exchange rates against the Greek Drachma as well as the overnight interest rate in the Greek market are employed in an attempt to predict the extent to which the local currency may be suffering an attack. The forecasting is extended to the estimation of future exchange rates and interest rates. The MLP proved to be highly ...
Spatio-temporal modeling for real-time ozone forecasting.
Paci, Lucia; Gelfand, Alan E; Holland, David M
2013-05-01
The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts.
Macroeconomic Forecasts in Models with Bayesian Averaging of Classical Estimates
Directory of Open Access Journals (Sweden)
Piotr Białowolski
2012-03-01
Full Text Available The aim of this paper is to construct a forecasting model oriented on predicting basic macroeconomic variables, namely: the GDP growth rate, the unemployment rate, and the consumer price inflation. In order to select the set of the best regressors, Bayesian Averaging of Classical Estimators (BACE is employed. The models are atheoretical (i.e. they do not reflect causal relationships postulated by the macroeconomic theory and the role of regressors is played by business and consumer tendency survey-based indicators. Additionally, survey-based indicators are included with a lag that enables to forecast the variables of interest (GDP, unemployment, and inflation for the four forthcoming quarters without the need to make any additional assumptions concerning the values of predictor variables in the forecast period. Bayesian Averaging of Classical Estimators is a method allowing for full and controlled overview of all econometric models which can be obtained out of a particular set of regressors. In this paper authors describe the method of generating a family of econometric models and the procedure for selection of a final forecasting model. Verification of the procedure is performed by means of out-of-sample forecasts of main economic variables for the quarters of 2011. The accuracy of the forecasts implies that there is still a need to search for new solutions in the atheoretical modelling.
Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.
2011-12-01
The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.
Modelling and forecasting Turkish residential electricity demand
Energy Technology Data Exchange (ETDEWEB)
Dilaver, Zafer, E-mail: Z.dilaver@surrey.ac.uk [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom); The Republic of Turkey Prime Ministry, PK 06573, Ankara (Turkey); Hunt, Lester C [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom)
2011-06-15
This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being -0.09 and -0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008. - Research Highlights: > Estimated short run and long run expenditure elasticities of 0.38 and 1.57, respectively. > Estimated short run and long run price elasticities of -0.09 and -0.38, respectively. > Estimated UEDT has increasing (i.e. energy using) and decreasing (i.e. energy saving) periods. > Predicted Turkish residential electricity demand between 48 and 80 TWh in 2020.
A New Method for Grey Forecasting Model Group
Institute of Scientific and Technical Information of China (English)
李峰; 王仲东; 宋中民
2002-01-01
In order to describe the characteristics of some systems, such as the process of economic and product forecasting, a lot of discrete data may be used. Although they are discrete, the inside law can be-founded by some methods. For a series that the discrete degree is large and the integrated tendency is ascending, a new method for grey forecasting model group is given by the grey system theory. The method is that it firstly transforms original data, chooses some clique values and divides original data into groups by different clique values; then, it establishes non-equigap GM(1, 1) model for different groups and searches forecasting area of original data by the solution of model. At the end of the paper, the result of reliability of forecasting value is obtained. It is shown that the method is feasible.
Short-Termed Integrated Forecasting System: 1993 Model documentation report
Energy Technology Data Exchange (ETDEWEB)
1993-05-01
The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.
Modeling and forecasting the peak flows of a river
Directory of Open Access Journals (Sweden)
Mario Lefebvre
2002-01-01
Full Text Available A stochastic model is found for the value of the peak flows of the Mistassibi river in Québec, Canada, when the river is in spate. Next, the objective is to forecast the value of the coming peak flow about four days in advance, when the flow begins to show a marked increase. Both the stochastic model proposed in the paper and a model based on linear regression are used to produce the forecasts. The quality of the forecasts is assessed by considering the standard errors and the peak criterion. The forecasts are much more accurate than those obtained by taking the mean value of the previous peak flows.
Short-Termed Integrated Forecasting System: 1993 Model documentation report
Energy Technology Data Exchange (ETDEWEB)
1993-05-01
The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.
ECONOMIC FORECASTS BASED ON ECONOMETRIC MODELS USING EViews 5
Directory of Open Access Journals (Sweden)
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.
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
Institute of Scientific and Technical Information of China (English)
龚文杰; 段晓燕; 张智晟
2012-01-01
For solving the drawback of the traditional BP algorithm, including immerse in the local minimal points, slow convergence rate and complex programming, short-term load forecasting model based on diagonal recursive neural network using estimation of distribution algorithm is constructed in this paper. The model adopted estimation of distribution algorithm to optimize diagonal recursive neural network. The simulation results show that mean absolute percentage error and maximum relative error can decrease 1. 097% and 2. 55% .respectively. The proposed forecasting model can obtain satisfactory forecasting precision, and it possesses good forecasting stability and adaptability.%针对传统的BP算法易陷入局部极小点,收敛速度慢,编程复杂等缺点,本文提出基于分布估计算法的对角递归神经网络的短期负荷预测模型.该模型采用分布估计算法对对角递归神经网络进行优化,仿真结果表明,该预测模型平均绝对误差降低1.097％,最大相对误差降低2.55％,该模型获得较满意的预测精度,具有较高的预测稳定性和较好的适应能力.
Probabilistic flood forecast: Exact and approximate predictive distributions
Krzysztofowicz, Roman
2014-09-01
For quantification of predictive uncertainty at the forecast time t0, the future hydrograph is viewed as a discrete-time continuous-state stochastic process {Hn: n=1,…,N}, where Hn is the river stage at time instance tn>t0. The probabilistic flood forecast (PFF) should specify a sequence of exceedance functions {F‾n: n=1,…,N} such that F‾n(h)=P(Zn>h), where P stands for probability, and Zn is the maximum river stage within time interval (t0,tn], practically Zn=max{H1,…,Hn}. This article presents a method for deriving the exact PFF from a probabilistic stage transition forecast (PSTF) produced by the Bayesian forecasting system (BFS). It then recalls (i) the bounds on F‾n, which can be derived cheaply from a probabilistic river stage forecast (PRSF) produced by a simpler version of the BFS, and (ii) an approximation to F‾n, which can be constructed from the bounds via a recursive linear interpolator (RLI) without information about the stochastic dependence in the process {H1,…,Hn}, as this information is not provided by the PRSF. The RLI is substantiated by comparing the approximate PFF against the exact PFF. Being reasonably accurate and very simple, the RLI may be attractive for real-time flood forecasting in systems of lesser complexity. All methods are illustrated with a case study for a 1430 km headwater basin wherein the PFF is produced for a 72-h interval discretized into 6-h steps.
Yu, Wansik; NAKAKITA, Eiichi; Jung, Kwansue
2016-01-01
This paper investigates the applicability of ensemble forecasts of numerical weather prediction (NWP) model for flood forecasting. In this study, 10 km resolution ensemble rainfalls forecast and their downscaled forecasts of 2 km resolution were used in the hydrologic model as input data for flood forecasting and application of flood early warning. Ensemble data consists of 51 members and 48 hr forecast time. Ensemble outputs are verified spatially whether they can produce suitable rainfall p...
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.
CSIR Research Space (South Africa)
Das, Sonali
2010-01-01
Full Text Available This paper uses the dynamic factor model framework, which accommodates a large cross-section of macroeconomic time series, for forecasting regional house price inflation. In this study, the authors forecast house price inflation for five...
A Bayesian Model Committee Approach to Forecasting Global Solar Radiation
Lauret, Philippe; Muselli, Marc; David, Mathieu; Diagne, Hadja; Voyant, Cyril
2012-01-01
This paper proposes to use a rather new modelling approach in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving Average (ARMA) and Neural Network (NN) models are combined to form a model committee. The Bayesian inference is used to affect a probability to each model in the committee. Hence, each model's predictions are weighted by their respective probability. The models are fitted to one year of hourly Global Horizontal Irradiance (GHI) measurements. Another year (the test set) is used for making genuine one hour ahead (h+1) out-of-sample forecast comparisons. The proposed approach is benchmarked against the persistence model. The very first results show an improvement brought by this approach.
A complex autoregressive model and application to monthly temperature forecasts
Directory of Open Access Journals (Sweden)
X. Gu
2005-11-01
Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.
Forecasting inflation in Montenegro using univariate time series models
Directory of Open Access Journals (Sweden)
Milena Lipovina-Božović
2015-04-01
Full Text Available The analysis of price trends and their prognosis is one of the key tasks of the economic authorities in each country. Due to the nature of the Montenegrin economy as small and open economy with euro as currency, forecasting inflation is very specific which is more difficult due to low quality of the data. This paper analyzes the utility and applicability of univariate time series models for forecasting price index in Montenegro. Data analysis of key macroeconomic movements in previous decades indicates the presence of many possible determinants that could influence forecasting result. This paper concludes that the forecasting models (ARIMA based only on its own previous values cannot adequately cover the key factors that determine the price level in the future, probably because of the existence of numerous external factors that influence the price movement in Montenegro.
Application of Markov Model in Crude Oil Price Forecasting
Directory of Open Access Journals (Sweden)
Nuhu Isah
2017-08-01
Full Text Available Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.
Forecasting unconventional resource productivity - A spatial Bayesian model
Montgomery, J.; O'sullivan, F.
2015-12-01
Today's low prices mean that unconventional oil and gas development requires ever greater efficiency and better development decision-making. Inter and intra-field variability in well productivity, which is a major contemporary driver of uncertainty regarding resource size and its economics is driven by factors including geological conditions, well and completion design (which companies vary as they seek to optimize their performance), and uncertainty about the nature of fracture propagation. Geological conditions are often not be well understood early on in development campaigns, but nevertheless critical assessments and decisions must be made regarding the value of drilling an area and the placement of wells. In these situations, location provides a reasonable proxy for geology and the "rock quality." We propose a spatial Bayesian model for forecasting acreage quality, which improves decision-making by leveraging available production data and provides a framework for statistically studying the influence of different parameters on well productivity. Our approach consists of subdividing a field into sections and forming prior distributions for productivity in each section based on knowledge about the overall field. Production data from wells is used to update these estimates in a Bayesian fashion, improving model accuracy far more rapidly and with less sensitivity to outliers than a model that simply establishes an "average" productivity in each section. Additionally, forecasts using this model capture the importance of uncertainty—either due to a lack of information or for areas that demonstrate greater geological risk. We demonstrate the forecasting utility of this method using public data and also provide examples of how information from this model can be combined with knowledge about a field's geology or changes in technology to better quantify development risk. This approach represents an important shift in the way that production data is used to guide
Skills of different mesoscale models over Indian region during monsoon season: Forecast errors
Indian Academy of Sciences (India)
Someshwar Das; Raghavendra Ashrit; Gopal Raman Iyengar; Saji Mohandas; M Das Gupta; John P George; E N Rajagopal; Surya Kanti Dutta
2008-10-01
Performance of four mesoscale models namely,the MM5,ETA,RSM and WRF,run at NCMRWF for short range weather forecasting has been examined during monsoon-2006.Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind,temperature,speciﬁc humidity,geopotential height,rainfall,systematic errors,root mean square errors and speciﬁc events like the monsoon depressions. It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none ’.Perhaps an ensemble approach would be the best.However, if we must make a ﬁnal verdict,it can be stated that in general,(i)the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and,the MM5 is able to produce best All India rainfall forecasts in day-3,but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India,(ii)the MM5 is able to produce least RMSE of wind and geopotential ﬁelds at most of the time,and (iii)the RSM is able to produce least errors in the day-1 forecasts of the tracks,while the ETA model produces least errors in the day-3 forecasts.
Modeling for Growth and Forecasting of Pulse Production in Bangladesh
Directory of Open Access Journals (Sweden)
Niaz Md. FarhatRahman
2013-05-01
Full Text Available The present study was carried out to estimate growth pattern and examine the best ARIMA model to efficiently forecasting pigeon pea, chickpea and field pea pulse production in Bangladesh. It appeared that the time series data for pigeon pea, chickpea and field pea were 1st order homogenous stationary. Two types of models namely Box-Jenkins type Autoregressive Integrated Moving Average (ARIMA and deterministic type growth models, are examined to identify the best forecasting models for pigeon pea, chickpea and field pea pulse production in Bangladesh. The study revealed that the best models were ARIMA (1, 1 and 1, ARIMA (0, 1 and 0 and ARIMA (1, 1 and 3 for pigeon pea, chickpea and field pea pulse production, respectively. Among the deterministic type growth models, the cubic model is best for pigeon pea, chickpea and field pea pulse production. The analysis indicated that short-term forecasts were more efficient for ARIMA models compared to the deterministic models. The production uncertainty of pulse could be minimized if production were forecasted well and necessary steps were taken against losses. The findings of this study would be more useful for policy makers, researchers as well as producers in order to forecast future national pulse production more accurately in the short run.
Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling.
Ye, Hao; Beamish, Richard J; Glaser, Sarah M; Grant, Sue C H; Hsieh, Chih-Hao; Richards, Laura J; Schnute, Jon T; Sugihara, George
2015-03-31
It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner-recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts.
A national econometric forecasting model of the dental sector.
Feldstein, P J; Roehrig, C S
1980-01-01
The Econometric Model of the the Dental Sector forecasts a broad range of dental sector variables, including dental care prices; the amount of care produced and consumed; employment of hygienists, dental assistants, and clericals; hours worked by dentists; dental incomes; and number of dentists. These forecasts are based upon values specified by the user for the various factors which help determine the supply an demand for dental care, such as the size of the population, per capita income, th...
Impact of festival factor on electric quantity multiplication forecast model
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
This research aims to improve the forecasting precision of electric quantity. It is discovered that the total electricity consumption considerably increased during the Spring Festival by the analysis of the electric quantity time series from 2002 to 2007 in Shandong province. The festival factor is ascertained to be one of the important seasonal factors affecting the electric quantity fluctuations, and the multiplication model for forecasting is improved by introducing corresponding variables and parameters...
Bayesian Hierarchical Models to Augment the Mediterranean Forecast System
2016-06-07
year. Our goal is to develop an ensemble ocean forecast methodology, using Bayesian Hierarchical Modelling (BHM) tools . The ocean ensemble forecast...from above); i.e. we assume Ut ~ Z Λt1/2. WORK COMPLETED The prototype MFS-Wind-BHM was designed and implemented based on stochastic...coding refinements we implemented on the prototype surface wind BHM. A DWF event in February 2005, in the Gulf of Lions, was identified for reforecast
Interval forecasts of a novelty hybrid model for wind speeds
Shanshan Qin; Feng Liu; Jianzhou Wang; Yiliao Song
2015-01-01
The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values...
Ensemble Forecasting of Tropical Cyclone Motion Using a Baroclinic Model
Institute of Scientific and Technical Information of China (English)
Xiaqiong ZHOU; Johnny C.L.CHEN
2006-01-01
The purpose of this study is to investigate the effectiveness of two different ensemble forecasting (EF) techniques-the lagged-averaged forecast (LAF) and the breeding of growing modes (BGM). In the BGM experiments, the vortex and the environment are perturbed separately (named BGMV and BGME).Tropical cyclone (TC) motions in two difficult situations are studied: a large vortex interacting with its environment, and an apparent binary interaction. The former is Typhoon Yancy and the latter involves Typhoon Ed and super Typhoon Flo, all occurring during the Tropical Cyclone Motion Experiment TCM-90. The model used is the baroclinic model of the University of New South Wales. The lateral boundary tendencies are computed from atmospheric analysis data. Only the relative skill of the ensemble forecast mean over the control run is used to evaluate the effectiveness of the EF methods, although the EF technique is also used to quantify forecast uncertainty in some studies. In the case of Yancy, the ensemble mean forecasts of each of the three methodologies are better than that of the control, with LAF being the best. The mean track of the LAF is close to the best track, and it predicts landfall over Taiwan. The improvements in LAF and the full BGM where both the environment and vortex are perturbed suggest the importance of combining the perturbation of the vortex and environment when the interaction between the two is appreciable. In the binary interaction case of Ed and Flo, the forecasts of Ed appear to be insensitive to perturbations of the environment and/or the vortex, which apparently results from erroneous forecasts by the model of the interaction between the subtropical ridge and Ed, as well as from the interaction between the two typhoons, thus reducing the effectiveness of the EF technique. This conclusion is reached through sensitivity experiments on the domain of the model and by adding or eliminating certain features in the model atmosphere. Nevertheless, the
A model for Long-term Industrial Energy Forecasting (LIEF)
Energy Technology Data Exchange (ETDEWEB)
Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)
1992-02-01
The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.
Optimization of multi-model ensemble forecasting of typhoon waves
Directory of Open Access Journals (Sweden)
Shun-qi Pan
2016-01-01
Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.
A comparative verification of high resolution precipitation forecasts using model output statistics
van der Plas, Emiel; Schmeits, Maurice; Hooijman, Nicolien; Kok, Kees
2017-04-01
Verification of localized events such as precipitation has become even more challenging with the advent of high-resolution meso-scale numerical weather prediction (NWP). The realism of a forecast suggests that it should compare well against precipitation radar imagery with similar resolution, both spatially and temporally. Spatial verification methods solve some of the representativity issues that point verification gives rise to. In this study a verification strategy based on model output statistics is applied that aims to address both double penalty and resolution effects that are inherent to comparisons of NWP models with different resolutions. Using predictors based on spatial precipitation patterns around a set of stations, an extended logistic regression (ELR) equation is deduced, leading to a probability forecast distribution of precipitation for each NWP model, analysis and lead time. The ELR equations are derived for predictands based on areal calibrated radar precipitation and SYNOP observations. The aim is to extract maximum information from a series of precipitation forecasts, like a trained forecaster would. The method is applied to the non-hydrostatic model Harmonie (2.5 km resolution), Hirlam (11 km resolution) and the ECMWF model (16 km resolution), overall yielding similar Brier skill scores for the 3 post-processed models, but larger differences for individual lead times. Besides, the Fractions Skill Score is computed using the 3 deterministic forecasts, showing somewhat better skill for the Harmonie model. In other words, despite the realism of Harmonie precipitation forecasts, they only perform similarly or somewhat better than precipitation forecasts from the 2 lower resolution models, at least in the Netherlands.
Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting
Directory of Open Access Journals (Sweden)
Jianjin Wang
2017-01-01
Full Text Available Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper and knowledge-based method (traditional hydrological model may booster simulation accuracy. In this study, we proposed a new back-propagation (BP neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.
Wavelet regression model in forecasting crude oil price
Hamid, Mohd Helmie; Shabri, Ani
2017-05-01
This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.
Functional dynamic factor models with application to yield curve forecasting
Hays, Spencer
2012-09-01
Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation- maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.
A model for Long-term Industrial Energy Forecasting (LIEF)
Energy Technology Data Exchange (ETDEWEB)
Ross, M. (Lawrence Berkeley Lab., CA (United States) Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.); Hwang, R. (Lawrence Berkeley Lab., CA (United States))
1992-02-01
The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.
Model Uncertainty and Exchange Rate Forecasting
Kouwenberg, Roy; Markiewicz, Agnieszka; Verhoeks, Ralph; Zwinkels, Remco
2013-01-01
textabstractWe propose a theoretical framework of exchange rate behavior where investors focus on a subset of economic fundamentals. We find that any adjustment in the set of predictors used by investors leads to changes in the relation between the exchange rate and fundamentals. We test the validity of this framework via a backward elimination rule which captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample forecasting tests show that the backward elimi...
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...
Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets
Directory of Open Access Journals (Sweden)
Erie Febrian
2014-11-01
Full Text Available Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in different volatility approximations. The concern becomes more complicated when one tries to use the forecasting for asset distribution and risk management purposes in the linked regional markets. This paper aims at observing the effectiveness of the contending models of statistical and econometric volatility forecasting in the three South-east Asian prominent capital markets, i.e. STI, KLSE, and JKSE. In this paper, we evaluate eleven different models based on two classes of evaluation measures, i.e. symmetric and asymmetric error statistics, following Kumar's (2006 framework. We employ 10-year data as in sample and 6-month data as out of sample to construct and test the models, consecutively. The resulting superior methods, which are selected based on the out of sample forecasts and some evaluation measures in the respective markets, are then used to assess the markets cointegration. We find that the best volatility forecasting models for JKSE, KLSE, and STI are GARCH (2,1, GARCH(3,1, and GARCH (1,1, respectively. We also find that international portfolio investors cannot benefit from diversification among these three equity markets as they are cointegrated.
Nearest neighbour models for local and regional avalanche forecasting
Directory of Open Access Journals (Sweden)
M. Gassner
2002-01-01
Full Text Available This paper presents two avalanche forecasting applications NXD2000 and NXD-REG which were developed at the Swiss Federal Institute for Snow and Avalanche Re-search (SLF. Even both are based on the nearest neighbour method they are targeted to different scales. NXD2000 is used to forecast avalanches on a local scale. It is operated by avalanche forecasters responsible for snow safety at snow sport areas, villages or cross country roads. The area covered ranges from 10 km2 up to 100 km2 depending on the climatological homogeneity. It provides the forecaster with ten most similar days to a given situation. The observed avalanches of these days are an indication of the actual avalanche danger. NXD-REG is used operationally by the Swiss avalanche warning service for regional avalanche forecasting. The Nearest Neighbour approach is applied to the data sets of 60 observer stations. The results of each station are then compiled into a map of current and future avalanche hazard. Evaluation of the model by cross-validation has shown that the model can reproduce the official SLF avalanche forecasts in about 52% of the days.
A review of forecasting models for new products
Directory of Open Access Journals (Sweden)
Marta Mas-Machuca
2014-02-01
Full Text Available Purpose. The main objective of this article is to present an up-to-date review of new product forecasting techniques. Design/methodology/approach: A systematic review of forecasting journals was carried out using the ISI-Web of Knowledge database. Several articles were retrieved and examined, and forecasting techniques relevant to this study were selected and assessed. Findings: The strengths, weaknesses and applications of the main forecasting models are discussed to examine trends and set future challenges. Research limitations/implications: A theoretical reference framework for forecasting techniques classified into judgmental, consumer/market research, cause-effect and artificial intelligence is proposed. Future research can assess these models qualitatively. Practical implications: Companies are currently motivated to launch new products and thus attract new customers to expand their market share. In order to reduce uncertainty and risk, many companies go to extra lengths to forecast sales accurately using several techniques. Originality/value: This article outlines new lines of research on the improvement of new product performance which will aid managers in decision making and allow companies to sustain their competitive advantages in this challenging world.
Institute of Scientific and Technical Information of China (English)
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.
Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint
Energy Technology Data Exchange (ETDEWEB)
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.
Directory of Open Access Journals (Sweden)
D. Mellor
2000-01-01
Full Text Available Key issues involved in converting MTB ensemble forecasts of rainfall into ensemble forecasts of runoff are addressed. The physically-based distributed modelling system, SHETRAN, is parameterised for the Brue catchment, and used to assess the impact of averaging spatially variable MTB rainfall inputs on the accuracy of simulated runoff response. Averaging is found to have little impact for wet antecedent conditions and to lead to some underestimation of peak discharge under dry catchment conditions. The simpler ARNO modelling system is also parameterised for the Brue and SHETRAN and ARNO calibration and validation results are found to be similar. Ensemble forecasts of runoff generated using both SHETRAN and the simpler ARNO modelling system are compared. The ensemble is more spread out with the SHETRAN model, and a likely explanation is that the ARNO model introduces too much smoothing. Nevertheless, the forecasting performance of the simpler model could be adequate for flood warning purposes. Keywords: SHETRAN, ARNO, HYREX, rainfall-runoff model, Brue, real-time flow forecasting
Forecasting flood-prone areas using Shannon's entropy model
Haghizadeh, Ali; Siahkamari, Safoura; Haghiabi, Amir Hamzeh; Rahmati, Omid
2017-04-01
With regard to the lack of quality information and data in watersheds, it is of high importance to present a new method for evaluating flood potential. Shannon's entropy model is a new model in evaluating dangers and it has not yet been used to evaluate flood potential. Therefore, being a new model in determining flood potential, it requires evaluation and investigation in different regions and this study is going to deal with this issue. For to this purpose, 70 flooding areas were recognized and their distribution map was provided by ArcGIS10.2 software in the study area. Information layers of altitude, slope angle, slope aspect, plan curvature, drainage density, distance from the river, topographic wetness index (TWI), lithology, soil type, and land use were recognized as factors affecting flooding and the mentioned maps were provided and digitized by GIS environment. Then, flood susceptibility forecasting map was provided and model accuracy evaluation was conducted using ROC curve and 30% flooding areas express good precision of the model (73.5%) for the study area.
Validation of Model Forecasts of the Ambient Solar Wind
Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.
2009-01-01
Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.
Forecasting the Euro exchange rate using vector error correction models
Aarle, B. van; Bos, M.; Hlouskova, J.
2000-01-01
Forecasting the Euro Exchange Rate Using Vector Error Correction Models. — This paper presents an exchange rate model for the Euro exchange rates of four major currencies, namely the US dollar, the British pound, the Japanese yen and the Swiss franc. The model is based on the monetary approach of ex
Improved forecasting of thermospheric densities using multi-model ensembles
Elvidge, Sean; Godinez, Humberto C.; Angling, Matthew J.
2016-07-01
This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. The MME, with its reduced uncertainties, can then be used as the initial conditions in a physics-based thermosphere model for forecasting. This should increase the forecast skill since a reduction in the errors of the initial conditions of a model generally increases model skill. In this paper the Thermosphere-Ionosphere Electrodynamic General Circulation Model (TIE-GCM), the US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00), and Global Ionosphere-Thermosphere Model (GITM) have been used to construct the MME. As well as comparisons between the MMEs and the "standard" runs of the model, the MME densities have been propagated forward in time using the TIE-GCM. It is shown that thermospheric forecasts of up to 6 h, using the MME, have a reduction in the root mean square error of greater than 60 %. The paper also highlights differences in model performance between times of solar minimum and maximum.
Evaluation of the performance of DIAS ionospheric forecasting models
Directory of Open Access Journals (Sweden)
Tsagouri Ioanna
2011-08-01
Full Text Available Nowcasting and forecasting ionospheric products and services for the European region are regularly provided since August 2006 through the European Digital upper Atmosphere Server (DIAS, http://dias.space.noa.gr. Currently, DIAS ionospheric forecasts are based on the online implementation of two models: (i the solar wind driven autoregression model for ionospheric short-term forecast (SWIF, which combines historical and real-time ionospheric observations with solar-wind parameters obtained in real time at the L1 point from NASA ACE spacecraft, and (ii the geomagnetically correlated autoregression model (GCAM, which is a time series forecasting method driven by a synthetic geomagnetic index. In this paper we investigate the operational ability and the accuracy of both DIAS models carrying out a metrics-based evaluation of their performance under all possible conditions. The analysis was established on the systematic comparison between models’ predictions with actual observations obtained over almost one solar cycle (1998–2007 at four European ionospheric locations (Athens, Chilton, Juliusruh and Rome and on the comparison of the models’ performance against two simple prediction strategies, the median- and the persistence-based predictions during storm conditions. The results verify operational validity for both models and quantify their prediction accuracy under all possible conditions in support of operational applications but also of comparative studies in assessing or expanding the current ionospheric forecasting capabilities.
Improving the Performance of Water Demand Forecasting Models by Using Weather Input
Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.
2014-01-01
Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv
Improving the Performance of Water Demand Forecasting Models by Using Weather Input
Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.
2014-01-01
Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv
Forecasting Performance of Asymmetric GARCH Stock Market Volatility Models
Directory of Open Access Journals (Sweden)
Hojin Lee
2009-12-01
Full Text Available We investigate the asymmetry between positive and negative returns in their effect on conditional variance of the stock market index and incorporate the characteristics to form an out-of-sample volatility forecast. Contrary to prior evidence, however, the results in this paper suggest that no asymmetric GARCH model is superior to basic GARCH(1,1 model. It is our prior knowledge that, for equity returns, it is unlikely that positive and negative shocks have the same impact on the volatility. In order to reflect this intuition, we implement three diagnostic tests for volatility models: the Sign Bias Test, the Negative Size Bias Test, and the Positive Size Bias Test and the tests against the alternatives of QGARCH and GJR-GARCH. The asymmetry test results indicate that the sign and the size of the unexpected return shock do not influence current volatility differently which contradicts our presumption that there are asymmetric effects in the stock market volatility. This result is in line with various diagnostic tests which are designed to determine whether the GARCH(1,1 volatility estimates adequately represent the data. The diagnostic tests in section 2 indicate that the GARCH(1,1 model for weekly KOSPI returns is robust to the misspecification test. We also investigate two representative asymmetric GARCH models, QGARCH and GJR-GARCH model, for our out-of-sample forecasting performance. The out-of-sample forecasting ability test reveals that no single model is clearly outperforming. It is seen that the GJR-GARCH and QGARCH model give mixed results in forecasting ability on all four criteria across all forecast horizons considered. Also, the predictive accuracy test of Diebold and Mariano based on both absolute and squared prediction errors suggest that the forecasts from the linear and asymmetric GARCH models need not be significantly different from each other.
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.
Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration
Energy Technology Data Exchange (ETDEWEB)
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.
Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint
Energy Technology Data Exchange (ETDEWEB)
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.
Improving urban streamflow forecasting using a high-resolution large scale modeling framework
Read, Laura; Hogue, Terri; Gochis, David; Salas, Fernando
2016-04-01
Urban flood forecasting is a critical component in effective water management, emergency response, regional planning, and disaster mitigation. As populations across the world continue to move to cities (~1.8% growth per year), and studies indicate that significant flood damages are occurring outside the floodplain in urban areas, the ability to model and forecast flow over the urban landscape becomes critical to maintaining infrastructure and society. In this work, we use the Weather Research and Forecasting- Hydrological (WRF-Hydro) modeling framework as a platform for testing improvements to representation of urban land cover, impervious surfaces, and urban infrastructure. The three improvements we evaluate include: updating the land cover to the latest 30-meter National Land Cover Dataset, routing flow over a high-resolution 30-meter grid, and testing a methodology for integrating an urban drainage network into the routing regime. We evaluate performance of these improvements in the WRF-Hydro model for specific flood events in the Denver-Metro Colorado domain, comparing to historic gaged streamflow for retrospective forecasts. Denver-Metro provides an interesting case study as it is a rapidly growing urban/peri-urban region with an active history of flooding events that have caused significant loss of life and property. Considering that the WRF-Hydro model will soon be implemented nationally in the U.S. to provide flow forecasts on the National Hydrography Dataset Plus river reaches - increasing capability from 3,600 forecast points to 2.7 million, we anticipate that this work will support validation of this service in urban areas for operational forecasting. Broadly, this research aims to provide guidance for integrating complex urban infrastructure with a large-scale, high resolution coupled land-surface and distributed hydrologic model.
Forecasting relativistic electron flux using dynamic multiple regression models
Directory of Open Access Journals (Sweden)
H.-L. Wei
2011-02-01
Full Text Available The forecast of high energy electron fluxes in the radiation belts is important because the exposure of modern spacecraft to high energy particles can result in significant damage to onboard systems. A comprehensive physical model of processes related to electron energisation that can be used for such a forecast has not yet been developed. In the present paper a systems identification approach is exploited to deduce a dynamic multiple regression model that can be used to predict the daily maximum of high energy electron fluxes at geosynchronous orbit from data. It is shown that the model developed provides reliable predictions.
Forecasting coconut production in the Philippines with ARIMA model
Lim, Cristina Teresa
2015-02-01
The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.
Challenging Issues on fog forecast with a three-dimensional fog forecast model
Masbou, M.
2012-12-01
Fog has a significant impact on economical aspect (traffic management and safety) as well as on environmental issues (fresh water source for the population and the biosphere in arid region). However, reliable fog and visibility forecasts stay challenging issue. Fog is generally a small scale phenomenon which is mostly affected by local advective transport, radiation, topography, vegetation, turbulent mixing at the surface as well as its microphysical structure. In order to consider these intertwined processes, the three-dimensional fog forecast model, COSMO-FOG, with a high vertical resolution with different microphysical complexity has been developed. This model includes a microphysical parameterisation based on the one-dimensional fog forecast model. The implementation of the cloud water droplets as a new prognostic variable allows a detailed definition of the sedimentation processes and the variations in visibility. Moreover, the turbulence scheme, based on a Mellor-Yamada 2.5 order and a closure of a 2nd order has been modified to improve the model behaviour in case of a stable atmosphere structure, occurring typically during night radiative fog episodes. The potential of COSMO-FOG will be presented in some realistic fog situations (flat, bumpy and complex terrain). The fog spatial extension will be compared with MSG satellite products for fog and low cloud. The interplays between dynamical, thermodynamical patterns and the soil-atmosphere interactions will be presented.
Product Design Time Forecasting by Kernel-Based Regression with Gaussian Distribution Weights
Directory of Open Access Journals (Sweden)
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.
Post-processing of Solar Irradiance Forecasts from WRF Model at Reunion Island
Diagne, Hadja Maïmouna; David, Mathieu; Boland, John; Schmutz, Nicolas; Lauret, Philippe
2014-01-01
International audience; An efficient use of solar energy production requires reliable forecast information on surface solar irradiance. This article aims at providing a model output statistics (MOS) method of improving solar irradiance forecasts from Weather Research and Forecasting (WRF) Model.The WRF model was used to produce one year of day ahead solar irradiance forecasts covering Reunion Island with an horizontal resolution of 3 km. These forecasts are refined with a Kalman filter using ...
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This paper develops a new inter-basin water transfer-supply and risk assessment model with consideration of rainfall forecast information. Firstly, based on the current state of reservoir and rainfall forecast information from the global forecast system (GFS), the actual diversion amount can be determined according to the inter-basin water transfer rules with the decision tree method; secondly, the reservoir supply operation system is used to distribute water resource of the inter-basin water transfer reservoir; finally, the integrated risk assessment model is built by selecting the reliability of water transfer, the reliability (water shortage risk), the resiliency and the vulnerability of water supply as risk analysis indexes. The case study shows that the inter-basin water transfer-supply model with rainfall forecast information considered can reduce the comprehensive risk and improve the utilization efficiency of water resource, as compared with conventional and optimal water distribution models.
Monahan, William B; Cook, Tammy; Melton, Forrest; Connor, Jeff; Bobowski, Ben
2013-01-01
Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis) to climate change in Rocky Mountain National Park (Colorado, USA). Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m(2)) show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change vulnerability and used
Directory of Open Access Journals (Sweden)
William B Monahan
Full Text Available Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis to climate change in Rocky Mountain National Park (Colorado, USA. Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m(2 show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change
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.
Forecasting wind-driven wildfires using an inverse modelling approach
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O. Rios
2013-12-01
Full Text Available A technology able to rapidly forecast wildlfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the on-going fire. The article at hand presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and a forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the high capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event. This work opens the door to further advances framework and more sophisticated models while keeping the computational time suitable for operativeness.
Description of Mixed-Phase Clouds in Weather Forecast and Climate Models
2014-09-30
1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Description of Mixed-Phase Clouds in Weather Forecast...TERM GOALS To develop improved parameterizations of so-called mixed-phase stratocumulus in numerical models of weather and climate, and of their...impact on the surface energy budget over the Arctic Ocean, their impact on the vertical structure of the lower troposphere and relationships to larger
Pappenberger, F.; K. J. Beven; N. M. Hunter; Bates, P. D.; B. T. Gouweleeuw; Thielen, J.; A. P. J. De De Roo
2005-01-01
International audience; The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall-runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessa...
Lake Michigan lake trout PCB model forecast post audit
Scenario forecasts for total PCBs in Lake Michigan (LM) lake trout were conducted using the linked LM2-Toxics and LM Food Chain models, supported by a suite of additional LM models. Efforts were conducted under the Lake Michigan Mass Balance Study and the post audit represents th...
Exchange Rate Forecasting Using Entropy Optimized Multivariate Wavelet Denoising Model
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Kaijian He
2014-01-01
Full Text Available Exchange rate is one of the key variables in the international economics and international trade. Its movement constitutes one of the most important dynamic systems, characterized by nonlinear behaviors. It becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulation and global integration worldwide. Facing the increasingly diversified and more integrated market environment, the forecasting model in the exchange markets needs to address the individual and interdependent heterogeneity. In this paper, we propose the heterogeneous market hypothesis- (HMH- based exchange rate modeling methodology to model the micromarket structure. Then we further propose the entropy optimized wavelet-based forecasting algorithm under the proposed methodology to forecast the exchange rate movement. The multivariate wavelet denoising algorithm is used to separate and extract the underlying data components with distinct features, which are modeled with multivariate time series models of different specifications and parameters. The maximum entropy is introduced to select the best basis and model parameters to construct the most effective forecasting algorithm. Empirical studies in both Chinese and European markets have been conducted to confirm the significant performance improvement when the proposed model is tested against the benchmark models.
Periodic Integration: Further Results on Model Selection and Forecasting
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1996-01-01
textabstractThis paper considers model selection and forecasting issues in two closely related models for nonstationary periodic autoregressive time series [PAR]. Periodically integrated seasonal time series [PIAR] need a periodic differencing filter to remove the stochastic trend. On the other
Weather modeling and forecasting of PV systems operation
Paulescu, Marius; Gravila, Paul; Badescu, Viorel
2012-01-01
In the past decade, there has been a substantial increase of grid-feeding photovoltaic applications, thus raising the importance of solar electricity in the energy mix. This trend is expected to continue and may even increase. Apart from the high initial investment cost, the fluctuating nature of the solar resource raises particular insertion problems in electrical networks. Proper grid managing demands short- and long-time forecasting of solar power plant output. Weather modeling and forecasting of PV systems operation is focused on this issue. Models for predicting the state of the sky, nowc
Forecast model of safety economy contribution rate of China
Institute of Scientific and Technical Information of China (English)
LIU Li-jun; SHI Shi-liang
2005-01-01
It is the rational and exact computation of the safety economy contribution rate that has the far-reaching realistic meaning to the improvement of society cognition to safety and the investment to the nation safety and the national macro-safety decision-makings. The accurate function between safety inputs and outputs was obtained through a founded econometric model. Then the forecasted safety economy contribution rate is 3.01% and the forecasted ratio between safety inputs and outputs is 1:1.81 in China in 2005. And the model accords with the practice of China and the results are satisfying.
Precipitation and temperature ensemble forecasts from single-value forecasts
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J. Schaake
2007-04-01
Full Text Available A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004. The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved.
Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods.
The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other
FOGCAST: Probabilistic fog forecasting based on operational (high-resolution) NWP models
Masbou, M.; Hacker, M.; Bentzien, S.
2013-12-01
numerical weather prediction systems: 1- COSMO-DE operational forecasts (50 vertical layers, dz_min=20m), 2- COSMO-DE forecasts with different vertical grid setups, 3- COSMO-DE forecasts with fog microphysics of the one dimensional fog forecast model, PAFOG 4- COSMO-FOG forecasts with a very high vertical resolution (60 layers, dz_min=4m) and an one-moment fog microphysics based on the PAFOG model. The results will quantify the impact of vertical grid resolution, and the importance of detailed cloud microphysics, considering explicitly cloud droplet distribution and sedimentation processes.
An updated subgrid orographic parameterization for global atmospheric forecast models
Choi, Hyun-Joo; Hong, Song-You
2015-12-01
A subgrid orographic parameterization (SOP) is updated by including the effects of orographic anisotropy and flow-blocking drag (FBD). The impact of the updated SOP on short-range forecasts is investigated using a global atmospheric forecast model applied to a heavy snowfall event over Korea on 4 January 2010. When the SOP is updated, the orographic drag in the lower troposphere noticeably increases owing to the additional FBD over mountainous regions. The enhanced drag directly weakens the excessive wind speed in the low troposphere and indirectly improves the temperature and mass fields over East Asia. In addition, the snowfall overestimation over Korea is improved by the reduced heat fluxes from the surface. The forecast improvements are robust regardless of the horizontal resolution of the model between T126 and T510. The parameterization is statistically evaluated based on the skill of the medium-range forecasts for February 2014. For the medium-range forecasts, the skill improvements of the wind speed and temperature in the low troposphere are observed globally and for East Asia while both positive and negative effects appear indirectly in the middle-upper troposphere. The statistical skill for the precipitation is mostly improved due to the improvements in the synoptic fields. The improvements are also found for seasonal simulation throughout the troposphere and stratosphere during boreal winter.
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.
Modelling and forecasting electricity price variability
Energy Technology Data Exchange (ETDEWEB)
Haugom, Erik
2012-07-01
The liberalization of electricity sectors around the world has induced a need for financial electricity markets. This thesis is mainly focused on calculating, modelling, and predicting volatility for financial electricity prices. The four first essays examine the liberalized Nordic electricity market. The purposes in these papers are to describe some stylized properties of high-frequency financial electricity data and to apply models that can explain and predict variation in volatility. The fifth essay examines how information from high-frequency electricity forward contracts can be used in order to improve electricity spot-price volatility predictions. This essay uses data from the Pennsylvania-New Jersey-Maryland wholesale electricity market in the U.S.A. Essay 1 describes some stylized properties of financial high-frequency electricity prices, their returns and volatilities at the Nordic electricity exchange, Nord Pool. The analyses focus on distribution properties, serial correlation, volatility clustering, the influence of extreme events and seasonality in the various measures. The objective of Essay 2 is to calculate, model, and predict realized volatility of financial electricity prices for quarterly and yearly contracts. The total variation is also separated into continuous and jump variation. Various market measures are also included in the models in order potentially to improve volatility predictions. Essay 3 compares day-ahead predictions of Nord Pool financial electricity price volatility obtained from a GARCH approach with those obtained using standard time-series techniques on realized volatility. The performances of a total of eight models (two representing the GARCH family and six representing standard autoregressive models) are compared and evaluated. Essay 4 examines whether predictions of day-ahead and week-ahead volatility can be improved by additionally including volatility and covariance effects from related financial electricity contracts
2017-01-01
Ecological niche models (ENMs) have increasingly been used to estimate the potential effects of climate change on species’ distributions worldwide. Recently, predictions of species abundance have also been obtained with such models, though knowledge about the climatic variables affecting species abundance is often lacking. To address this, we used a well-studied guild (temperate North American quail) and the Maxent modeling algorithm to compare model performance of three variable selection approaches: correlation/variable contribution (CVC), biological (i.e., variables known to affect species abundance), and random. We then applied the best approach to forecast potential distributions, under future climatic conditions, and analyze future potential distributions in light of available abundance data and presence-only occurrence data. To estimate species’ distributional shifts we generated ensemble forecasts using four global circulation models, four representative concentration pathways, and two time periods (2050 and 2070). Furthermore, we present distributional shifts where 75%, 90%, and 100% of our ensemble models agreed. The CVC variable selection approach outperformed our biological approach for four of the six species. Model projections indicated species-specific effects of climate change on future distributions of temperate North American quail. The Gambel’s quail (Callipepla gambelii) was the only species predicted to gain area in climatic suitability across all three scenarios of ensemble model agreement. Conversely, the scaled quail (Callipepla squamata) was the only species predicted to lose area in climatic suitability across all three scenarios of ensemble model agreement. Our models projected future loss of areas for the northern bobwhite (Colinus virginianus) and scaled quail in portions of their distributions which are currently areas of high abundance. Climatic variables that influence local abundance may not always scale up to influence
Jarnevich, Catherine S.; Young, Nicholas E; Sheffels, Trevor R.; Carter, Jacoby; Systma, Mark D.; Talbert, Colin
2017-01-01
Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu (Myocastor coypus [Molina, 1782]), we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model (GCM) predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs, but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.
Directory of Open Access Journals (Sweden)
Catherine S. Jarnevich
2017-01-01
Full Text Available Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu (Myocastor coypus [Molina, 1782], we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model (GCM predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs, but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.
Study of statistically correcting model CMAQ-MOS for forecasting regional air quality
Institute of Scientific and Technical Information of China (English)
XU Jianming; HE Jinhai; YANG Yuanqin; WANG Jiahe; XU Xiangde; LIU Yu; DING Guoan; CHEN Huailiang; HU Jiangkai; ZHANG Jianchun; WU Hao; LI Weiliang
2005-01-01
Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution
FORECASTING MODEL OF GHG EMISSION IN MANUFACTURING SECTORS OF THAILAND
Directory of Open Access Journals (Sweden)
Pruethsan Sutthichaimethee
2017-01-01
Full Text Available This study aims to analyze the modeling and forecasting the GHG emission of energy consumption in manufacturing sectors. The scope of the study is to analysis energy consumption and forecasting GHG emission of energy consumption for the next 10 years (2016-2025 and 25 years (2016-2040 by using ARIMAX model from the Input-output table of Thailand. The result shows that iron and steel has the highest value of energy consumption and followed by cement, fluorite, air transport, road freight transport, hotels and places of loading, coal and lignite, petrochemical products, other manufacturing, road passenger transport, respectively. The prediction results show that these models are effective in forecasting by measured by using RMSE, MAE, and MAPE. The results forecast of each model is as follows: 1 Model 1(2,1,1 shows that GHG emission will be increasing steadily and increasing at 25.17% by the year 2025 in comparison to 2016. 2 Model 2 (2,1,2 shows that GHG emission will be rising steadily and increasing at 41.51% by the year 2040 in comparison to 2016.
[Development of forecasting models for fatal road traffic injuries].
Tan, Aichun; Tian, Danping; Huang, Yuanxiu; Gao, Lin; Deng, Xin; Li, Li; He, Qiong; Chen, Tianmu; Hu, Guoqing; Wu, Jing
2014-02-01
To develop the forecasting models for fatal road traffic injuries and to provide evidence for predicting the future trends on road traffic injuries. Data on the mortality of road traffic injury including factors as gender and age in different countries, were obtained from the World Health Organization Mortality Database. Other information on GDP per capita, urbanization, motorization and education were collected from online resources of World Bank, WHO, the United Nations Population Division and other agencies. We fitted logarithmic models of road traffic injury mortality by gender and age group, including predictors of GDP per capita, urbanization, motorization and education. Sex- and age-specific forecasting models developed by WHO that including GDP per capita, education and time etc. were also fitted. Coefficient of determination(R(2)) was used to compare the performance between our modes and WHO models. 2 626 sets of data were collected from 153 countries/regions for both genders, between 1965 and 2010. The forecasting models of road traffic injury mortality based on GDP per capita, motorization, urbanization and education appeared to be statistically significant(P forecasting models that we developed seemed to be better than those developed by WHO.
Optimization of Evaporative Demand Models for Seasonal Drought Forecasting
McEvoy, D.; Huntington, J. L.; Hobbins, M.
2015-12-01
Providing reliable seasonal drought forecasts continues to pose a major challenge for scientists, end-users, and the water resources and agricultural communities. Precipitation (Prcp) forecasts beyond weather time scales are largely unreliable, so exploring new avenues to improve seasonal drought prediction is necessary to move towards applications and decision-making based on seasonal forecasts. A recent study has shown that evaporative demand (E0) anomaly forecasts from the Climate Forecast System Version 2 (CFSv2) are consistently more skillful than Prcp anomaly forecasts during drought events over CONUS, and E0 drought forecasts may be particularly useful during the growing season in the farming belts of the central and Midwestern CONUS. For this recent study, we used CFSv2 reforecasts to assess the skill of E0 and of its individual drivers (temperature, humidity, wind speed, and solar radiation), using the American Society for Civil Engineers Standardized Reference Evapotranspiration (ET0) Equation. Moderate skill was found in ET0, temperature, and humidity, with lesser skill in solar radiation, and no skill in wind. Therefore, forecasts of E0 based on models with no wind or solar radiation inputs may prove to be more skillful than the ASCE ET0. For this presentation we evaluate CFSv2 E0 reforecasts (1982-2009) from three different E0 models: (1) ASCE ET0; (2) Hargreaves and Samani (ET-HS), which is estimated from maximum and minimum temperature alone; and (3) Valiantzas (ET-V), which is a modified version of the Penman method for use when wind speed data are not available (or of poor quality) and is driven only by temperature, humidity, and solar radiation. The University of Idaho's gridded meteorological data (METDATA) were used as observations to evaluate CFSv2 and also to determine if ET0, ET-HS, and ET-V identify similar historical drought periods. We focus specifically on CFSv2 lead times of one, two, and three months, and season one forecasts; which are
Climate model forecast biases assessed with a perturbed physics ensemble
Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David
2017-09-01
Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.
Climate model forecast biases assessed with a perturbed physics ensemble
Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David
2016-10-01
Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.
Model for Adjustment of Aggregate Forecasts using Fuzzy Logic
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Taracena–Sanz L. F.
2010-07-01
Full Text Available This research suggests a contribution in the implementation of forecasting models. The proposed model is developed with the aim to fit the projection of demand to surroundings of firms, and this is based on three considerations that cause that in many cases the forecasts of the demand are different from reality, such as: 1 one of the problems most difficult to model in the forecasts is the uncertainty related to the information available; 2 the methods traditionally used by firms for the projection of demand mainly are based on past behavior of the market (historical demand; and 3 these methods do not consider in their analysis the factors that are influencing so that the observed behaviour occurs. Therefore, the proposed model is based on the implementation of Fuzzy Logic, integrating the main variables that affect the behavior of market demand, and which are not considered in the classical statistical methods. The model was applied to a bottling of carbonated beverages, and with the adjustment of the projection of demand a more reliable forecast was obtained.
SARX Model Application for Industrial Power Demand Forecasting in Brazil
Directory of Open Access Journals (Sweden)
Alessandra de Ávila Montini
2012-06-01
Full Text Available The objective of this paper is to propose the application of the SARX model to arrive at industrial power consumption forecasts in Brazil, which are critical to support decision-making in the energy sector, based on technical, economic and environmentally sustainable grounds. The proposed model has a seasonal component and considers the influence of exogenous variables on the projection of the dependent variable and utilizes an autoregressive process for residual modeling so as to improve its explanatory power. Five exogenous variables were included: industrial capacity utilization, industrial electricity tariff, industrial real revenues, exchange rate, and machinery and equipment inflation. In addition, the model assumed that power forecast was dependent on its own time lags and also on a dummy variable to reflect 2009 economic crisis. The study used 84 monthly observations, from January 2003 to December 2009. The backward method was used to select exogenous variables, assuming a 0.10 descriptive value. The results showed an adjusted coefficient of determination of 93.9% and all the estimated coefficients were statistically significant at a 0.10 descriptive level. Forecasts were also made from January to May 2010 at a 95% confidence interval, which included actual consumption values for this period. The SARX model has demonstrated an excellent performance for industrial power consumption forecasting in Brazil.
Yuan, Xing
2016-06-01
This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982-2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08-0.2. To compare with the observed
Modeling of the 2011 Japan Tsunami: Lessons for Near-Field Forecast
Wei, Yong; Chamberlin, Christopher; Titov, Vasily V.; Tang, Liujuan; Bernard, Eddie N.
2013-06-01
During the devastating 11 March 2011 Japanese tsunami, data from two tsunami detectors were used to determine the tsunami source within 1.5 h of earthquake origin time. For the first time, multiple near-field tsunami measurements of the 2011 Japanese tsunami were used to demonstrate the accuracy of the National Oceanic and Atmospheric Administration (NOAA) real-time flooding forecast system in the far field. To test the accuracy of the same forecast system in the near field, a total of 11 numerical models with grids telescoped to 2 arcsec (~60 m) were developed to hindcast the propagation and coastal inundation of the 2011 Japanese tsunami along the entire east coastline of Japan. Using the NOAA tsunami source computed in near real-time, the model results of tsunami propagation are validated with tsunami time series measured at different water depths offshore and near shore along Japan's coastline. The computed tsunami runup height and spatial distribution are highly consistent with post-tsunami survey data collected along the Japanese coastline. The computed inundation penetration also agrees well with survey data, giving a modeling accuracy of 85.5 % for the inundation areas along 800 km of coastline between Ibaraki Prefecture (north of Kashima) and Aomori Prefecture (south of Rokkasho). The inundation model results highlighted the variability of tsunami impact in response to different offshore bathymetry and flooded terrain. Comparison of tsunami sources inferred from different indirect methods shows the crucial importance of deep-ocean tsunami measurements for real-time tsunami forecasts. The agreement between model results and observations along Japan's coastline demonstrate the ability and potential of NOAA's methodology for real-time near-field tsunami flooding forecasts. An accurate tsunami flooding forecast within 30 min may now be possible using the NOAA forecast methodology with carefully placed tsunameters and large-scale high-resolution inundation
Time series modelling and forecasting of emergency department overcrowding.
Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Tahon, Christian
2014-09-01
Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.
Networking Sensor Observations, Forecast Models & Data Analysis Tools
Falke, S. R.; Roberts, G.; Sullivan, D.; Dibner, P. C.; Husar, R. B.
2009-12-01
This presentation explores the interaction between sensor webs and forecast models and data analysis processes within service oriented architectures (SOA). Earth observation data from surface monitors and satellite sensors and output from earth science models are increasingly available through open interfaces that adhere to web standards, such as the OGC Web Coverage Service (WCS), OGC Sensor Observation Service (SOS), OGC Web Processing Service (WPS), SOAP-Web Services Description Language (WSDL), or RESTful web services. We examine the implementation of these standards from the perspective of forecast models and analysis tools. Interoperable interfaces for model inputs, outputs, and settings are defined with the purpose of connecting them with data access services in service oriented frameworks. We review current best practices in modular modeling, such as OpenMI and ESMF/Mapl, and examine the applicability of those practices to service oriented sensor webs. In particular, we apply sensor-model-analysis interfaces within the context of wildfire smoke analysis and forecasting scenario used in the recent GEOSS Architecture Implementation Pilot. Fire locations derived from satellites and surface observations and reconciled through a US Forest Service SOAP web service are used to initialize a CALPUFF smoke forecast model. The results of the smoke forecast model are served through an OGC WCS interface that is accessed from an analysis tool that extract areas of high particulate matter concentrations and a data comparison tool that compares the forecasted smoke with Unattended Aerial System (UAS) collected imagery and satellite-derived aerosol indices. An OGC WPS that calculates population statistics based on polygon areas is used with the extract area of high particulate matter to derive information on the population expected to be impacted by smoke from the wildfires. We described the process for enabling the fire location, smoke forecast, smoke observation, and
A novel recurrent neural network forecasting model for power intelligence center
Institute of Scientific and Technical Information of China (English)
LIU Ji-cheng; NIU Dong-xiao
2008-01-01
In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic,fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power Intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.
A multivariate heuristic model for fuzzy time-series forecasting.
Huarng, Kun-Huang; Yu, Tiffany Hui-Kuang; Hsu, Yu Wei
2007-08-01
Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables.
Comparison of Conventional and ANN Models for River Flow Forecasting
Jain, A.; Ganti, R.
2011-12-01
Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.
Developing a model of forecasting information systems performance
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G. N. Isaev
2017-01-01
Full Text Available Research aim: to develop a model to forecast the performance ofinformation systems as a mechanism for preliminary assessment of the information system effectiveness before the beginning of financing the information system project.Materials and methods: the starting material used the results of studying the parameters of the statistical structure of information system data processing defects. Methods of cluster analysis and regression analysis were applied.Results: in order to reduce financial risks, information systems customers try to make decisions on the basis of preliminary calculations on the effectiveness of future information systems. However, the assumptions on techno-economic justification of the project can only be obtained when the funding for design work is already open. Its evaluation can be done before starting the project development using a model of forecasting information system performance. The model is developed using regression analysis in the form of a multiple linear regression. The value of information system performance is the predicted variable in the regression equation. The values of data processing defects in the classes of accuracy, completeness and timeliness are the forecast variables. Measurement and evaluation of parameters of the statistical structure of defects were done through programmes of cluster analysis and regression analysis. The calculations for determining the actual and forecast values of the information system performance were conducted.Conclusion: in terms of implementing the model, a research of information systems was carried out, as well as the development of forecasting model of information system performance. The conducted experimental work showed the adequacy of the model. The model is implemented in the complex task of designing information systems in education and industry.
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
Temperature sensitivity of a numerical pollen forecast model
Scheifinger, Helfried; Meran, Ingrid; Szabo, Barbara; Gallaun, Heinz; Natali, Stefano; Mantovani, Simone
2016-04-01
Allergic rhinitis has become a global health problem especially affecting children and adolescence. Timely and reliable warning before an increase of the atmospheric pollen concentration means a substantial support for physicians and allergy suffers. Recently developed numerical pollen forecast models have become means to support the pollen forecast service, which however still require refinement. One of the problem areas concerns the correct timing of the beginning and end of the flowering period of the species under consideration, which is identical with the period of possible pollen emission. Both are governed essentially by the temperature accumulated before the entry of flowering and during flowering. Phenological models are sensitive to a bias of the temperature. A mean bias of -1°C of the input temperature can shift the entry date of a phenological phase for about a week into the future. A bias of such an order of magnitude is still possible in case of numerical weather forecast models. If the assimilation of additional temperature information (e.g. ground measurements as well as satellite-retrieved air / surface temperature fields) is able to reduce such systematic temperature deviations, the precision of the timing of phenological entry dates might be enhanced. With a number of sensitivity experiments the effect of a possible temperature bias on the modelled phenology and the pollen concentration in the atmosphere is determined. The actual bias of the ECMWF IFS 2 m temperature will also be calculated and its effect on the numerical pollen forecast procedure presented.
Interval forecasts of a novelty hybrid model for wind speeds
Directory of Open Access Journals (Sweden)
Shanshan Qin
2015-11-01
Full Text Available The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values, which cannot properly handle uncertainties. For quantifying potential uncertainties, a hybrid model constructed by the Cuckoo Search Optimization (CSO-based Back Propagation Neural Network (BPNN is proposed to establish wind speed interval forecasts (IFs by estimating the lower and upper bounds. The quality of IFs is assessed quantitatively using IFs coverage probability (IFCP and IFs normalized average width (IFNAW. Moreover, to assess the overall quality of IFs comprehensively, a tradeoff between informativeness (IFNAW and validity (IFCP of IFs is examined by coverage width-based criteria (CWC. As an applicative study, wind speeds from the Xinjiang Region in China are used to validate the proposed hybrid model. The results demonstrate that the proposed model can construct higher quality IFs for short-term wind speed forecasts.
A systematic review of health manpower forecasting models.
Martins-Coelho, G.; Greuningen, M. van; Barros, H.; Batenburg, R.
2011-01-01
Context: Health manpower planning (HMP) aims at matching health manpower (HM) supply to the population’s health requirements. To achieve this, HMP needs information on future HM supply and requirement (S&R). This is estimated by several different forecasting models (FMs). In this paper, we review
Development of a forecast model for global air traffic emissions
Energy Technology Data Exchange (ETDEWEB)
Schaefer, Martin
2012-07-01
The thesis describes the methodology and results of a simulation model that quantifies fuel consumption and emissions of civil air traffic. Besides covering historical emissions, the model aims at forecasting emissions in the medium-term future. For this purpose, simulation models of aircraft and engine types are used in combination with a database of global flight movements and assumptions about traffic growth, fleet rollover and operational aspects. Results from an application of the model include emissions of scheduled air traffic for the years 2000 to 2010 as well as forecasted emissions until the year 2030. In a baseline scenario of the forecast, input assumptions (e.g. traffic growth rates) are in line with predictions by the aircraft industry. Considering the effects of advanced technologies of the short-term and medium-term future, the forecast focusses on fuel consumption and emissions of nitric oxides. Calculations for historical air traffic additionally cover emissions of carbon monoxide, unburned hydrocarbons and soot. Results are validated against reference data including studies by the International Civil Aviation Organization (ICAO) and simulation results from international research projects. (orig.)
101 Modelling and Forecasting Periodic Electric Load for a ...
African Journals Online (AJOL)
User
2012-01-24
Jan 24, 2012 ... In this work, three models are used to analyze the electric load capacity of a ..... Forecasting electricity prices for a day-ahead pool-based electric energy market. ... Control, Operation and Management, Hong Kong pgs.782–7.
Performance assessment of models to forecast induced seismicity
Wiemer, Stefan; Karvounis, Dimitrios; Zechar, Jeremy; Király, Eszter; Kraft, Toni; Pio Rinaldi, Antonio; Catalli, Flaminia; Mignan, Arnaud
2015-04-01
Managing and mitigating induced seismicity during reservoir stimulation and operation is a critical prerequisite for many GeoEnergy applications. We are currently developing and validating so called 'Adaptive Traffic Light Systems' (ATLS), fully probabilistic forecast models that integrate all relevant data on the fly into a time-dependent hazard and risk model. The combined model intrinsically considers both aleatory and model-uncertainties, the robustness of the forecast is maximized by using a dynamically update ensemble weighting. At the heart of the ATLS approach are a variety of forecast models that range from purely statistical models, such as flow-controlled Epidemic Type Aftershock Sequence (ETAS) models, to models that consider various physical interaction mechanism (e.g., pore pressure changes, dynamic and static stress transfer, volumetric strain changes). The automated re-calibration of these models on the fly given data imperfection, degrees of freedom, and time-constraints is a sizable challenge, as is the validation of the models for applications outside of their calibrated range (different settings, larger magnitudes, changes in physical processes etc.). Here we present an overview of the status of the model development, calibration and validation. We also demonstrate how such systems can contribute to a quantitative risk assessment and mitigation of induced seismicity in a wide range of applications and time scales.
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.
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
An improved market penetration model for wind energy technology forecasting
Energy Technology Data Exchange (ETDEWEB)
Lund, P.D. [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems
1995-12-31
An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)
Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting
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Tansu Filik
2016-03-01
Full Text Available In this paper, the spatio-temporal (multi-channel linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and revised. It is shown in detail how to apply these MARMA models to the spatially distributed wind speed measurements. The proposed MARMA models are tested using real wind speed measurements which are collected from the five stations around Canakkale region of Turkey. According to the test results, considerable improvements are observed over the well known persistence, autoregressive (AR and multi-channel/vector autoregressive (VAR models. It is also shown that the model can predict wind speed very fast (in milliseconds which is suitable for the immediate short-term forecasting.
Forecasting seasonal influenza with a state-space SIR model.
Osthus, Dave; Hickmann, Kyle S; Caragea, Petruţa C; Higdon, Dave; Del Valle, Sara Y
2017-03-01
Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention's influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics.
Beckett, F. M.; Witham, C. S.; Hort, M. C.; Stevenson, J. A.; Bonadonna, C.; Millington, S. C.
2015-11-01
This study examines the sensitivity of atmospheric dispersion model forecasts of volcanic ash clouds to the physical characteristics assigned to the particles. We show that the particle size distribution (PSD) used to initialise a dispersion model has a significant impact on the forecast of the mass loading of the ash particles in the atmosphere. This is because the modeled fall velocity of the particles is sensitive to the particle diameter. Forecasts of the long-range transport of the ash cloud consider particles with diameters between 0.1 μm and 100 μm. The fall velocity of particles with diameter 100 μm is over 5 orders of magnitude greater than a particle with diameter 0.1 μm, and 30 μm particles fall 88% slower and travel up to 5× further than a 100 μm particle. Identifying the PSD of the ash cloud at the source, which is required to initialise a model, is difficult. Further, aggregation processes are currently not explicitly modeled in operational dispersion models due to the high computational costs associated with aggregation schemes. We show that using a modified total grain size distribution (TGSD) that effectively accounts for aggregation processes improves the modeled PSD of the ash cloud and deposits from the eruption of Eyjafjallajökull in 2010. Knowledge of the TGSD of an eruption is therefore critical for reducing uncertainty in quantitative forecasts of ash cloud dispersion. The density and shape assigned to the model particles have a lesser but still significant impact on the calculated fall velocity. Accounting for the density distribution and sphericity of ash from the eruption of Eyjafjallajökull in 2010, modeled particles can travel up to 84% further than particles with default particle characteristics that assume the particles are spherical and have a fixed density.
Forecasting Financial Time-Series using Artificial Market Models
Gupta, N; Johnson, N F; Gupta, Nachi; Hauser, Raphael; Johnson, Neil F.
2005-01-01
We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself -- was discussed by Johnson et al. in cond-mat/0105303 and cond-mat/0105258 and was based on some encouraging preliminary investigations of the dollar-yen exchange rate, various individual stocks, and stock market indices. However, the initial attempts lacked a clear formal methodology. Here we present a detailed methodology, using optimization techniques to build an estimate of the strategy distribution across the multi-trader population. In contrast to earlier attempts, we are able to present a systematic method for identifying 'pockets of predictability' in real-world markets. We find that as each pocket closes up, the black-box system needs to be 'reset' - which is equivalent to sayi...
Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.
2012-04-01
dynamic and processes, i. e. sample heterogeneity. For a same streamflow range corresponds different processes such as rising limbs or recession, where uncertainties are different. The dynamical approach improves reliability, skills and sharpness of forecasts and globally reduces confidence intervals width. When compared in details, the dynamical approach allows a noticeable reduction of confidence intervals during recessions where uncertainty is relatively lower and a slight increase of confidence intervals during rising limbs or snowmelt where uncertainty is greater. The dynamic approach, validated by forecaster's experience that considered the empirical approach not discriminative enough, improved forecaster's confidence and communication of uncertainties. Montanari, A. and Brath, A., (2004). A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resources Research, 40, W01106, doi:10.1029/2003WR002540. Schaefli, B., Balin Talamba, D. and Musy, A., (2007). Quantifying hydrological modeling errors through a mixture of normal distributions. Journal of Hydrology, 332, 303-315.
Evaluating non-linear models on point and interval forecasts: an application with exchange rates
Directory of Open Access Journals (Sweden)
Emanuela Marrocu
2005-01-01
Full Text Available The aim of this paper is to compare the forecasting performance of SETAR and GARCH models against a linear benchmark using historical data for the returns of the Japanese yen/US dollar exchange rate. The relative performance of the models is evaluated on point forecasts and on interval forecasts. Point forecasts evaluation over the whole forecast period indicates that the performance of the models, when distinguishable, tends to favour the linear models. However, we show that if the evaluation of point forecasts is conducted over distinct subsamples or specific regimes there is more evidence of forecasting gains, especially from the SETAR models. Moreover, when we evaluate the validity of interval forecasts, the results produce clear evidence of the superiority of the non-linear models, and tend to favour especially the GARCH models.
Assessment of Quantitative Precipitation Forecasts from Operational NWP Models (Invited)
Sapiano, M. R.
2010-12-01
Previous work has shown that satellite and numerical model estimates of precipitation have complimentary strengths, with satellites having greater skill at detecting convective precipitation events and model estimates having greater skill at detecting stratiform precipitation. This is due in part to the challenges associated with retrieving stratiform precipitation from satellites and the difficulty in resolving sub-grid scale processes in models. These complimentary strengths can be exploited to obtain new merged satellite/model datasets, and several such datasets have been constructed using reanalysis data. Whilst reanalysis data are stable in a climate sense, they also have relatively coarse resolution compared to the satellite estimates (many of which are now commonly available at quarter degree resolution) and they necessarily use fixed forecast systems that are not state-of-the-art. An alternative to reanalysis data is to use Operational Numerical Weather Prediction (NWP) model estimates, which routinely produce precipitation with higher resolution and using the most modern techniques. Such estimates have not been combined with satellite precipitation and their relative skill has not been sufficiently assessed beyond model validation. The aim of this work is to assess the information content of the models relative to satellite estimates with the goal of improving techniques for merging these data types. To that end, several operational NWP precipitation forecasts have been compared to satellite and in situ data and their relative skill in forecasting precipitation has been assessed. In particular, the relationship between precipitation forecast skill and other model variables will be explored to see if these other model variables can be used to estimate the skill of the model at a particular time. Such relationships would be provide a basis for determining weights and errors of any merged products.
Real-Time Flood Forecasting System Using Channel Flow Routing Model with Updating by Particle Filter
Kudo, R.; Chikamori, H.; Nagai, A.
2008-12-01
A real-time flood forecasting system using channel flow routing model was developed for runoff forecasting at water gauged and ungaged points along river channels. The system is based on a flood runoff model composed of upstream part models, tributary part models and downstream part models. The upstream part models and tributary part models are lumped rainfall-runoff models, and the downstream part models consist of a lumped rainfall-runoff model for hillslopes adjacent to a river channel and a kinematic flow routing model for a river channel. The flow forecast of this model is updated by Particle filtering of the downstream part model as well as by the extended Kalman filtering of the upstream part model and the tributary part models. The Particle filtering is a simple and powerful updating algorithm for non-linear and non-gaussian system, so that it can be easily applied to the downstream part model without complicated linearization. The presented flood runoff model has an advantage in simlecity of updating procedure to the grid-based distributed models, which is because of less number of state variables. This system was applied to the Gono-kawa River Basin in Japan, and flood forecasting accuracy of the system with both Particle filtering and extended Kalman filtering and that of the system with only extended Kalman filtering were compared. In this study, water gauging stations in the objective basin were divided into two types of stations, that is, reference stations and verification stations. Reference stations ware regarded as ordinary water gauging stations and observed data at these stations are used for calibration and updating of the model. Verification stations ware considered as ungaged or arbitrary points and observed data at these stations are used not for calibration nor updating but for only evaluation of forecasting accuracy. The result confirms that Particle filtering of the downstream part model improves forecasting accuracy of runoff at
Validating induced seismicity forecast models - Induced Seismicity Test Bench
Kiraly-Proag, Eszter; Gischig, Valentin; Wiemer, Stefan; Karvounis, Dimitrios; Doetsch, Joseph
2016-01-01
Induced earthquakes often accompany fluid injection, and the seismic hazard they pose threatens various underground engineering projects. Models to monitor and control induced seismic hazard with traffic light systems should be probabilistic, forward-looking, and updated as new data arrive. In this study, we propose an Induced Seismicity Test Bench to test and rank such models; this test bench can be used for model development, model selection, and ensemble model building. We apply the test bench to data from the Basel 2006 and Soultz-sous-For\\^ets 2004 geothermal stimulation projects, and we assess forecasts from two models: Shapiro and Smoothed Seismicity (SaSS) and Hydraulics and Seismics (HySei). These models incorporate a different mix of physics-based elements and stochastic representation of the induced sequences. Our results show that neither model is fully superior to the other. Generally, HySei forecasts the seismicity rate better after shut-in, but is only mediocre at forecasting the spatial distri...
Review of Wind Energy Forecasting Methods for Modeling Ramping Events
Energy Technology Data Exchange (ETDEWEB)
Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R
2011-03-28
Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.
Forecasting wind-driven wildfires using an inverse modelling approach
Directory of Open Access Journals (Sweden)
O. Rios
2014-06-01
Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.
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
Prive, Nikki C.; Errico, Ronald M.
2013-01-01
A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.
Slater, Louise; Villarini, Gabriele
2017-04-01
There are two main approaches to long-range (monthly to seasonal) streamflow forecasting: statistical approaches that typically relate climate precursors directly to streamflow, and dynamical physically-based approaches in which spatially distributed models are forced with downscaled meteorological forecasts. While the former approach is potentially limited by a lack of physical causality, the latter tends to be complex and time-consuming to implement. In contrast, hybrid statistical-dynamical techniques that use global climate model (GCM) ensemble forecasts as inputs to statistical models are both physically-based and rapid to run, but are a relatively new field of research. Here, we conduct the first systematic multimodel statistical-dynamical forecasting of streamflow using NMME climate forecasts from eight GCMs (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) across a broad region. At several hundred U.S. Midwest stream gauges with long (50+ continuous years) streamflow records, we fit probabilistic statistical models for seasonal streamflow percentiles ranging from minimum to maximum flows. As predictors, we use basin-averaged values of precipitation, antecedent wetness, temperature, agricultural row crop acreage, and population density. Using the observed data, we select the best-fitting probabilistic model for every site, season, and streamflow percentile (ranging from low to high flows). The best-fitting models are then used to obtain streamflow predictions by incorporating the NMME climate forecasts and the extrapolated agricultural and population time series as predictors. The forecasting skill of our models is assessed using both deterministic and probabilistic verification measures. The influence of the different predictors is evaluated for all streamflow percentiles and across the full range of lead times. Our findings reveal that statistical-dynamical streamflow forecasting produces promising results, which may enable water managers
Comparative analysis for traffic flow forecasting models with real-life data in Beijing
Directory of Open Access Journals (Sweden)
Yaping Rong
2015-12-01
Full Text Available Rational traffic flow forecasting is essential to the development of advanced intelligent transportation systems. Most existing research focuses on methodologies to improve prediction accuracy. However, applications of different forecast models have not been adequately studied yet. This research compares the performance of three representative prediction models with real-life data in Beijing. They are autoregressive integrated moving average, neutral network, and nonparametric regression. The results suggest that nonparametric regression significantly outperforms the other models. With Wilcoxon signed-rank test, the root mean square errors and the error distribution reveal that the nonparametric regression model experiences superior accuracy. In addition, the nonparametric regression model exhibits the best spatial-transferred application effect.
CSIR Research Space (South Africa)
Landman, WA
2012-11-01
Full Text Available -forecasts) have been generated by a statistical model and by state-of-the-art fully coupled ocean-atmosphere general circulation models. Since forecast users generally require well-calibrated probability forecasts we employ a model output statistics approach...
New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.
Song, Qiang; Chissom, Brad S.
Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…
A Novel Fuzzy Document Based Information Retrieval Model for Forecasting
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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.
Forecasting solar irradiation using WRF model and refining statistics for Northeastern Brazil
Pereira, E. B.; Lima, F. J. L.; Martins, F. R.
2015-12-01
Solar energy is referred to as variable generation sources because their electricity production varies based on the availability of sun irradiance. To accommodate this variability, electricity grid operators use a variety of tools to maintain a reliable electricity supply, one of them is to forecast solar irradiation, and to adjust other electricity sources as needed. This work reports an approach to forecast solar irradiation in the Brazilian Northeastern region (NEB) by using statistically post-processing data from mesoscale model outputs. The method assimilates the diversity of climate characteristics occurring in the region presenting the largest solar energy potentials in Brazil. Untreated solar irradiance forecasts for 24h in advance were obtained using the WRF model runs. Cluster analysis technique was employed to find out areas presenting similar climate characteristics and to reduce uncertainties. Comparison analysis between WRF model outputs and site-specific measured data were performed to evaluate the model skill in forecasting the surface solar irradiation. After that, post-processing of WRF outputs using artificial neural networks (ANNs) and multiple regression methods refined the short-term solar irradiation forecasts. A set of pre-selected variables of the WRF model outputs representing the forecasted atmospheric conditions were used as predictors by the ANNs. Several predictors were tested in the adjustment and simulation of the ANNs. We found the best ANNs architecture and a group of 10 predictors, with which more in-depth analyzes were carried out, including performance evaluation for fall and spring of 2011 (rainy and dry season in NEB). The site-specific measured solar radiation data came from 110 stations distributed throughout the NEB. Data for the rainy season were acquired from March to May, and for the dry season from September to November. We concluded that the untreated numerical forecasts of solar irradiation provided by WRF exhibited a
Application of Improved Grey Prediction Model to Petroleum Cost Forecasting
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
The grey theory is a multidisciplinary and generic theory that deals with systems that lack adequate information and/or have only poor information. In this paper, an improved grey model using step function was proposed.Petroleum cost forecast of the Henan oil field was used as the case study to test the efficiency and accuracy of the proposed method. According to the experimental results, the proposed method obviously could improve the prediction accuracy of the original grey model.
Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations
Wanders, Niko; Wood, Eric F.
2016-09-01
Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts for the period 1982-2012. Two weighted multi-model ensemble means from the models have been developed for predictions of both sub-seasonal precipitation and temperature. By combining models through optimal weights, the multi-model forecast skill is significantly improved compared to a ‘standard’ equally weighted multi-model forecast mean. We show that optimal model weights are robust and the forecast skill is maintained for increased length of time and regions with a low initial forecast skill show significant skill after optimal weighting of the individual model forecast. The sub-seasonal model forecasts models show high skill over the tropics, approximating their skill at monthly resolution. Using the weighted approach, a significant increase is found in the forecast skill for dry, wet, cold and warm extreme events. The weighted mean approach brings significant advances to sub-seasonal forecasting due to its reduced uncertainty in the forecasts with a gain in forecast skill. This significantly improves their value for end-user applications and our ability to use them to prepare for upcoming extreme conditions, like floods and droughts.
Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
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Ganjar Alfian
2017-06-01
Full Text Available A carsharing service can be seen as a transport alternative between private and public transport that enables a group of people to share vehicles based at certain stations. The advanced carsharing service, one-way carsharing, enables customers to return the car to another station. However, one-way implementation generates an imbalanced distribution of cars in each station. Thus, this paper proposes forecasting relocation to solve car distribution imbalances for one-way carsharing services. A discrete event simulation model was developed to help evaluate the proposed model performance. A real case dataset was used to find the best simulation result. The results provide a clear insight into the impact of forecasting relocation on high system utilization and the reservation acceptance ratio compared to traditional relocation methods.
Directory of Open Access Journals (Sweden)
K. J. Franz
2011-11-01
Full Text Available The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE, and the Shuffle Complex Evolution Metropolis (SCEM. Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
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
A Novel Algorithm of Forecasting the Potential Development of Generation in the Distribution Grid
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Michał Bajor
2014-06-01
Full Text Available The paper presents a novel method of forecasting the potential for the development of various types of generation, including renewable, connecting to the distribution grid. The proposed algorithm is based on the idea of identifying different factors influencing the possibility of developing various types of generation in different time horizons. Descriptions of subsequent stages of the forecasting procedure, used terms and the software implementing the algorithm, developed by the authors, are also included in the paper. Finally, comments regarding the reliability of the results obtained using the method are described.
Very short-term probabilistic forecasting of wind power with generalized logit-Normal distributions
DEFF Research Database (Denmark)
Pinson, Pierre
2012-01-01
Very-short-term probabilistic forecasts, which are essential for an optimal management of wind generation, ought to account for the non-linear and double-bounded nature of that stochastic process. They take here the form of discrete–continuous mixtures of generalized logit–normal distributions...... classical assumptions about the shape of predictive densities, e.g. normal and beta, is demonstrated on the basis of 10-min-ahead point and probabilistic forecasting at the Horns Rev wind farm in Denmark....
Developing Snow Model Forcing Data From WRF Model Output to Aid in Water Resource Forecasting
Havens, S.; Marks, D. G.; Watson, K. A.; Masarik, M.; Flores, A. N.; Kormos, P.; Hedrick, A. R.
2015-12-01
Traditional operational modeling tools used by water managers in the west are challenged by more frequently occurring uncharacteristic stream flow patterns caused by climate change. Water managers are now turning to new models based on the physical processes within a watershed to combat the increasing number of events that do not follow the historical patterns. The USDA-ARS has provided near real time snow water equivalent (SWE) maps using iSnobal since WY2012 for the Boise River Basin in southwest Idaho and since WY2013 for the Tuolumne Basin in California that feeds the Hetch Hetchy reservoir. The goal of these projects is to not only provide current snowpack estimates but to use the Weather Research and Forecasting (WRF) model to drive iSnobal in order to produce a forecasted stream flow when coupled to a hydrology model. The first step is to develop methods on how to create snow model forcing data from WRF outputs. Using a reanalysis 1km WRF dataset from WY2009 over the Boise River Basin, WRF model results like surface air temperature, relative humidity, wind, precipitation, cloud cover, and incoming long wave radiation must be downscaled for use in iSnobal. iSnobal results forced with WRF output are validated at point locations throughout the basin, as well as compared with iSnobal results forced with traditional weather station data. The presentation will explore the differences in forcing data derived from WRF outputs and weather stations and how this affects the snowpack distribution.
Moore, Robert J.; Wells, Steven C.; Cole, Steven J.
2016-04-01
It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area
Gray comprehensive assessment and optimal selection of water consumption forecasting model
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A comprehensive assessing method based on the principle of the gray system theory and gray relational grade analysis was put forward to optimize water consumption forecasting models. The method provides a better accuracy for the assessment and the optimal selection of the water consumption forecasting models. The results show that the forecasting model built on this comprehensive assessing method presents better self-adaptability and accuracy in forecasting.
Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model
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Xia Li
2014-01-01
Full Text Available Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models.
Forecasting TRY/USD Exchange Rate with Various Artificial Neural Network Models
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Cagatay Bal
2017-02-01
Full Text Available Exchange rate forecasting is one of the most common subjects among the forecasting problem field. Researchers and academicians from many different disciplines proposed various approaches for better exchange rate forecasting. In recent years, for solving the stated forecasting problem artificial neural networks have become successful tool to obtain solutions. Many different artificial neural networks have been used, developed and still developing for even better and trustable forecasts. In this study, TRY/USD exchange rate forecasting is modeled with different learning algorithms, activations functions and performance measures. Various Artificial Neural Network (ANN models for better forecasting were investigated, compared and the obtained forecasting results interpreted respectively. The results of the application show that Variable Learning Rate Backpropagation learning algorithm with tan-sigmoid activation function has the best performance for TRY/USD exchange rate forecasting.
Zhao, Tongtiegang; Schepen, Andrew; Wang, Q. J.
2016-10-01
The Bayesian joint probability (BJP) modelling approach is used operationally to produce seasonal (three-month-total) ensemble streamflow forecasts in Australia. However, water resource managers are calling for more informative sub-seasonal forecasts. Taking advantage of BJP's capability of handling multiple predictands, ensemble forecasting of sub-seasonal to seasonal streamflows is investigated for 23 catchments around Australia. Using antecedent streamflow and climate indices as predictors, monthly forecasts are developed for the three-month period ahead. Forecast reliability and skill are evaluated for the period 1982-2011 using a rigorous leave-five-years-out cross validation strategy. BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread. Forecast skill, relative to climatology, is positive in 74% of cases in the first month, decreasing to 57% and 46% respectively for streamflow forecasts for the final two months of the season. As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology. Seasonal forecasts accumulated from monthly forecasts are found to be similarly skilful to forecasts from BJP models based on seasonal totals directly. The BJP modelling approach is demonstrated to be a viable option for producing ensemble time-series sub-seasonal to seasonal streamflow forecasts.
Forecasting with the Fokker-Planck model: Bayesian setting of parameter
Montagnon, Chris
2017-04-01
Using a closed solution to a Fokker-Planck model of a time series, a probability distribution for the next point in the time series is developed. This probability distribution has one free parameter. Various Bayesian approaches to setting this parameter are tested by forecasting some real world time series. Results show a more than 25 % reduction in the ' 95 % point' of the probability distribution (the safety stock required in these real world situations), versus the conventional ARMA approach, without a significant increase in actuals exceeding this level.
Modelling and forecasting monthly swordfish catches in the Eastern Mediterranean
Directory of Open Access Journals (Sweden)
Konstantinos I. Stergiou
2003-04-01
Full Text Available In this study, we used the X-11 census technique for modelling and forecasting the monthly swordfish (Xiphias gladius catches in the Greek Seas during 1982-1996 and 1997 respectively, using catches reported by the National Statistical Service of Greece (NSSG. Forecasts built with X-11 were also compared with those derived from ARIMA andWinter’s exponential smoothing (WES models. The X-11 method captured the features of the study series and outperformed the other two methods, in terms of both fitting and forecasting performance, for all the accuracy measures used. Thus, with the exception of October, November and December 1997, when the corresponding absolute percentage error(APE values were very high (as high as 178.6% because of the low level of the catches, monthly catches during the remaining months of 1997 were predicted accurately, with a mean APE of 12.5%. In contrast, the mean APE values of the other two methods for the same months were higher (ARIMA: 14.6%; WES: 16.6%. The overall good performance of X-11 andthe fact that it provides an insight into the various components (i.e. the seasonal, trend-cycle and irregular components of the time series of interest justify its use in fisheries research. The basic features of the swordfish catches revealed by the application of the X-11 method, the effect of the length of the forecasting horizon on forecasting accuracy and the accuracy of the catches reported by NSSG are also discussed.
Crop Yield Forecasted Model Based on Time Series Techniques
Institute of Scientific and Technical Information of China (English)
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.
Developing energy forecasting model using hybrid artificial intelligence method
Institute of Scientific and Technical Information of China (English)
Shahram Mollaiy-Berneti
2015-01-01
An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Teräsvirta, Timo
In this paper we consider the forecasting performance of a well-defined class of flexible models, the so-called single hidden-layer feedforward neural network models. A major aim of our study is to find out whether they, due to their flexibility, are as useful tools in economic forecasting as some...... previous studies have indicated. When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. In fact, their parameters are not even globally...... on the linearisation idea: the Marginal Bridge Estimator and Autometrics. Second, one must decide whether forecasting should be carried out recursively or directly. Comparisons of these two methodss exist for linear models and here these comparisons are extended to neural networks. Finally, a nonlinear model...
Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling
Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè
2017-04-01
Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.
Scaling forecast models for wind turbulence and wind turbine power intermittency
Duran Medina, Olmo; Schmitt, Francois G.; Calif, Rudy
2017-04-01
The intermittency of the wind turbine power remains an important issue for the massive development of this renewable energy. The energy peaks injected in the electric grid produce difficulties in the energy distribution management. Hence, a correct forecast of the wind power in the short and middle term is needed due to the high unpredictability of the intermittency phenomenon. We consider a statistical approach through the analysis and characterization of stochastic fluctuations. The theoretical framework is the multifractal modelisation of wind velocity fluctuations. Here, we consider three wind turbine data where two possess a direct drive technology. Those turbines are producing energy in real exploitation conditions and allow to test our forecast models of power production at a different time horizons. Two forecast models were developed based on two physical principles observed in the wind and the power time series: the scaling properties on the one hand and the intermittency in the wind power increments on the other. The first tool is related to the intermittency through a multifractal lognormal fit of the power fluctuations. The second tool is based on an analogy of the power scaling properties with a fractional brownian motion. Indeed, an inner long-term memory is found in both time series. Both models show encouraging results since a correct tendency of the signal is respected over different time scales. Those tools are first steps to a search of efficient forecasting approaches for grid adaptation facing the wind energy fluctuations.
Tarasov, D. A.; Buevich, A. G.; Sergeev, A. P.; Shichkin, A. V.; Baglaeva, E. M.
2017-06-01
Forecasting the soil pollution is a considerable field of study in the light of the general concern of environmental protection issues. Due to the variation of content and spatial heterogeneity of pollutants distribution at urban areas, the conventional spatial interpolation models implemented in many GIS packages mostly cannot provide appreciate interpolation accuracy. Moreover, the problem of prediction the distribution of the element with high variability in the concentration at the study site is particularly difficult. The work presents two neural networks models forecasting a spatial content of the abnormally distributed soil pollutant (Cr) at a particular location of the subarctic Novy Urengoy, Russia. A method of generalized regression neural network (GRNN) was compared to a common multilayer perceptron (MLP) model. The proposed techniques have been built, implemented and tested using ArcGIS and MATLAB. To verify the models performances, 150 scattered input data points (pollutant concentrations) have been selected from 8.5 km2 area and then split into independent training data set (105 points) and validation data set (45 points). The training data set was generated for the interpolation using ordinary kriging while the validation data set was used to test their accuracies. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. The predictive accuracy of both models was confirmed to be significantly higher than those achieved by the geostatistical approach (kriging). It is shown that MLP could achieve better accuracy than both kriging and even GRNN for interpolating surfaces.
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Maui-Oahu
National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Hawaiian islands of Oahu,...
On Comparing NWP and Radar Nowcast Models for Forecasting of Urban Runoff
DEFF Research Database (Denmark)
Thorndahl, Søren Liedtke; Bøvith, T.; Rasmussen, Michael R.;
2012-01-01
The paper compares quantitative precipitation forecasts using weather radars and numerical weather prediction models. In order to test forecasts under different conditions, point-comparisons with quantitative radar precipitation estimates and raingauges are presented. Furthermore, spatial...
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Main Hawaiian Islands
National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Main Hawaiian Islands (MHI)...
CSIR Research Space (South Africa)
Landman, WA
2011-11-01
Full Text Available The various institutions involved with seasonal forecast development and production are discussed. New modelling approaches and the establishment of infrastructures to improve forecast dissemination are discussed....
Forecasting models for national economic planning
Heesterman, A R G
1972-01-01
This book is about the specification of linear econometric models, and for this reason some important related fields have been deliberately omitted. I did not want to discuss the problems of parameter-estimation, at least not in any detail, as there are other books on these problems written by specialized statisticians. This book is about the models them selves and macro-economic models in particular. A second related sub ject is the policy decision that can be made with the help of a model. While I did write a chapter on policy decisions, I limited myself to some extent because of my views on planning as such. The logical approach to this problem is in terms of mathematical programming, but our models and our ideas about the policies we want are too crude for its effective utilisation. A realistic formulation of the problem should involve non linearities in an essential way, the models I consider (and most existing models) are linear. At the present state of econometrics, I do not really believe in suc...
A forecast comparison of volatility models
DEFF Research Database (Denmark)
Hansen, Peter Reinhard; Lunde, Asger
2005-01-01
We compare 330 ARCH-type models in terms of their ability to describe the conditional variance. The models are compared out-of-sample using DM-$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outpe......We compare 330 ARCH-type models in terms of their ability to describe the conditional variance. The models are compared out-of-sample using DM-$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1...
An Efficient and Simplified Model for Forecasting using SRM
Directory of Open Access Journals (Sweden)
Hafiz Muhammad Shahzad Asif
2014-01-01
Full Text Available Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines, provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory, SRM (Structural Risk Minimization Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building
Parallelism and optimization of numerical ocean forecasting model
Xu, Jianliang; Pang, Renbo; Teng, Junhua; Liang, Hongtao; Yang, Dandan
2016-10-01
According to the characteristics of Chinese marginal seas, the Marginal Sea Model of China (MSMC) has been developed independently in China. Because the model requires long simulation time, as a routine forecasting model, the parallelism of MSMC becomes necessary to be introduced to improve the performance of it. However, some methods used in MSMC, such as Successive Over Relaxation (SOR) algorithm, are not suitable for parallelism. In this paper, methods are developedto solve the parallel problem of the SOR algorithm following the steps as below. First, based on a 3D computing grid system, an automatic data partition method is implemented to dynamically divide the computing grid according to computing resources. Next, based on the characteristics of the numerical forecasting model, a parallel method is designed to solve the parallel problem of the SOR algorithm. Lastly, a communication optimization method is provided to avoid the cost of communication. In the communication optimization method, the non-blocking communication of Message Passing Interface (MPI) is used to implement the parallelism of MSMC with complex physical equations, and the process of communication is overlapped with the computations for improving the performance of parallel MSMC. The experiments show that the parallel MSMC runs 97.2 times faster than the serial MSMC, and root mean square error between the parallel MSMC and the serial MSMC is less than 0.01 for a 30-day simulation (172800 time steps), which meets the requirements of timeliness and accuracy for numerical ocean forecasting products.
Strategy-Based Forecasting Model for Civil Airlines
Institute of Scientific and Technical Information of China (English)
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.
Distributed generation systems model
Energy Technology Data Exchange (ETDEWEB)
Barklund, C.R.
1994-12-31
A slide presentation is given on a distributed generation systems model developed at the Idaho National Engineering Laboratory, and its application to a situation within the Idaho Power Company`s service territory. The objectives of the work were to develop a screening model for distributed generation alternatives, to develop a better understanding of distributed generation as a utility resource, and to further INEL`s understanding of utility concerns in implementing technological change.
MODELLING CHALLENGES TO FORECAST URBAN GOODS DEMAND FOR RAIL
Directory of Open Access Journals (Sweden)
Antonio COMI
2015-12-01
Full Text Available This paper explores the new research challenges for forecasting urban goods demand by rail. In fact, the growing interest to find urban logistics solutions for improving city sustainability and liveability, mainly due to the reduction of urban road accessibility and environmental constraints, has pushed to explore solutions alternative to the road. Multimodal urban logistics, based on the use of railway, seem an interesting alternative solution, but it remained mainly at conceptual level. Few studies have explored the factors, that push actors to find competitive such a system with respect to the road, and modelling framework for forecasting the relative demand. Therefore, paper reviews the current literature, investigates the factors involved in choosing such a mode, and finally, recalls a recent modelling framework and hence proposes some advancements that allow to point out the rail transport alternative.
Development and evaluation of novel forecasting adaptive ensemble model
Directory of Open Access Journals (Sweden)
C.M. Anish
2016-09-01
Full Text Available This paper proposes a new ensemble based adaptive forecasting structure for efficient different interval days' ahead prediction of five different asset values (NAV. In this approach three individual adaptive structures such as adaptive moving average (AMA, adaptive auto regressive moving average (AARMA and feedback radial basis function network (FRBF are employed to first train with conventional LMS, conventional forward-backward LMS and corresponding learning algorithm of FRBF respectively. After successful validation of each model the output obtained by each individual model is optimally weighted using Genetic algorithm (GA as well as particle swarm optimization (PSO based techniques to produce the best possible different days ahead prediction accuracy. Finally the results of prediction obtained of the NAV values are compared with the results obtained by individual predictors as well as by other four existing ensemble schemes. It is in general demonstrated that in all cases the proposed forecasting scheme outperforms other competitive methods.
Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models
Energy Technology Data Exchange (ETDEWEB)
Tan, Zhongfu; Zhang, Jinliang; Xu, Jun [North China Electric Power University, Beijing 102206 (China); Wang, Jianhui [Argonne National Laboratory, Argonne, IL 60439 (United States)
2010-11-15
This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. (author)
A Model for Forecasting Enlisted Student IA Billet Requirements
2016-03-01
were promised and had at least one course failure . Training times Student execution depends on TTT. TTT includes under-instruction (UI) time and...Cleared for Public Release A Model for Forecasting Enlisted Student IA Billet Requirements Steven W. Belcher with David L. Reese...and Kletus S. Lawler March 2016 Copyright © 2016 CNA This document contains the best opinion of CNA at the time of issue. It does
Dynamic ANN Modeling for Flood Forecasting in a River Network
Roy, Parthajit; Choudhury, P. S.; Saharia, Manabendra
2010-10-01
An experiment on predicting flood flows at each of the upstream and a down stream section of a river network is presented using focused Time Lagged Recurrent Neural Network with three different memories like TDNN memory, Gamma memory and Laguarre memory. This paper focuses on application of memory to the input layer of a TLRN in developing flood forecasting models for multiple sections in a river system. The study shows the Gamma memory has better applicability followed by TDNN and Laguarre memory.
Fractional Differencing Modeling and Forecasting of Eurocurrency Deposit Rates
John Barkoulas; Baum, Christopher F
1996-01-01
We investigate the low frequency properties of three- and six- month rates for Eurocurrency deposits denominated in eight major currencies with specific emphasis on fractional dynamics. Using the fractional integration testing procedure suggested by Geweke and Porter-Hudak (1983), we find that several of the Eurocurrency deposit rates are fractionally integrated processes with long memory. These findings have important implications for econometric modeling, forecasting, and cointegration test...
A High Resolution Forecast Model of Storm Surge Inundation
Institute of Scientific and Technical Information of China (English)
LIU Juan; JIANG Wensheng; SUN Wenxin; WANG Yongzhi
2005-01-01
In order to forecast storm surge inundation, a two-dimensional model is established. In the model, an alternating computation sequence method is used to solve the governing equations, and the dry and wet method is introduced to treat the moving boundary. This model is easy to use. It has a friendly input interface and Arcview GIS is used as the output interface. The model is applied to the Shantou area to simulate the storm surge elevations and inundations caused by Typhoons 6903 ane 0104 using the same relevant parameters. The calculated results agree well with the observations.
Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application
Institute of Scientific and Technical Information of China (English)
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.
Models for forecasting the flowering of Cornicabra olive groves
Rojo, Jesús; Pérez-Badia, Rosa
2015-11-01
This study examined the impact of weather-related variables on flowering phenology in the Cornicabra olive tree and constructed models based on linear and Poisson regression to forecast the onset and length of the pre-flowering and flowering phenophases. Spain is the world's leading olive oil producer, and the Cornicabra variety is the second largest Spanish variety in terms of surface area. However, there has been little phenological research into this variety. Phenological observations were made over a 5-year period (2009-2013) at four sampling sites in the province of Toledo (central Spain). Results showed that the onset of the pre-flowering phase is governed largely by temperature, which displayed a positive correlation with the temperature in the start of dormancy (November) and a negative correlation during the months prior to budburst (January, February and March). A similar relationship was recorded for the onset of flowering. Other weather-related variables, including solar radiation and rainfall, also influenced the succession of olive flowering phenophases. Linear models proved the most suitable for forecasting the onset and length of the pre-flowering period and the onset of flowering. The onset and length of pre-flowering can be predicted up to 1 or 2 months prior to budburst, whilst the onset of flowering can be forecast up to 3 months beforehand. By contrast, a nonlinear model using Poisson regression was best suited to predict the length of the flowering period.
Forecasting municipal solid waste generation using artificial intelligence modelling approaches.
Abbasi, Maryam; El Hanandeh, Ali
2016-10-01
Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg.
Models for forecasting the flowering of Cornicabra olive groves.
Rojo, Jesús; Pérez-Badia, Rosa
2015-11-01
This study examined the impact of weather-related variables on flowering phenology in the Cornicabra olive tree and constructed models based on linear and Poisson regression to forecast the onset and length of the pre-flowering and flowering phenophases. Spain is the world's leading olive oil producer, and the Cornicabra variety is the second largest Spanish variety in terms of surface area. However, there has been little phenological research into this variety. Phenological observations were made over a 5-year period (2009-2013) at four sampling sites in the province of Toledo (central Spain). Results showed that the onset of the pre-flowering phase is governed largely by temperature, which displayed a positive correlation with the temperature in the start of dormancy (November) and a negative correlation during the months prior to budburst (January, February and March). A similar relationship was recorded for the onset of flowering. Other weather-related variables, including solar radiation and rainfall, also influenced the succession of olive flowering phenophases. Linear models proved the most suitable for forecasting the onset and length of the pre-flowering period and the onset of flowering. The onset and length of pre-flowering can be predicted up to 1 or 2 months prior to budburst, whilst the onset of flowering can be forecast up to 3 months beforehand. By contrast, a nonlinear model using Poisson regression was best suited to predict the length of the flowering period.
Ensemble Forecasting of Coronal Mass Ejections Using the WSA-ENLIL with CONED Model
Emmons, D.; Acebal, A.; Pulkkinen, A.; Taktakishvili, A.; MacNeice, P.; Odstricil, D.
2013-01-01
The combination of the Wang-Sheeley-Arge (WSA) coronal model, ENLIL heliospherical model version 2.7, and CONED Model version 1.3 (WSA-ENLIL with CONED Model) was employed to form ensemble forecasts for 15 halo coronal mass ejections (halo CMEs). The input parameter distributions were formed from 100 sets of CME cone parameters derived from the CONED Model. The CONED Model used image processing along with the bootstrap approach to automatically calculate cone parameter distributions from SOHO/LASCO imagery based on techniques described by Pulkkinen et al. (2010). The input parameter distributions were used as input to WSA-ENLIL to calculate the temporal evolution of the CMEs, which were analyzed to determine the propagation times to the L1 Lagrangian point and the maximum Kp indices due to the impact of the CMEs on the Earth's magnetosphere. The Newell et al. (2007) Kp index formula was employed to calculate the maximum Kp indices based on the predicted solar wind parameters near Earth assuming two magnetic field orientations: a completely southward magnetic field and a uniformly distributed clock-angle in the Newell et al. (2007) Kp index formula. The forecasts for 5 of the 15 events had accuracy such that the actual propagation time was within the ensemble average plus or minus one standard deviation. Using the completely southward magnetic field assumption, 10 of the 15 events contained the actual maximum Kp index within the range of the ensemble forecast, compared to 9 of the 15 events when using a uniformly distributed clock angle.
Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter
Institute of Scientific and Technical Information of China (English)
Sheng JIN; Dian-hai WANG; Cheng XU; Dong-fang MA
2013-01-01
In this paper; a prediction model is developed that combines a Gaussian mixture model (GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision (TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe situations.Then,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood (ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization (EM) algorithm.We propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts (PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance estimation.The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets.These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.
A first large-scale flood inundation forecasting model
Energy Technology Data Exchange (ETDEWEB)
Schumann, Guy J-P; Neal, Jeffrey C.; Voisin, Nathalie; Andreadis, Konstantinos M.; Pappenberger, Florian; Phanthuwongpakdee, Kay; Hall, Amanda C.; Bates, Paul D.
2013-11-04
At present continental to global scale flood forecasting focusses on predicting at a point discharge, with little attention to the detail and accuracy of local scale inundation predictions. Yet, inundation is actually the variable of interest and all flood impacts are inherently local in nature. This paper proposes a first large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas and at continental scales. The model was built for the Lower Zambezi River in southeast Africa to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. The inundation model domain has a surface area of approximately 170k km2. ECMWF meteorological data were used to force the VIC (Variable Infiltration Capacity) macro-scale hydrological model which simulated and routed daily flows to the input boundary locations of the 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of many river channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst at the same time representing the floodplain at an appropriate and efficient scale. The modeling system was first calibrated using water levels on the main channel from the ICESat (Ice, Cloud, and land Elevation Satellite) laser altimeter and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of about 1 km (one model resolution) compared to an observed flood edge of the event. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2. However, initial model test runs in forecast mode
Deterministic and heuristic models of forecasting spare parts demand
Directory of Open Access Journals (Sweden)
Ivan S. Milojević
2012-04-01
Full Text Available Knowing the demand of spare parts is the basis for successful spare parts inventory management. Inventory management has two aspects. The first one is operational management: acting according to certain models and making decisions in specific situations which could not have been foreseen or have not been encompassed by models. The second aspect is optimization of the model parameters by means of inventory management. Supply items demand (asset demand is the expression of customers' needs in units in the desired time and it is one of the most important parameters in the inventory management. The basic task of the supply system is demand fulfillment. In practice, demand is expressed through requisition or request. Given the conditions in which inventory management is considered, demand can be: - deterministic or stochastic, - stationary or nonstationary, - continuous or discrete, - satisfied or unsatisfied. The application of the maintenance concept is determined by the technological level of development of the assets being maintained. For example, it is hard to imagine that the concept of self-maintenance can be applied to assets developed and put into use 50 or 60 years ago. Even less complex concepts cannot be applied to those vehicles that only have indicators of engine temperature - those that react only when the engine is overheated. This means that the maintenance concepts that can be applied are the traditional preventive maintenance and the corrective maintenance. In order to be applied in a real system, modeling and simulation methods require a completely regulated system and that is not the case with this spare parts supply system. Therefore, this method, which also enables the model development, cannot be applied. Deterministic models of forecasting are almost exclusively related to the concept of preventive maintenance. Maintenance procedures are planned in advance, in accordance with exploitation and time resources. Since the timing
Ten-year operational dust forecasting - Recent model development and future plans
Energy Technology Data Exchange (ETDEWEB)
Kallos, G; Spyrou, C; Astitha, M; Mitsakou, C; Solomos, S; Kushta, J; Pytharoulis, I; Katsafados, P; Mavromatidis, E; Papantoniou, N; Vlastou, G [University of Athens, School of Physics, Atmospheric Modeling and Weather Forecasting Group - UOA/AM and WFG, University Campus, Bldg. PHYS-V, Athens 15784 (Greece)], E-mail: kallos@mg.uoa.gr
2009-03-01
The Sahara desert is one of the major sources of mineral dust on Earth, producing up to 2x10{sup 8} t yr-{sup 1}. A combined effort has been devoted during the last ten years at the University of Athens (UOA) from the Atmospheric Modeling and Weather Forecasting Group (AM and WFG) to the development of an analysis and forecasting tool that will provide early warning of Saharan dust outbreaks. The developed tool is the SKIRON limited-area forecasting system, based on the Eta limited area modeling system with embedded algorithms describing the dust cycle. A new version of the model is currently available, with extra features like eight-size particle bins, radiative transfer corrections, new dust source identification and utilization of rocky soil characterization and incorporation of more accurate deposition schemes. The new version of SKIRON modeling system is coupled with the photochemical model CAMx in order to study processes like the shading effect of dust particles on photochemical processes and the production of second and third generation of aerosols. Moreover, another new development in the AM and WFG is based on the RAMS model, with the incorporation of processes like dust and sea-salt production, gas and aqueous phase chemistry and particle formation. In this study, the major characteristics of the developed (and under development) modeling systems are presented, as well as the spatiotemporal distribution of the transported dust amounts, the interaction with anthropogenically-produced particles and the potential implications on radiative transfer.
The systematic study of the stability of forecasts in the rate- and state-dependent model.
De Gaetano, D.; McCloskey, J.; Nalbant, S.
2012-04-01
likelihood methods. We compare the Dieterich and the Omori-Utsu forecasts using the Akaike information criterion which appropriately penalises each model for the number of free parameters used in the fit and explore the full spatial distribution of parameters, forecasts and forecast skill. We find that the Omori-Utsu law consistently out-performs the Dieterich model. The method described is then applied to other earthquake sequences and we assess its usefulness as a real time aftershock forecasting protocol. Finally, we produce a synthetic catalogue. The spatial seismicity of this catalogue is governed by the structural fractal complexity of the region, while the rate of the earthquakes (the temporal seismicity) follows the rate- and state-dependent model. This enables us to examine if we are able to recover the rate- and state-dependent model parameters and help us understand if the variable success in forecasting is due to the physics in the model differing from the real world or else due to the lack of enough data in the catalogue.
Distributed energy resources scheduling considering real-time resources forecast
DEFF Research Database (Denmark)
Silva, M.; Sousa, T.; Ramos, S.
2014-01-01
grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper...
Institute of Scientific and Technical Information of China (English)
齐雪雯; 谢开贵; 胡博
2012-01-01
风电机组多状态出力模型的准确建立是含风电机组配电网可靠性评估的关键.基于风速历史数据由近及远影响逐渐减弱的特性,提出一种新的风速分布预测模型,即风速分布预测的指数平滑模型,在指数平滑模型建立中,以风速预测值与实测值间的绝对误差最小为目标函数构造并确定平滑系数最优取值,从而建立风电机组多状态出力模型.通过同ARMA、Weibull分布等风速模型的比较,验证了指数平滑方法建立风电机组多状态出力模型的精确性与高效性.将该方法应用于IEEERBTS测试系统BUS 6的配电网可靠性评估,并与已有方法进行对比,表明该方法在可靠性评估计算精度上具有一定的优势.%Building an accurate multi-state output model of wind turbine generators (WTG) is a key element in the system reliability evaluation of electrical distribution networks (EDN) containing WTGs. This paper proposes an exponential smoothing method to forecast the wind speed distribution based on the fact that the closer to the current wind speed, the greater the wind speed has an influence on the wind speed distribution. The optimal smoothing factor of the proposed model is formulated as an optimization problem with the objective function of minimizing the sum of absolute errors between the predicted and statistic wind speeds, which can be used to accurately build the WTG multi-state output model. Compared with the ARMA and Weibull methods for wind speeds, the proposed model provides more accurate and efficient results. The case studies on the IEEE BUS6 EDN show that the proposed wind speed model has the advantage in the accuracy of EDN reliability evaluation compared with the traditional wind speed models. This work is supported by National Natural Science Foundation of China (No. 50777067 and No. 51077135).
Directory of Open Access Journals (Sweden)
Bo Qu
2017-01-01
Full Text Available Statistical post-processing for multi-model grand ensemble (GE hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA to statistically post-process raw GE runoff forecasts in the Fu River basin in China, at lead times ranging from 6 to 120 h. The raw forecasts were generated by running the Xinanjiang hydrologic model with ensemble forecasts (164 forecast members, using seven different “THORPEX Interactive Grand Global Ensemble” (TIGGE weather centres as forcing inputs. Some measures, such as data transformation and high-dimensional optimization, were included in the experiment after considering the practical water regime and data conditions. The results indicate that the BMA post-processing method is capable of improving the performance of raw GE runoff forecasts, yielding more calibrated and sharp predictive probability density functions (PDFs, over a range of lead times from 24 to 120 h. The analysis of percentile forecasts in two different flood events illustrates the great potential and prospects of BMA GE probabilistic river discharge forecasts, for taking precautions against severe flooding events.
Trend time-series modeling and forecasting with neural networks.
Qi, Min; Zhang, G Peter
2008-05-01
Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.
A simple forecasting model for industrial electric energy consumption
Energy Technology Data Exchange (ETDEWEB)
Al-Shehri, Abdallah [King Fahd Univ. of Petroleum and Minerals, Electrical Engineering Dept., Dhaharan (Saudi Arabia)
2000-07-01
A single-equation model is developed and employed for forecasting industrial electric energy consumption in the Saudi Consolidated Electric Company in the Eastern Province (SCECO-East) of Saudi Arabia. SCECO-East's industrial loads are composed mainly of oil-related and petrochemical industries. Even though industrial loads are generally characterised by their steadiness, the harsh weather conditions of the Eastern Province cause great variations in the industrial electric energy consumption at SCECO-East. The developed model reflects these variations. MATLAB is used to solve the model. (Author)
Application of nonlinear forecasting techniques for meteorological modeling
Directory of Open Access Journals (Sweden)
V. Pérez-Muñuzuri
Full Text Available A nonlinear forecasting method was used to predict the behavior of a cloud coverage time series several hours in advance. The method is based on the reconstruction of a chaotic strange attractor using four years of cloud absorption data obtained from half-hourly Meteosat infrared images from Northwestern Spain. An exhaustive nonlinear analysis of the time series was carried out to reconstruct the phase space of the underlying chaotic attractor. The forecast values are used by a non-hydrostatic meteorological model ARPS for daily weather prediction and their results compared with surface temperature measurements from a meteorological station and a vertical sounding. The effect of noise in the time series is analyzed in terms of the prediction results.
Key words: Meterology and atmospheric dynamics (mesoscale meteorology; general – General (new fields
Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model
Directory of Open Access Journals (Sweden)
Petrovska Magdalena
2016-09-01
Full Text Available This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005. In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.
Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh
2014-01-01
Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.
Morawietz, Martin; Xu, Chong-Yu; Gottschalk, Lars; Tallaksen, Lena
2010-05-01
A post-processor that accounts for the hydrologic uncertainty in a probabilistic streamflow forecast system is necessary to account for the uncertainty introduced by the hydrological model. In this study different variants of an autoregressive error model that can be used as a post-processor for short to medium range streamflow forecasts, are evaluated. The deterministic HBV model is used to form the basis for the streamflow forecast. The general structure of the error models then used as post-processor is a first order autoregressive model of the form dt = αdt-1 + σɛt where dt is the model error (observed minus simulated streamflow) at time t, α and σ are the parameters of the error model, and ɛt is the residual error described through a probability distribution. The following aspects are investigated: (1) Use of constant parameters α and σ versus the use of state dependent parameters. The state dependent parameters vary depending on the states of temperature, precipitation, snow water equivalent and simulated streamflow. (2) Use of a Standard Normal distribution for ɛt versus use of an empirical distribution function constituted through the normalized residuals of the error model in the calibration period. (3) Comparison of two different transformations, i.e. logarithmic versus square root, that are applied to the streamflow data before the error model is applied. The reason for applying a transformation is to make the residuals of the error model homoscedastic over the range of streamflow values of different magnitudes. Through combination of these three characteristics, eight variants of the autoregressive post-processor are generated. These are calibrated and validated in 55 catchments throughout Norway. The discrete ranked probability score with 99 flow percentiles as standardized thresholds is used for evaluation. In addition, a non-parametric bootstrap is used to construct confidence intervals and evaluate the significance of the results. The main
Forecasting Austrian national elections: The Grand Coalition model
Aichholzer, Julian; Willmann, Johanna
2014-01-01
Forecasting the outcomes of national elections has become established practice in several democracies. In the present paper, we develop an economic voting model for forecasting the future success of the Austrian ‘grand coalition’, i.e., the joint electoral success of the two mainstream parties SPOE and OEVP, at the 2013 Austrian Parliamentary Elections. Our main argument is that the success of both parties is strongly tied to the accomplishments of the Austrian system of corporatism, that is, the Social Partnership (Sozialpartnerschaft), in providing economic prosperity. Using data from Austrian national elections between 1953 and 2008 (n=18), we rely on the following predictors in our forecasting model: (1) unemployment rates, (2) previous incumbency of the two parties, and (3) dealignment over time. We conclude that, in general, the two mainstream parties benefit considerably from low unemployment rates, and are weakened whenever they have previously formed a coalition government. Further, we show that they have gradually been losing a good share of their voter basis over recent decades. PMID:26339109
Tsunami Modeling, Forecast and Warning (Invited)
Satake, K.
2010-12-01
Tsunami is an infrequent natural hazard; however, once it happens, the effects are devastating and can be on global scale, as demonstrated by the 2004 Indian Ocean tsunami. Deterministic modeling of tsunami generation, propagation and coastal behavior has become popular, at least for earthquake tsunamis. Once the earthquake parameters are specified, tsunami arrival times, heights and current velocity at specific coastal points, and inland inundation area can be estimated. Such modeling has been used to make hazard maps usually by assuming largest possible earthquakes. However, smaller tsunamis than such a worst-case scenario occur more frequently. If the hazard maps are used incorrectly, it may lose reliability of coastal residents. Probabilistic tsunami hazard assessments, similar to Probabilistic Seismic Hazard Analysis, have been made for some coasts. The output is tsunami hazard curves, i.e. annual probability (or return period) for specified coastal tsunami heights. A hazard curve is obtained by integration over the aleatory uncertainties, and a large number of hazard curves are made for each branch of logic tress representing epistemic uncertainty. Probabilistic tsunami hazard analysis is used for design of critical facilities but not popularly used for disaster mitigation. Tsunami warning systems, which have been significantly developed since 2004, rely on seismic and sea-level monitoring and pre-made numerical simulation. Real-time data assimilation of offshore sea level measurements can be used to update the warning levels. Tsunami from the February 2010 Chilean earthquake was recorded on many tide gauges and ocean bottom pressure gauges in the Pacific, before it arrived on the Japanese coast about 22 hours after the earthquake. The tsunami height was up to 2 m on the Japanese coast, causing fishery damage amounting 60 million US dollars, but did not cause any human damage.
Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems
Directory of Open Access Journals (Sweden)
Luis Hernández
2014-03-01
Full Text Available The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.
DEFF Research Database (Denmark)
Nielsen, Peter; Jiang, Liping; Rytter, Niels Gorm Malý
2014-01-01
This paper evaluates the influence of forecast horizon and observation fit on the robustness and performance of a specific freight rate forecast model used in the liner shipping industry. In the first stage of the research, a forecast model used to predict container freight rate development...... of the forecast horizon and observation fit and their interactions on the forecast model's performance. The results underline the complicated nature of creating a suitable forecast model by balancing business needs, a desire to fit a good model and achieve high accuracy. There is strong empirical evidence from...... this study; that a robust model is preferable, that overfitting is a true danger, and that a balance must be achieved between forecast horizon and the number of observations used to fit the model. In addition, methodological guidance has also been provided on how to test, design, and choose the superior...
Overview of Urban PM2. 5Numerical Forecast Models in China
Institute of Scientific and Technical Information of China (English)
Nianliang; CHENG; Hongxia; LI; Fan; MENG; Fahe; CHAI
2015-01-01
This paper made an overview and introduction of urban PM2. 5numerical forecast models in China,and mainly introduced air quality simulated forecast system of Beijing,Shanghai,and Nanjing. On this basis,it discussed development direction and existing problems of urban PM2. 5forecast models in China. Besides,it revealed significance of numerical models for air quality forecast. In a heavy air pollution of Beijing- Tianjin- Hebei in October 6- 12 th of 2014,the forecast results indicated that pollutants was transported from south to north,so the regional transport exerts great influence on concentration of PM2. 5.
Directory of Open Access Journals (Sweden)
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Forecasting Model of Coal Requirement Quantity Based on Grey System Theory
Institute of Scientific and Technical Information of China (English)
孙继湖
2001-01-01
The generally used methods of forecasting coal requirement quantity include the analogy method, the outside-push method and the cause-effect analysis method. However, the precision of forecasting results using these methods is lower. This paper uses the grey system theory, and sets up grey forecasting model GM (1, 3) to coal requirement quantity. The forecasting result for the Chinese coal requirement quantity coincides with the actual values, and this shows that the model is reliable. Finally, this model are used to forecast Chinese coal requirement quantity in the future ten years.
Forecasting the mortality rates using Lee-Carter model and Heligman-Pollard model
Ibrahim, R. I.; Ngataman, N.; Abrisam, W. N. A. Wan Mohd
2017-09-01
Improvement in life expectancies has driven further declines in mortality. The sustained reduction in mortality rates and its systematic underestimation has been attracting the significant interest of researchers in recent years because of its potential impact on population size and structure, social security systems, and (from an actuarial perspective) the life insurance and pensions industry worldwide. Among all forecasting methods, the Lee-Carter model has been widely accepted by the actuarial community and Heligman-Pollard model has been widely used by researchers in modelling and forecasting future mortality. Therefore, this paper only focuses on Lee-Carter model and Heligman-Pollard model. The main objective of this paper is to investigate how accurately these two models will perform using Malaysian data. Since these models involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 8.0 (MATLAB 8.0) software will be used to estimate the parameters of the models. Autoregressive Integrated Moving Average (ARIMA) procedure is applied to acquire the forecasted parameters for both models as the forecasted mortality rates are obtained by using all the values of forecasted parameters. To investigate the accuracy of the estimation, the forecasted results will be compared against actual data of mortality rates. The results indicate that both models provide better results for male population. However, for the elderly female population, Heligman-Pollard model seems to underestimate to the mortality rates while Lee-Carter model seems to overestimate to the mortality rates.