Landslide susceptibility mapping along PLUS expressways in Malaysia using probabilistic based model in GIS

Science.gov (United States)

Yusof, Norbazlan M.; Pradhan, Biswajeet

2014-06-01

PLUS Berhad holds the concession for a total of 987 km of toll expressways in Malaysia, the longest of which is the North-South Expressway or NSE. Acting as the backbone' of the west coast of the peninsula, the NSE stretches from the Malaysian-Thai border in the north to the border with neighbouring Singapore in the south, linking several major cities and towns along the way. North-South Expressway in Malaysia contributes to the country economic development through trade, social and tourism sector. Presently, the highway is good in terms of its condition and connection to every state but some locations need urgent attention. Stability of slopes at these locations is of most concern as any instability can cause danger to the motorist. In this paper, two study locations have been analysed; they are Gua Tempurung (soil slope) and Jelapang (rock slope) which are obviously having two different characteristics. These locations passed through undulating terrain with steep slopes where landslides are common and the probability of slope instability due to human activities in surrounding areas is high. A combination of twelve (12) landslide conditioning factors database on slope stability such as slope degree and slope aspect were extracted from IFSAR (interoferometric synthetic aperture radar) while landuse, lithology and structural geology were constructed from interpretation of high resolution satellite data from World View II, Quickbird and Ikonos. All this information was analysed in geographic information system (GIS) environment for landslide susceptibility mapping using probabilistic based frequency ratio model. Consequently, information on the slopes such as inventories, condition assessments and maintenance records were assessed through total expressway maintenance management system or better known as TEMAN. The above mentioned system is used by PLUS as an asset management and decision support tools for maintenance activities along the highways as well as for data

Landslide susceptibility mapping along PLUS expressways in Malaysia using probabilistic based model in GIS

International Nuclear Information System (INIS)

Yusof, Norbazlan M; Pradhan, Biswajeet

2014-01-01

PLUS Berhad holds the concession for a total of 987 km of toll expressways in Malaysia, the longest of which is the North-South Expressway or NSE. Acting as the backbone' of the west coast of the peninsula, the NSE stretches from the Malaysian-Thai border in the north to the border with neighbouring Singapore in the south, linking several major cities and towns along the way. North-South Expressway in Malaysia contributes to the country economic development through trade, social and tourism sector. Presently, the highway is good in terms of its condition and connection to every state but some locations need urgent attention. Stability of slopes at these locations is of most concern as any instability can cause danger to the motorist. In this paper, two study locations have been analysed; they are Gua Tempurung (soil slope) and Jelapang (rock slope) which are obviously having two different characteristics. These locations passed through undulating terrain with steep slopes where landslides are common and the probability of slope instability due to human activities in surrounding areas is high. A combination of twelve (12) landslide conditioning factors database on slope stability such as slope degree and slope aspect were extracted from IFSAR (interoferometric synthetic aperture radar) while landuse, lithology and structural geology were constructed from interpretation of high resolution satellite data from World View II, Quickbird and Ikonos. All this information was analysed in geographic information system (GIS) environment for landslide susceptibility mapping using probabilistic based frequency ratio model. Consequently, information on the slopes such as inventories, condition assessments and maintenance records were assessed through total expressway maintenance management system or better known as TEMAN. The above mentioned system is used by PLUS as an asset management and decision support tools for maintenance activities along the highways as well as for

Landslide Susceptibility Index Determination Using Aritificial Neural Network

Science.gov (United States)

Kawabata, D.; Bandibas, J.; Urai, M.

2004-12-01

The occurrence of landslide is the result of the interaction of complex and diverse environmental factors. The geomorphic features, rock types and geologic structure are especially important base factors of the landslide occurrence. Generating landslide susceptibility index by defining the relationship between landslide occurrence and that base factors using conventional mathematical and statistical methods is very difficult and inaccurate. This study focuses on generating landslide susceptibility index using artificial neural networks in Southern Japanese Alps. The training data are geomorphic (e.g. altitude, slope and aspect) and geologic parameters (e.g. rock type, distance from geologic boundary and geologic dip-strike angle) and landslides. Artificial neural network structure and training scheme are formulated to generate the index. Data from areas with and without landslide occurrences are used to train the network. The network is trained to output 1 when the input data are from areas with landslides and 0 when no landslide occurred. The trained network generates an output ranging from 0 to 1 reflecting the possibility of landslide occurrence based on the inputted data. Output values nearer to 1 means higher possibility of landslide occurrence. The artificial neural network model is incorporated into the GIS software to generate a landslide susceptibility map.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.

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Jie Dou

Full Text Available This paper assesses the potentiality of certainty factor models (CF for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED was split into two subsets: (i 70% of the landslides in the inventory to be used for building the CF based model; (ii 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI and logistic regression (LR models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC. The result shows that the area under the ROC curve (AUC for the LR model (AUC = 0.817 is slightly higher than those obtained from the SI model (AUC = 0.801. Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.

Science.gov (United States)

Dou, Jie; Tien Bui, Dieu; Yunus, Ali P; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

2015-01-01

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.

Earthquake-induced landslide-susceptibility mapping using an artificial neural network

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S. Lee

2006-01-01

Full Text Available The purpose of this study was to apply and verify landslide-susceptibility analysis techniques using an artificial neural network and a Geographic Information System (GIS applied to Baguio City, Philippines. The 16 July 1990 earthquake-induced landslides were studied. Landslide locations were identified from interpretation of aerial photographs and field survey, and a spatial database was constructed from topographic maps, geology, land cover and terrain mapping units. Factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from faults were derived from the geology database. Land cover was identified from the topographic database. Terrain map units were interpreted from aerial photographs. These factors were used with an artificial neural network to analyze landslide susceptibility. Each factor weight was determined by a back-propagation exercise. Landslide-susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from GIS data. The susceptibility map was compared with known landslide locations and verified. The demonstrated prediction accuracy was 93.20%.

Landslide susceptibility zonation in part of Tehri reservoir region

Indian Academy of Sciences (India)

Fuzzy logic; landslide susceptibility; frequency ratio. ... zones using landslide frequency ratio and fuzzy logic in GIS environment is presented for Tehri ... Temporal remote sensing data was used to prepare important landslide causative factor ...

Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

Science.gov (United States)

Tien Bui, Dieu; Pradhan, Biswajeet; Lofman, Owe; Revhaug, Inge; Dick, Oystein B.

2012-08-01

The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the prediction capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest prediction capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

MULTI-CRITERIA ANALYSIS APPLIED TO LANDSLIDE SUSCEPTIBILITY MAPPING

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Mariana Madruga de Brito

2017-10-01

Full Text Available This paper presents the application of a multi-criteria analysis (MCA tool for landslide susceptibility assessment in Porto Alegre municipality, southern Brazil. A knowledge driven approach was used, aiming to ensure an optimal use of the available information. The landslide conditioning factors considered were slope, lithology, flow accumulation and distance from lineaments. Standardization of these factors was done through fuzzy membership functions, and evaluation of their relative importance for landslide predisposition was supported by the analytic hierarchy process (AHP, based on local expert knowledge. Finally, factors were integrated in a GIS environment using the weighted linear combination (WLC method. For validation, an inventory, including 107 landslide points recorded between 2007 and 2013 was used. Results indicated that 8.2% (39.40 km² of the study area are highly and very highly susceptible to landslides. An overall accuracy of 95% was found, with an area under the receiver operating characteristic (ROC curve of 0.960. Therefore, the resulting map can be regarded as useful for monitoring landslide-prone areas. Based on the findings, it is concluded that the proposed method is effective for susceptibility assessment since it yielded meaningful results and does not require extensive input data.

Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale

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Christos Chalkias

2016-03-01

Full Text Available In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP and Peak Ground Acceleration (PGA—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC analysis depending on an independent (validation dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale.

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

Science.gov (United States)

Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

2016-02-01

The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

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Saro Lee

2016-02-01

Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

Susceptibility analysis of landslide in Chittagong City Corporation Area, Bangladesh

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Sourav Das

2015-06-01

Full Text Available In Chittagong city, landslide phenomena is the most burning issue which causes great problems to the life and properties and it is increasing day by day and becoming one of the main problems of city life. On 11 June 2007, a massive landslide happened in Chittagong City Corporation (CCC area, a large number of foothill settlements and slums were demolished; more than 90 people died and huge resource destruction took place. It is therefore essential to analyze the landslide susceptibility for CCC area to prepare mitigation strategies as well as assessing the impacts of climate change. To assess community susceptibility of landslide hazard, a landslide susceptibility index map has been prepared using analytical hierarchy process (AHP model based on geographic information system (GIS and remote sensing (RS and its susceptibility is analyzed through community vulnerability assessment tool (CVAT. The major findings of the research are 27% of total CCC area which is susceptible to landslide hazard and whereas 6.5 sq.km areas are found very highly susceptible. The landslide susceptible areas of CCC have also been analyzed in respect of physical, social, economic, environmental and critical facilities and it is found that the overall CCC area is highly susceptible to landslide hazard. So the findings of the research can be utilized to prioritize risk mitigation investments, measures to strengthen the emergency preparedness and response mechanisms for reducing the losses and damages due to future landslide events. DOI: http://dx.doi.org/10.3126/ije.v4i2.12635 International Journal of Environment Vol.4(2 2015: 157-181

Evaluating performances of simplified physically based landslide susceptibility models.

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Capparelli, Giovanna; Formetta, Giuseppe; Versace, Pasquale

2015-04-01

Rainfall induced shallow landslides cause significant damages involving loss of life and properties. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. This paper presents a package of GIS based models for landslide susceptibility analysis. It was integrated in the NewAge-JGrass hydrological model using the Object Modeling System (OMS) modeling framework. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices (GOF) by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system offers the possibility to investigate and fairly compare the quality and the robustness of models and models parameters, according a procedure that includes: i) model parameters estimation by optimizing each of the GOF index separately, ii) models evaluation in the ROC plane by using each of the optimal parameter set, and iii) GOF robustness evaluation by assessing their sensitivity to the input parameter variation. This procedure was repeated for all three models. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, Average Index (AI) optimization coupled with model M3 is the best modeling solution for our test case. This research was funded by PON Project No. 01_01503 "Integrated Systems for Hydrogeological Risk

Application of an advanced fuzzy logic model for landslide susceptibility analysis

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Biswajeet Pradhan

2010-09-01

Full Text Available The aim of this study is to evaluate the susceptibility of landslides at Klang valley area, Malaysia, using a Geographic Information System (GIS and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. A data derived model (frequency ratio and a knowledge-derived model (fuzzy operator were combined for landslide susceptibility analysis. The nine factors that influence landslide occurrence were extracted from the database and the frequency ratio coefficient for each factor was computed. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslide susceptibility mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (l = 0.8 showed the best accuracy (91% while the case in which the fuzzy algebraic product was applied showed the worst accuracy (79%.

A precipitation-induced landslide susceptibility model for natural gas transmission pipelines

Energy Technology Data Exchange (ETDEWEB)

Finley, Jason P. [Fugro William Lettis and Associates, Inc., Valencia, California (United States); Slayter, David L.; Hitchcock, Chris S. [Fugro William Lettis and Associates, Inc., Walnut Creek, California (United States); Lee, Chih-Hung [Pacific Gas and Electric Company, Gas Systems Integrity Management, Walnut Creek, California (United States)

2010-07-01

Landslides related to heavy rainfall can cause extensive damage to natural gas transmission pipelines. Fugro William Lettis and Associates Inc. have developed and implemented a geographic information system (GIS) model that evaluates near real-time precipitation-induced landslide susceptibility. The model incorporates state-wide precipitation data and geologically-based landslide classifications to produce rapid landslide risk evaluation for Pacific Gas and Electric Company's (PGandE) gas transmission system during winter rain storms in California. The precipitation data include pre-storm event quantitative precipitation forecasts (QPF) and post-storm event quantitative precipitation estimate (QPE) from the United States National Oceanic and Atmospheric Administration (NOAA). The geologic classifications are based on slope, susceptible geologic formations, and the locations of historic or known landslide occurrences. Currently the model is calibrated using qualitative measures. This paper describes the development of the model algorithm and input data, model results, calibration efforts, and the on-going research and landslide collection warranted for continued refinement of the model.

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

Science.gov (United States)

Oh, Hyun-Joo; Pradhan, Biswajeet

2011-09-01

This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.

Landslide susceptibility mapping using a neuro-fuzzy

Science.gov (United States)

Lee, S.; Choi, J.; Oh, H.

2009-12-01

This paper develops and applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. A neuro-fuzzy system is based on a fuzzy system that is trained by a learning algorithm derived from the neural network theory. The learning procedure operates on local information, and causes only local modifications in the underlying fuzzy system. The study area, Boun, suffered much damage following heavy rain in 1998 and was selected as a suitable site for the evaluation of the frequency and distribution of landslides. Boun is located in the central part of Korea. Landslide-related factors such as slope, soil texture, wood type, lithology, and density of lineament were extracted from topographic, soil, forest, and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs) and analysis results were verified using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping. Various MFs were used in this study, and after verification, the difference in accuracy according to the MFs was small, between 84.81% and 84.96%. The difference was just 0.15% and therefore the choice of MFs was not important in the study. Also, compared with the likelihood ratio model, which showed 84.94%, the accuracy was similar. Thus, the ANFIS could be applied to other study areas with different data and other study methods such as cross-validation. The developed ANFIS learns the if-then rules between landslide-related factors and landslide

Landslide Catastrophes and Disaster Risk Reduction: A GIS Framework for Landslide Prevention and Management

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Xin Wang

2010-09-01

Full Text Available As catastrophic phenomena, landslides often cause large-scale socio-economic destruction including loss of life, economic collapse, and human injury. In addition, landslides can impair the functioning of critical infrastructure and destroy cultural heritage and ecological systems. In order to build a more landslide resistant and resilient society, an original GIS-based decision support system is put forth in order to help emergency managers better prepare for and respond to landslide disasters. The GIS-based landslide monitoring and management system includes a Central Repository System (CRS, Disaster Data Processing Modules (DDPM, a Command and Control System (CCS and a Portal Management System (PMS. This architecture provides valuable insights into landslide early warning, landslide risk and vulnerability analyses, and critical infrastructure damage assessments. Finally, internet-based communications are used to support landslide disaster modelling, monitoring and management.

GIS based Grid overlay method versus modeling approach – A comparative study for landslide hazard zonation (LHZ in Meta Robi District of West Showa Zone in Ethiopia

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Tarun Kumar Raghuvanshi

2015-12-01

Full Text Available The present study area is located in Meta Robi District of West Showa Zone in Oromiya Regional State in Ethiopia. The main objective of the present study was to evaluate landslide hazard zonation (LHZ by utilizing ‘Grid overlay’ and ‘GIS modeling’ approaches. Also, it was attempted to know the effectiveness of the two methods. The methodology followed was based on the analysis of past landslides in the area. For the present study six causative factors namely; slope material, slope, aspect, elevation, land use and land cover and groundwater surface traces were considered. Later, Landslide Susceptibility Index (LSI was computed based on the relative influence of causative factors on past landslides. For the ‘Grid overlay’ method a grid with cells 10 m by 10 m was overlaid over the study area and later it was geo-processed to delineate various sub-classes of each causative factor. LSI values were assigned to each sub-causative factor within each grid cell and a ‘Total Landslide Susceptibility Index’ was calculated to produce the LHZ map. For ‘GIS modeling’ the same causative factors and similar LSI values were utilized. In the case of LHZ map prepared by the ‘Grid overlay’ method about 82% of past landslides fall within ‘very high hazard’ or ‘high hazard’ zones whereas in the case of ‘GIS modeling’ about 95% of past landslides fall within ‘very high hazard’ or ‘high hazard’ zones. Finally, the validation showed that ‘GIS modeling’ produced better LHZ map. Also, ‘Grid overlay’ method is more tedious and time consuming as compared to GIS modeling.

Landslide susceptibility mapping for a part of North Anatolian Fault Zone (Northeast Turkey) using logistic regression model

Science.gov (United States)

Demir, Gökhan; aytekin, mustafa; banu ikizler, sabriye; angın, zekai

2013-04-01

The North Anatolian Fault is know as one of the most active and destructive fault zone which produced many earthquakes with high magnitudes. Along this fault zone, the morphology and the lithological features are prone to landsliding. However, many earthquake induced landslides were recorded by several studies along this fault zone, and these landslides caused both injuiries and live losts. Therefore, a detailed landslide susceptibility assessment for this area is indispancable. In this context, a landslide susceptibility assessment for the 1445 km2 area in the Kelkit River valley a part of North Anatolian Fault zone (Eastern Black Sea region of Turkey) was intended with this study, and the results of this study are summarized here. For this purpose, geographical information system (GIS) and a bivariate statistical model were used. Initially, Landslide inventory maps are prepared by using landslide data determined by field surveys and landslide data taken from General Directorate of Mineral Research and Exploration. The landslide conditioning factors are considered to be lithology, slope gradient, slope aspect, topographical elevation, distance to streams, distance to roads and distance to faults, drainage density and fault density. ArcGIS package was used to manipulate and analyze all the collected data Logistic regression method was applied to create a landslide susceptibility map. Landslide susceptibility maps were divided into five susceptibility regions such as very low, low, moderate, high and very high. The result of the analysis was verified using the inventoried landslide locations and compared with the produced probability model. For this purpose, Area Under Curvature (AUC) approach was applied, and a AUC value was obtained. Based on this AUC value, the obtained landslide susceptibility map was concluded as satisfactory. Keywords: North Anatolian Fault Zone, Landslide susceptibility map, Geographical Information Systems, Logistic Regression Analysis.

A Spatially Explicit Approach for Sensitivity and Uncertainty Analysis of GIS-Multicriteria Landslide Susceptibility Mapping. GI_Forum 2013 – Creating the GISociety|

OpenAIRE

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2016-01-01

GIS multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping for the prediction of future hazards, decision making, as well as hazard mitigation plans. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are prone to multiple types of uncertainty. In this paper, the spatiality explicitly method is employed to assess the uncertainty associated with two methods of GIS-MCDA namely, Analytical Hierarchi...

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆

Science.gov (United States)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-01-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights. PMID:25843987

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

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Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

Manifestation of a neuro-fuzzy model to produce landslide susceptibility map using remote sensing data derived parameters

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Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred

Landslides are the most common natural hazards in Malaysia. Preparation of landslide suscep-tibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this study, a new attempt is tried to produce landslide susceptibility map of a part of Cameron Valley of Malaysia. This paper develops an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment for landslide susceptibility mapping. To ob-tain the neuro-fuzzy relations for producing the landslide susceptibility map, landslide locations were identified from interpretation of aerial photographs and high resolution satellite images, field surveys and historical inventory reports. Landslide conditioning factors such as slope, plan curvature, distance to drainage lines, soil texture, lithology, and distance to lineament were extracted from topographic, soil, and lineament maps. Landslide susceptible areas were analyzed by the ANFIS model and mapped using the conditioning factors. Furthermore, we applied various membership functions (MFs) and fuzzy relations to produce landslide suscep-tibility maps. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. Results show that, triangular, trapezoidal, and polynomial MFs were the best individual MFs for modelling landslide susceptibility maps (86

Landslides along Highways: GIS-based Inventory and Planning Issues

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Jaeger, Ann-Kathrin; Klose, Martin; Damm, Bodo

2015-04-01

Highways rank as critical transportation infrastructures that are at risk of landslides in many areas worldwide (e.g., Hungr et al., 1999; Bhandary et al., 2013). Safe and affordable operations of traffic routes constitute the two main criteria for transportation planning in landslide-prone terrain. A right balancing of these often conflicting priorities requires profound knowledge of landslide locations across highway networks and the costs caused by landslides in the past (e.g., Saha et al., 2005). Much of the direct costs affecting transportation departments relate to capital investments for landslide repair or mitigation and operational expenditures in connection with maintenance works. A systematic collection and inventory of such data sets combined with an acquisition of hazard information on vulnerable road sections is still rarely the case in engineering practice. This is despite significant cost impacts and budgetary burdens, especially in peripheral mountain areas where financial resources are naturally limited (e.g., Klose et al., 2014). The present contribution introduces a regional inventory of landslides along highways in the Harz Mountains, NW Germany. As subset of a landslide database for the entire country, this focused GIS-based inventory has been compiled in close collaboration with the Lower Saxony Department of Transportation. The inventory includes data sets gathered by archive studies and relies on high-quality information sources such as maintenance protocols, geotechnical reports, and documents from tendering, controlling, and accounting. A mapping tool in ArcGIS format is used to specify and visualize road sections affected by landslides. This spatial information on hazard exposure is complemented by narrative risk profiles for landslide sites showing a long history of damage events. By summarizing the occurrence dates of landslides, the associated damages, and the types and costs of repair or prevention, such risk profiles are useful to

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

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Pradhan, Biswajeet

2013-02-01

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e

Adjustment of the problems of landslide GIS data

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Uchiyama, S.; Doshida, S.; Oyagi, N.; Shimizu, F.; Inokuchi, T.

2012-12-01

Information on the distribution of landslides is a basic type of data used by countries for disaster prevention. Since 1972, 1:50,000 landslide maps have been produced at the Japanese National Research Institute for Earth Science and Disaster Prevention. From October 2000, the institute has been producing landslide GIS data and transmitting these data over the web. The area that has been published so far covers over 80% of Japan. Presently, the number of diagrams printed are 980 (March 2012). In addition, 350,000 landslide GIS data graphs have been digitized with the same diagrams as a base. Twelve years have passed since this GIS data acquisition program was launched, and in that time, several problems have been identified. These problems are listed below. 1) Scarps do not become polygonized. 2) Landslides which extend over the boundaries of the printed graphs are divided into separate elements. 3) When the time taken to read and interpret the landslide data differs, the shape of the landslides can vary between diagrams. 4) There have been cases of inaccurate positions and shapes in landslide GIS data produced since 2005. 5) Obvious mistakes are present in the attribute data. The causes of such problems are as follows: 1) Lack of technical examination at the time of the start of the production of the landslide GIS data. 2) Limitations of the landslide GIS data editing systems which were developed separately. 3) Program bugs which occur during the conversion of information input to an individual editing system into general-purpose GIS data. 4) Problems which arise during the process of the production of landslide GIS data. This project at the National Research Institute for Earth Science and Disaster Prevention is planned to be completed in 2013. By the end of the project, we hope to present a catalogue of all identified problems and formulate a plan to resolve them, and pass them on to the next generation.; Problems: For the diagram, scarps are presented by

Landslide susceptibility assessment using Spatial Analysis and GIS modeling in Cluj-Napoca Metropolitan Area, Romania

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Bogdan Eugen Dolean

2017-06-01

Full Text Available In Romania, landslides together with the multitude geomorphological processes linked to them are some of the most common hazards which manifested in vulnerable areas with important human activities can induce many negative effects. From this perspective, identifying the areas affected by landslides, based on GIS spatial analysis models and statistical methods, is a subject frequently discussed in the national and international literature. This research was focused on the methods and practices of GIS spatial analysis, with a target of creating a complex model and a viable methodology of assessment the probability of occurrence of landslides, applicable within any territory. The study was based on the identification and analysis in a bivariate systemic manner of the numerous factors involved in the production of landslides, such as topography, morphology, hydrography, geological, lithology, weather, land use. The area in which the analysis has been conducted, The Metropolitan Area of Cluj-Napoca, was chosen due to the exacerbated urbanization of the recent years, coupled with a massive increase in the number of inhabitants, thus being a space of socioeconomic importance and a real challenge regarding spatial planning. Applying the model in this area has generated relatively good results, with a power of predictability of over 80%, measured in landslides sample areas used for the validation of the results, fact which attest the viability of the model and the fact that the model can be used in different areas with related morphometric and environmental characteristics.

Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model

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García-Rodríguez, M. J.; Malpica, J. A.

2010-06-01

This paper presents an approach for assessing earthquake-triggered landslide susceptibility using artificial neural networks (ANNs). The computational method used for the training process is a back-propagation learning algorithm. It is applied to El Salvador, one of the most seismically active regions in Central America, where the last severe destructive earthquakes occurred on 13 January 2001 (Mw 7.7) and 13 February 2001 (Mw 6.6). The first one triggered more than 600 landslides (including the most tragic, Las Colinas landslide) and killed at least 844 people. The ANN is designed and programmed to develop landslide susceptibility analysis techniques at a regional scale. This approach uses an inventory of landslides and different parameters of slope instability: slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness. The information obtained from ANN is then used by a Geographic Information System (GIS) to map the landslide susceptibility. In a previous work, a Logistic Regression (LR) was analysed with the same parameters considered in the ANN as independent variables and the occurrence or non-occurrence of landslides as dependent variables. As a result, the logistic approach determined the importance of terrain roughness and soil type as key factors within the model. The results of the landslide susceptibility analysis with ANN are checked using landslide location data. These results show a high concordance between the landslide inventory and the high susceptibility estimated zone. Finally, a comparative analysis of the ANN and LR models are made. The advantages and disadvantages of both approaches are discussed using Receiver Operating Characteristic (ROC) curves.

Geographic Information Systems and geomorphological mapping applied to landslide inventory and susceptibility mapping in El Estado river, Pico de Orizaba, Mexico

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José Fernando Aceves Quesada

2016-11-01

-off switching of layers in the GIS system, a base map is created to assist in the digitizing of landslides and the modeling of susceptibility. A landslide inventory is created from aerial photographs, field investigations, and all the above GIS thematic layers. El Estado river watershed on the southwestern flank of Pico de Orizaba volcano has been selected as study area. The watershed is located in the southwestern slope of Citlaltepetl or Pico de Orizaba volcano. Geological (the stream channel of El Estado river erodes Tertiary and Quaternary lavas, disjointed volcanoclastic materials such as pyroclastic flows, fall deposits, lahars deposits, and alluvium and geomorphological factors (steep slopes, energy relief, and vertical erosion in combination with high seasonal rainfall (annual rainfall averages 1000-1100 mm/yr at > 4000 m a.s.l. and 927 mm/yr at <1500 m a.s.l., and the high degree of weathering, make the study area susceptible to landslides. To assess landslide susceptibility, a landslide inventory map and geomorphometric cartography (altimetry, slope and geomorphography were reviewed, and field work was conducted. In the study area, more than one hundred landslides were mapped. Shallow landslides (including debris slides and debris flows are the predominant type. Shallow landslides predominate on hills capped with ash and pyroclastic deposits. The second major landslide process includes rock falls (which occur where the stream erodes lava flows and lahars and deep-seated landslides (which occur in ash and pyroclastic deposits where lava flows act as a slip plane. In parallel, the spatial geodatabase of landslides was constructed from standardized GIS datasets. Pertinent attributes are recorded on a geo-dataset. These include 1 mass wasting process, 2 level of certainty of the observation, 3 photo identification date, 4 landslide size, 5 landslide activity, 6 landslide parts (head, evacuation zone, deposit, 7 slope shape, 8 field slope gradient, 9 map gradient measured

GIS-Based Terrain Analysis of Balakot Region after Occurred Landslide Disaster in October 2005

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Abdul Salam Soomro

2011-10-01

Full Text Available The landslide susceptibility models require the appropriate and reliable terrain analytical based study of the landslides prone areas using SRTM (Shuttle Radar Topography Mission data, based on certain GIS (Geographical Information Systems and remote sensing techniques. This research paper focuses on the analysis of the terrain conditions of Balakot region. The analytical operations have been used in the different phases: (i Extracting the study area from the large data; (ii preparing it into grid format; (iii developing contour lines with certain contour intervals (iv Re-classification of it into required classes and (v preparation of digital terrain model with its different required various supplementary models for analyzing the terrain conditions of the study area located in Mansehra district, north part of Pakistan where the great earthquake induced landslide disaster occurred in October 2005. This analytical study has notified the different sensitive issues concerning to the critical slope angles, variation in the elevation and the surface of study area. The various distinctions in the terrain phenomenon validate the occurred and probable landslides because the topography of such study area can predict the various probable landslide hazards, vulnerability and risk threats in the region again. This analytical study can be useful for the decisive authorities by becoming pro-active to rebuild the region to mitigate the expected losses from the natural disaster.

Landslide Hazard Assessment and Mapping in the Guil Catchment (Queyras, Southern French Alps): From Landslide Inventory to Susceptibility Modelling

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Roulleau, Louise; Bétard, François; Carlier, Benoît; Lissak, Candide; Fort, Monique

2016-04-01

Landslides are common natural hazards in the Southern French Alps, where they may affect human lives and cause severe damages to infrastructures. As a part of the SAMCO research project dedicated to risk evaluation in mountain areas, this study focuses on the Guil river catchment (317 km2), Queyras, to assess landslide hazard poorly studied until now. In that area, landslides are mainly occasional, low amplitude phenomena, with limited direct impacts when compared to other hazards such as floods or snow avalanches. However, when interacting with floods during extreme rainfall events, landslides may have indirect consequences of greater importance because of strong hillslope-channel connectivity along the Guil River and its tributaries (i.e. positive feedbacks). This specific morphodynamic functioning reinforces the need to have a better understanding of landslide hazards and their spatial distribution at the catchment scale to prevent local population from disasters with multi-hazard origin. The aim of this study is to produce a landslide susceptibility mapping at 1:50 000 scale as a first step towards global estimation of landslide hazard and risk. The three main methodologies used for assessing landslide susceptibility are qualitative (i.e. expert opinion), deterministic (i.e. physics-based models) and statistical methods (i.e. probabilistic models). Due to the rapid development of geographical information systems (GIS) during the last two decades, statistical methods are today widely used because they offer a greater objectivity and reproducibility at large scales. Among them, multivariate analyses are considered as the most robust techniques, especially the logistic regression method commonly used in landslide susceptibility mapping. However, this method like others is strongly dependent on the accuracy of the input data to avoid significant errors in the final results. In particular, a complete and accurate landslide inventory is required before the modelling

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

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Qianqian Ba

2017-01-01

Full Text Available Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM, an information value model improved by an analytic hierarchy process (IVM-AHP and our new improved model. Approximately 70% (5905 of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530 were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model

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M. J. García-Rodríguez

2010-06-01

Full Text Available This paper presents an approach for assessing earthquake-triggered landslide susceptibility using artificial neural networks (ANNs. The computational method used for the training process is a back-propagation learning algorithm. It is applied to El Salvador, one of the most seismically active regions in Central America, where the last severe destructive earthquakes occurred on 13 January 2001 (M_w 7.7 and 13 February 2001 (M_w 6.6. The first one triggered more than 600 landslides (including the most tragic, Las Colinas landslide and killed at least 844 people.

The ANN is designed and programmed to develop landslide susceptibility analysis techniques at a regional scale. This approach uses an inventory of landslides and different parameters of slope instability: slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness. The information obtained from ANN is then used by a Geographic Information System (GIS to map the landslide susceptibility. In a previous work, a Logistic Regression (LR was analysed with the same parameters considered in the ANN as independent variables and the occurrence or non-occurrence of landslides as dependent variables. As a result, the logistic approach determined the importance of terrain roughness and soil type as key factors within the model. The results of the landslide susceptibility analysis with ANN are checked using landslide location data. These results show a high concordance between the landslide inventory and the high susceptibility estimated zone. Finally, a comparative analysis of the ANN and LR models are made. The advantages and disadvantages of both approaches are discussed using Receiver Operating Characteristic (ROC curves.

Mapping Landslides Susceptibility in a Traditional Northern Nigerian City

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Oluwafemi, Olawale A.; Yakubu, Tahir A.; Muhammad, Mahmud U.; Shitta, Nyofo; Akinwumiju, Akinola S.

2018-05-01

As a result of dearth of relevant information about Landslides Susceptibility in Nigeria, the monitoring and assessment appears intractable. Hence, the study developed a Remote Sensing approach to mapping landslides susceptibility, landuse and landcover analysis in Jos South LGA, Plateau State, Nigeria. Field Observation, SPOT 5 2009 and 2012, ASTER DEM 2009, Geological Map 2006, Topographical Map 1966 were used to map Landslide Susceptibility and Landuse /Lancover Analysis in the study area. Geospatial Analytical Operations employed using ArcGIS 10.3 and Erdas Imagine 2014 include Spatial Modeling, Vectorization, Pre-lineament Extraction, Image Processing among others. Result showed that 72.38 % of the study area is underlain by granitic rocks. The landuse/cover types delineated for the study area include floodplain (29.27 %), farmland (23.96 %), sparsely vegetated land (15.43 %), built up area (13.65 %), vegetated outcrop (8.48 %), light vegetation (5.37 %), thick vegetation (2.39 %), water body (0.58 %), plantation (0.50 %) and mining pond (0.37 %). Landslide Susceptibility Analysis also revealed that 87 % of the study area is relatively at low to very low risk of landslide event. While only 13 % of the study area is at high to very high risk of landslide event. The study revealed that the susceptibility of landslide event is very low in the study area. However, possible landslide event in the hot spots could be pronounced and could destabilize the natural and man-made environmental systems of the study area.

An overview of a GIS method for mapping landslides and assessing landslide hazards at Río El Estado watershed, on the SW flank of Pico de Orizaba Volcano, Mexico

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Legorreta Paulin, G.; Bursik, M. I.; Contreras, T.; Polenz, M.; Ramírez Herrera, M.; Paredes Mejía, L.; Arana Salinas, L.

2012-12-01

This poster provides an overview of the on-going research project (Grant SEP-CONACYT no 167495) from the Institute of Geography at the National Autonomous University of Mexico (UNAM) that seeks to conduct a multi-temporal landslide inventory, produce a landslide susceptibility map, and estimate sediment production by using Geographic Information Systems (GIS). The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano, the highest mountain in Mexico, is selected as a study area. The catchment covers 5.2 km2 with elevations ranging from 2676.79 to 4248.2 m a.s.l. and hillslopes between 0° and 56°. The stream system of Río El Estado catchment erodes Tertiary and Quaternary lavas, pyroclastic flows, and fall deposits. The geologic and geomorphologic factors in combination with high seasonal precipitation, high degree of weathering, and steep slopes predispose the study area to landslides. The methodology encompasses three main stages of analysis to assess landslide hazards: Stage 1 builds a historic landslide inventory. In the study area, an inventory of more than 170 landslides is created from multi-temporal aerial-photo-interpretation and local field surveys to assess landslide distribution. All landslides were digitized into a geographic information system (GIS), and a spatial geo-database of landslides was constructed from standardized GIS datasets. Stage 2 Calculates the susceptibility for the watershed. During this stage, Multiple Logistic Regression and SINMAP) will be evaluated to select the one that provides scientific accuracy, technical accessibility, and applicability. Stage 3 Estimate the potential total material delivered to the main stream drainage channel by all landslides in the catchment. Detailed geometric measurements of individual landslides visited during the field work will be carried out to obtain the landslide area and volume. These measurements revealed an empirical relationship between area and volume that took the

Evaluation of landslide susceptibility of Sete Cidades Volcano (S. Miguel Island, Azores

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A. Gomes

2005-01-01

Full Text Available Sete Cidades is an active central volcano with a summit caldera located in the westernmost part of S. Miguel Island (Azores. Since the settlement of the Island, in the 15th century, many landslide events occurred in this volcano, causing extensive damages in buildings and infrastructures. The study of historical records and the observation of new occurrences showed that landslides in the region have been triggered by heavy rainfall periods, earthquakes and erosion. In order to assess landslide susceptibility at Sete Cidades Volcano, landslide scars and associated deposits were mapped through aerial photographs and field surveys. The obtained data were inserted in a GIS to produce a landslide distribution map. It was concluded that the high density landslide areas are related with (1 major scarp faults, (2 the margin of fluvial channels, (3 the sea cliffs and (4 volcanic landforms, namely the caldera wall. About 73% of the mapped events took place in areas where pyroclastic deposits are the dominant lithology and more than 77% occurred where slopes are equal or higher than 20°. These two parameters were integrated and used to generate a preliminary susceptibility map. The incorporation of vulnerability data into the GIS allowed concluding that 30% of dwellings and most of the roads on Sete Cidades Volcano are located in areas where landslide susceptibility is high to very high. Such conclusion should be taken into account for emergency and land use planning.

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

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Saro Lee

2017-01-01

Full Text Available In this study, the support vector machine (SVM was applied and validated by using the geographic information system (GIS in order to map landslide susceptibility. In order to test the usefulness and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis.

A GIS-based susceptibility map for landslides at the Franconian Alb, Germany

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Jaeger, Daniel; Wilde, Martina; Lorenz, Michael; Terhorst, Birgit; Neuhäuser, Bettina; Damm, Bodo; Bemm, Stefan

2014-05-01

In general, slopes of cuesta scarps like the Franconian Alb are highly prone to slide activity due to susceptible geological and geomorphological conditions. The geological setting with alternating permeable and non-permeable bedrock results in the characteristic cuesta landforms of almost flat backslopes and steeper front slopes. Furthermore, this bipartite structure leads to a strong disposition for mass movements. The slopes of the study area near the town of Ebermannstadt are affected by different types of mass movements, such as topples, slides, lateral spreads and flows, either in single or in combined occurrence. In the years 1625, 1957, 1961 and 1979, four large landslides took place in the area of Ebermannstadt, reaching close to the town limits and causing major destructions to traffic facilities. In the study area, slopes are covered by debris and slide masses, thus they are prone to remobilization and further mass movements. In order to assess hazardous areas, a GIS-based susceptibility modelling was generated for the study area. The susceptibtibility modeling was processed with the slope stability model SINMAP (Stability Index Mapping), developed by TARBOTON (1997) and PACK et al. (1999). As SINMAP was particularly designed to model shallow translational slides, it should be well designed for describing the conditions of the study area in a sufficient way. SINMAP is based on the "infinite slope stability model" by HAMMONT et al. (1992) and MONTGOMERY & DIETRICH (1994), which focuses on the relation of stabilizing (cohesiveness, friction angle) and destabilizing (gravitation) factors on a plain surface. By adding a slope gradient, as well as soil mechanical and climatical data, indices of slope stabilities are calculated. For a more detailed modeling of the slope conditions, SINMAP computes different "calibration regions", which merge similar parameters of soil, land-use, vegetation, and geology. Due to the fact that vegetation, land-use, and soils only

Using online database for landslide susceptibility assessment with an example from the Veneto Region (north-eastern Italy).

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Floris, Mario; Squarzoni, Cristina; Zorzi, Luca; D'Alpaos, Andrea; Iafelice, Maria

2010-05-01

Landslide susceptibility maps describe landslide-prone areas by the spatial correlation between landslides and related factors, derived from different kinds of datasets: geological, geotechnical and geomechanical maps, hydrogeological maps, landslides maps, vector and raster terrain data, real-time inclinometer and pore pressure data. In the last decade, thanks to the increasing use of web-based tools for management, sharing and communication of territorial information, many Web-based Geographical Information Systems (WebGIS) were created by local governments or nations, University and Research Centres. Nowadays there is a strong proliferation of geological WebGIS or GeoBrowser, allowing free download of spatial information. There are global Cartographical Portals that provide a free download of DTM and other vector data related to the whole planet (http://www.webgis.com). At major scale, there are WebGIS regarding entire nation (http://www.agiweb.org), or specific region of a country (http://www.mrt.tas.gov.au), or single municipality (http://sitn.ne.ch/). Moreover, portals managed by local government and academic government (http://turtle.ags.gov.ab.ca/Peace_River/Site/) or by a private agency (http://www.bbt-se.com) are noteworthy. In Italy, the first national projects for the creation of WebGIS and web-based databases begun during the 1980s, and evolved, through years, to the present number of different WebGIS, which have different territorial extensions: national (Italian National Cartographical Portal, http://www.pcn.minambiente.it; E-GEO Project, http://www.egeo.unisi.it), interregional (River Tiber Basin Authority, www.abtevere.it ), and regional (Veneto Region, www.regione.veneto.it). In this way we investigated most of the Italian WebGIS in order to verify their geographic range and the availability and quality of data useful for landslide hazard analyses. We noticed a large variability of the accessing information among the different browsers. In

A GIS-based statistical model for rapid landslide susceptibility mapping in the Beichuan-Pingwu area, Sichuan, China

International Nuclear Information System (INIS)

Chen, Y; Wang, Q J

2014-01-01

The 2008 Wenchuan earthquake, with a magnitude of Mw 8.0, induced numerous landslides. Remote sensing planes were sent out to take high resolution aerial photographs, from which the geologic hazards could be instantly interpreted. However, aerial images covering all of the study area could not be obtained in a short time because of the limitations of the planes and the influence of weather conditions. This studyestablishes a statistical model based on the landslide interpretation results of one photographic strip inside the Beichuan-Pingwu area. It has strong applicability and can be applied to other places without such data. Finally, we produced a landslide susceptibility map, which providesscientific support for the instant evaluation of disaster information and post-disaster reconstruction

GIS-based debris flow source and runout susceptibility assessment from DEM data – a case study in NW Nicaragua

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J. M. Vilaplana

2007-11-01

Full Text Available In October 1998, Hurricane Mitch triggered numerous landslides (mainly debris flows in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. The potential application of relatively simple and affordable spatial prediction models for landslide hazard mapping in developing countries was studied. Our attention was focused on a region in NW Nicaragua, one of the most severely hit places during the Mitch event. A landslide map was obtained at 1:10 000 scale in a Geographic Information System (GIS environment from the interpretation of aerial photographs and detailed field work. In this map the terrain failure zones were distinguished from the areas within the reach of the mobilized materials. A Digital Elevation Model (DEM with 20 m×20 m of pixel size was also employed in the study area. A comparative analysis of the terrain failures caused by Hurricane Mitch and a selection of 4 terrain factors extracted from the DEM which, contributed to the terrain instability, was carried out. Land propensity to failure was determined with the aid of a bivariate analysis and GIS tools in a terrain failure susceptibility map. In order to estimate the areas that could be affected by the path or deposition of the mobilized materials, we considered the fact that under intense rainfall events debris flows tend to travel long distances following the maximum slope and merging with the drainage network. Using the TauDEM extension for ArcGIS software we generated automatically flow lines following the maximum slope in the DEM starting from the areas prone to failure in the terrain failure susceptibility map. The areas crossed by the flow lines from each terrain failure susceptibility class correspond to the runout susceptibility classes represented in a runout susceptibility map. The study of terrain failure and runout susceptibility enabled us to obtain a spatial prediction for landslides, which could contribute to landslide risk

Non-susceptible landslide areas in Italy and in the Mediterranean region

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Marchesini, I.; Ardizzone, F.; Alvioli, M.; Rossi, M.; Guzzetti, F.

2014-08-01

We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3-arcseconds shuttle radar topography mission digital elevation model (SRTM DEM) to determine areas where landslide susceptibility is expected to be negligible in Italy and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted of the local terrain slope which was computed in a square 3 × 3-cell moving window, and in the regional relative relief computed in a circular 15 × 15-cell moving window. We tested three different models to classify the "non-susceptible" landslide areas, including a linear model (LNR), a quantile linear model (QLR), and a quantile, non-linear model (QNL). We tested the performance of the three models using independent landslide information presented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia - IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a geographic information system (GIS) with geographical census data for Italy. The result determined that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be negligible. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results showed that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the validation areas in Spain. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible landslide areas, at the synoptic scale.

Modeling Typhoon Event-Induced Landslides Using GIS-Based Logistic Regression: A Case Study of Alishan Forestry Railway, Taiwan

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Sheng-Chuan Chen

2013-01-01

Full Text Available This study develops a model for evaluating the hazard level of landslides at Alishan Forestry Railway, Taiwan, by using logistic regression with the assistance of a geographical information system (GIS. A typhoon event-induced landslide inventory, independent variables, and a triggering factor were used to build the model. The environmental factors such as bedrock lithology from the geology database; topographic aspect, terrain roughness, profile curvature, and distance to river, from the topographic database; and the vegetation index value from SPOT 4 satellite images were used as variables that influence landslide occurrence. The area under curve (AUC of a receiver operator characteristic (ROC curve was used to validate the model. Effects of parameters on landslide occurrence were assessed from the corresponding coefficient that appears in the logistic regression function. Thereafter, the model was applied to predict the probability of landslides for rainfall data of different return periods. Using a predicted map of probability, the study area was classified into four ranks of landslide susceptibility: low, medium, high, and very high. As a result, most high susceptibility areas are located on the western portion of the study area. Several train stations and railways are located on sites with a high susceptibility ranking.

Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression

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García-Rodríguez, M. J.; Malpica, J. A.; Benito, B.; Díaz, M.

2008-03-01

This work has evaluated the probability of earthquake-triggered landslide occurrence in the whole of El Salvador, with a Geographic Information System (GIS) and a logistic regression model. Slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness are the predictor variables used to determine the dependent variable of occurrence or non-occurrence of landslides within an individual grid cell. The results illustrate the importance of terrain roughness and soil type as key factors within the model — using only these two variables the analysis returned a significance level of 89.4%. The results obtained from the model within the GIS were then used to produce a map of relative landslide susceptibility.

Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility

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Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.

2013-03-01

In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100-150 m with slope angles from 135°-225° and 40°-60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence

Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

International Nuclear Information System (INIS)

Althuwaynee, Omar F; Pradhan, Biswajeet; Ahmad, Noordin

2014-01-01

This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies

Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

Science.gov (United States)

Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

2014-06-01

This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

Application of Physically based landslide susceptibility models in Brazil

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Carvalho Vieira, Bianca; Martins, Tiago D.

2017-04-01

Shallow landslides and floods are the processes responsible for most material and environmental damages in Brazil. In the last decades, some landslides events induce a high number of deaths (e.g. Over 1000 deaths in one event) and incalculable social and economic losses. Therefore, the prediction of those processes is considered an important tool for land use planning tools. Among different methods the physically based landslide susceptibility models having been widely used in many countries, but in Brazil it is still incipient when compared to other ones, like statistical tools and frequency analyses. Thus, the main objective of this research was to assess the application of some Physically based landslide susceptibility models in Brazil, identifying their main results, the efficiency of susceptibility mapping, parameters used and limitations of the tropical humid environment. In order to achieve that, it was evaluated SHALSTAB, SINMAP and TRIGRS models in some studies in Brazil along with the Geotechnical values, scales, DEM grid resolution and the results based on the analysis of the agreement between predicted susceptibility and the landslide scar's map. Most of the studies in Brazil applied SHALSTAB, SINMAP and to a lesser extent the TRIGRS model. The majority researches are concentrated in the Serra do Mar mountain range, that is a system of escarpments and rugged mountains that extends more than 1,500 km along the southern and southeastern Brazilian coast, and regularly affected by heavy rainfall that generates widespread mass movements. Most part of these studies used conventional topographic maps with scales ranging from 1:2000 to 1:50000 and DEM-grid resolution between 2 and 20m. Regarding the Geotechnical and hydrological values, a few studies use field collected data which could produce more efficient results, as indicated by international literature. Therefore, even though they have enormous potential in the susceptibility mapping, even for comparison

Multidisciplinary approach to evaluate landslide susceptibility along highway in northern Calabria, Italy

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Muto, Francesco; Conforti, Massimo; Critelli, Salvatore; Fabbricatore, Davide; Filomena, Luciana; Rago, Valeria; Robustelli, Gaetano; Scarciglia, Fabio; Versace, Pasquale

2014-05-01

The interaction of landslides with linear infrastructures is often the cause of disasters. In Italy landslide impact on roads, railways and buildings cause millions of Euro per year in damage and restoration as well. The proposed study is aimed to the landslide susceptibility evaluation using a multidisciplinary approach: geological and geomorphological survey, statistical analysis and GIS technique, along a section of highway "A3 (Salerno-Reggio Calabria)" between Cosenza Sud and Altilia, northern Calabria. This study is included in a wider research project, named: PON01-01503, Landslides Early Warning-Sistemi integrati per il monitoraggio e la mitigazione del rischio idrogeologico lungo le grandi vie di comunicazione - aimed at the hydrogeological risk mitigation and at the early warning along the highways. The work was first based on air-photo interpretations and field investigations, in order to realize the geological map, geomorphological map and landslide inventory map. In the study area the geomorphology is strongly controlled by its bedrock geology and tectonics. The bedrock geology consists of Neogene sedimentary rocks that cover a thick stack of allochthonous nappes. These nappes consist of crystalline rocks mainly gneiss, phyllite and schist. A total of 835 landslides were mapped and the type of movement are represented mainly by slides and complex and subordinately flow. In order to estimate and validate landslide susceptibility the landslides were divided in two group. One group (training set) was used to prepare susceptibility map and the second group (validation set) to validate the map. Then, the selection of predisposing factors was performed, according with the geological and geomorphological settings of the study area: lithology, distance from tectonic elements, land use, slope, aspect, stream power index (SPI) and plan curvature. In order to evaluate landslide susceptibility Conditional Analysis was applied to Unique Conditions Units (UCUs

Una metodologia GIS per la valutazione della suscettibilità da frana

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Leoni Gabriele

2010-03-01

Full Text Available GIS methodology to assess landslide susceptibility: application to a river catchment of central ItalyThis paper illustrates a GIS supported methodology for the assessment of landslide susceptibility. The methodology involves four steps: survey, site analysis, macro-area analysis, and susceptibility analysis. Statiscal and GIS processing of basical large scale geological dataset leads to the recognition of discriminating parameters (land conditions necessary but not sufficient to trigger landslides and predisposing factors (conditions that worsen slope stability separately for each landslides types. The susceptibility function combines GIS data to draw landslide susceptibility maps. These results represent the preliminary step for the assessment of landslide hazard and risk.

Landslide Spatial Distribution Analysis Using GIS. Case Study Secașelor Plateau.

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Gheorghe Rosian

2017-05-01

Full Text Available Landslides represent an extremely frequent geomorphological phenomenon in the Secașelor Plateau. The regional unit is located in the South-Eastern part of the Transylvanian Basin (large basin within the Carpathian Mountains. In this paper, we analyzed the distribution of the landslides through spatial statistics techniques and GIS. In order to analyze the distribution of the landslides we took into consideration 5 criteria: geology, height, slope, exposition and the territorial administrative units. This type of study is necessary to find out the way in which the actual landslides are distributed and on the other hand, the research will collect information on the susceptible fields which are favored by these geomorphological processes. After the visual analysis of the area using the 1:5000 aerial photography and topographic maps, 835 landslides were identified and vectorized. At the level of administrative-territorial units, these cover mostly agricultural areas. Given the lithological conditions (the presence of friable rocks of marl, clay and poorly cemented sands and the land use (mostly agricultural it can be said that in the future new landslides will ocure in similar conditions of slope, exposition and geological characteristic etc. The identification of areas that are susceptible to landslides is beneficial for the future territorial planning actions and also to avoid building on areas which are prone to landslides.

Comparison of the landslide susceptibility models in Taipei Water Source Domain, Taiwan

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WU, C. Y.; Yeh, Y. C.; Chou, T. H.

2017-12-01

Taipei Water Source Domain, locating at the southeast of Taipei Metropolis, is the main source of water resource in this region. Recently, the downstream turbidity often soared significantly during the typhoon period because of the upstream landslides. The landslide susceptibilities should be analysed to assess the influence zones caused by different rainfall events, and to ensure the abilities of this domain to serve enough and quality water resource. Generally, the landslide susceptibility models can be established based on either a long-term landslide inventory or a specified landslide event. Sometimes, there is no long-term landslide inventory in some areas. Thus, the event-based landslide susceptibility models are established widely. However, the inventory-based and event-based landslide susceptibility models may result in dissimilar susceptibility maps in the same area. So the purposes of this study were to compare the landslide susceptibility maps derived from the inventory-based and event-based models, and to interpret how to select a representative event to be included in the susceptibility model. The landslide inventory from Typhoon Tim in July, 1994 and Typhoon Soudelor in August, 2015 was collected, and used to establish the inventory-based landslide susceptibility model. The landslides caused by Typhoon Nari and rainfall data were used to establish the event-based model. The results indicated the high susceptibility slope-units were located at middle upstream Nan-Shih Stream basin.

ANFIS modeling for the assessment of landslide susceptibility for the Cameron Highland (Malaysia)

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Pradhan, Biswajeet; Sezer, Ebru; Gokceoglu, Candan; Buchroithner, Manfred F.

2010-05-01

Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. In landslide literature, there are several approaches such as probabilistic, bivariate and multivariate statistical models, fuzzy and artificial neural network models etc. However, a neuro-fuzzy application on the landslide susceptibility assessment has not been encountered in the literature. For this reason, this study presents the results of an adaptive neuro-fuzzy inference system (ANFIS) using remote sensing data and GIS for landslide susceptibility analysis in a part of the Cameron Highland areas in Malaysia. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments, NDVI and land cover were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, curvature, distance from drainage, lithology, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to produce the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were constructed. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values were calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 97% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary land

An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method

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Suchita Shrestha

2017-11-01

Full Text Available The Mw 7.8 Gorkha earthquake of 25 April 2015 triggered thousands of landslides in the central part of the Nepal Himalayas. The main goal of this study was to generate an ensemble-based map of co-seismic landslide susceptibility in Sindhupalchowk District using model comparison and combination strands. A total of 2194 co-seismic landslides were identified and were randomly split into 1536 (~70%, to train data for establishing the model, and the remaining 658 (~30% for the validation of the model. Frequency ratio, evidential belief function, and weight of evidence methods were applied and compared using 11 different causative factors (peak ground acceleration, epicenter proximity, fault proximity, geology, elevation, slope, plan curvature, internal relief, drainage proximity, stream power index, and topographic wetness index to prepare the landslide susceptibility map. An ensemble of random forest was then used to overcome the various prediction limitations of the individual models. The success rates and prediction capabilities were critically compared using the area under the curve (AUC of the receiver operating characteristic curve (ROC. By synthesizing the results of the various models into a single score, the ensemble model improved accuracy and provided considerably more realistic prediction capacities (91% than the frequency ratio (81.2%, evidential belief function (83.5% methods, and weight of evidence (80.1%.

A landslide susceptibility map of Africa

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Broeckx, Jente; Vanmaercke, Matthias; Duchateau, Rica; Poesen, Jean

2017-04-01

Studies on landslide risks and fatalities indicate that landslides are a global threat to humans, infrastructure and the environment, certainly in Africa. Nonetheless our understanding of the spatial patterns of landslides and rockfalls on this continent is very limited. Also in global landslide susceptibility maps, Africa is mostly underrepresented in the inventories used to construct these maps. As a result, predicted landslide susceptibilities remain subject to very large uncertainties. This research aims to produce a first continent-wide landslide susceptibility map for Africa, calibrated with a well-distributed landslide dataset. As a first step, we compiled all available landslide inventories for Africa. This data was supplemented by additional landslide mapping with Google Earth in underrepresented regions. This way, we compiled 60 landslide inventories from the literature (ca. 11000 landslides) and an additional 6500 landslides through mapping in Google Earth (including 1500 rockfalls). Various environmental variables such as slope, lithology, soil characteristics, land use, precipitation and seismic activity, were investigated for their significance in explaining the observed spatial patterns of landslides. To account for potential mapping biases in our dataset, we used Monte Carlo simulations that selected different subsets of mapped landslides, tested the significance of the considered environmental variables and evaluated the performance of the fitted multiple logistic regression model against another subset of mapped landslides. Based on these analyses, we constructed two landslide susceptibility maps for Africa: one for all landslide types and one excluding rockfalls. In both maps, topography, lithology and seismic activity were the most significant variables. The latter factor may be surprising, given the overall limited degree of seismicity in Africa. However, its significance indicates that frequent seismic events may serve as in important

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

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Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

Landslide susceptibility mapping using logistic statistical regression in Babaheydar Watershed, Chaharmahal Va Bakhtiari Province, Iran

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Ebrahim Karimi Sangchini

2015-01-01

Full Text Available Landslides are amongst the most damaging natural hazards in mountainous regions. Every year, hundreds of people all over the world lose their lives in landslides; furthermore, there are large impacts on the local and global economy from these events. In this study, landslide hazard zonation in Babaheydar watershed using logistic regression was conducted to determine landslide hazard areas. At first, the landslide inventory map was prepared using aerial photograph interpretations and field surveys. The next step, ten landslide conditioning factors such as altitude, slope percentage, slope aspect, lithology, distance from faults, rivers, settlement and roads, land use, and precipitation were chosen as effective factors on landsliding in the study area. Subsequently, landslide susceptibility map was constructed using the logistic regression model in Geographic Information System (GIS. The ROC and Pseudo-R2 indexes were used for model assessment. Results showed that the logistic regression model provided slightly high prediction accuracy of landslide susceptibility maps in the Babaheydar Watershed with ROC equal to 0.876. Furthermore, the results revealed that about 44% of the watershed areas were located in high and very high hazard classes. The resultant landslide susceptibility maps can be useful in appropriate watershed management practices and for sustainable development in the region.

A GIS-based numerical simulation of the March 2014 Oso landslide fluidized motion

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Fukuoka, H.; Ogbonnaya, I.; Wang, C.

2014-12-01

Sliding and flowing are the major movement type after slope failures. Landslides occur when slope-froming material moves downhill after failing along a sliding surface. Most debris flows originally occur in the form of rainfall-induced landslides before they move into valley channel. Landslides that mobilize into debris flows usually are characterized by high-speed movement and long run-out distance and may present the greatest risk to human life. The 22 March 2014 Oso landslide is a typical case of landside transformint to debris flow. The landslide was triggered on the edge of a plateau about 200 m high composed of glacial sediments after excessive prolonged rainfall of 348 in March 2014. After its initiation, portions of the landslide materials transitioned into a rapidly moving debris flow which traveled long distances across the downslope floodplain. U.S. Geological Survey estimated the volume of the slide to be about 7 million m3, and it traveled about 1 km from the toe of the slope. The apparent friction angle measured by the energy line drawn from the crown of the head scarp to the toe of the deposits which reached largest distance, was only 5~6 degrees. we performed two numerical modeling to predicting the runout distance and to get insight into the behaviour of the landslide movement. One is GIS-based revised Hovland's 3D limit equilibrium model which is used to simulate the movement and stoppage of a landslide. In this research, sliding is defined by a slip surface which cuts through the slope, causing the mass of earth to move above it. The factor of safety will be calculated step by step during the sliding process simulation. Stoppage is defined by the factor of safety much greater than one and the velocity equal zero. The other is GIS-based depth-averaged 2D numerical model using a coupled viscous and Coulomb type law to simulate a debris flow from initiation to deposition. We compared our simulaiton results with the results of preliminary computer

Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain

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D. Costanzo

2012-02-01

Full Text Available A procedure to select the controlling factors connected to the slope instability has been defined. It allowed us to assess the landslide susceptibility in the Rio Beiro basin (about 10 km² over the northeastern area of the city of Granada (Spain. Field and remote (Google EarthTM recognition techniques allowed us to generate a landslide inventory consisting in 127 phenomena. To discriminate between stable and unstable conditions, a diagnostic area had been chosen as the one limited to the crown and the toe of the scarp of the landslide. 15 controlling or determining factors have been defined considering topographic, geologic, geomorphologic and pedologic available data. Univariate tests, using both association coefficients and validation results of single-variable susceptibility models, allowed us to select the best predictors, which were combined for the unique conditions analysis. For each of the five recognised landslide typologies, susceptibility maps for the best models were prepared. In order to verify both the goodness of fit and the prediction skill of the susceptibility models, two different validation procedures were applied and compared. Both procedures are based on a random partition of the landslide archive for producing a test and a training subset. The first method is based on the analysis of the shape of the success and prediction rate curves, which are quantitatively analysed exploiting two morphometric indexes. The second method is based on the analysis of the degree of fit, by considering the relative error between the intersected target landslides by each of the different susceptibility classes in which the study area was partitioned. Both the validation procedures confirmed a very good predictive performance of the susceptibility models and of the actual procedure followed to select the controlling factors.

Comparison and validation of shallow landslides susceptibility maps generated by bi-variate and multi-variate linear probabilistic GIS-based techniques. A case study from Ribeira Quente Valley (S. Miguel Island, Azores)

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Marques, R.; Amaral, P.; Zêzere, J. L.; Queiroz, G.; Goulart, C.

2009-04-01

Slope instability research and susceptibility mapping is a fundamental component of hazard assessment and is of extreme importance for risk mitigation, land-use management and emergency planning. Landslide susceptibility zonation has been actively pursued during the last two decades and several methodologies are still being improved. Among all the methods presented in the literature, indirect quantitative probabilistic methods have been extensively used. In this work different linear probabilistic methods, both bi-variate and multi-variate (Informative Value, Fuzzy Logic, Weights of Evidence and Logistic Regression), were used for the computation of the spatial probability of landslide occurrence, using the pixel as mapping unit. The methods used are based on linear relationships between landslides and 9 considered conditioning factors (altimetry, slope angle, exposition, curvature, distance to streams, wetness index, contribution area, lithology and land-use). It was assumed that future landslides will be conditioned by the same factors as past landslides in the study area. The presented work was developed for Ribeira Quente Valley (S. Miguel Island, Azores), a study area of 9,5 km2, mainly composed of volcanic deposits (ash and pumice lapilli) produced by explosive eruptions in Furnas Volcano. This materials associated to the steepness of the slopes (38,9% of the area has slope angles higher than 35°, reaching a maximum of 87,5°), make the area very prone to landslide activity. A total of 1.495 shallow landslides were mapped (at 1:5.000 scale) and included in a GIS database. The total affected area is 401.744 m2 (4,5% of the study area). Most slope movements are translational slides frequently evolving into debris-flows. The landslides are elongated, with maximum length generally equivalent to the slope extent, and their width normally does not exceed 25 m. The failure depth rarely exceeds 1,5 m and the volume is usually smaller than 700 m3. For modelling

Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

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Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

2017-10-01

In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

Landslides susceptibility mapping at Gunung Ciremai National Park

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Faizin; Nur, Bambang Azis

2018-02-01

In addition to agriculture, tourism became one of primary economic income for communities around Mount Ciremai, West, Java. Unfortunately, the landscape of West Java has many potential causes to disasters, mainly landslides. Mapping of disaster susceptibility area is needed as a consideration of tourism planning. The study was conducted in Gunung Ciremai National Park, West Java. This paper propose a methodology to map landslides susceptibilities based on spatial data. Using Geographic Information System tools, several environmental parameters such as slope, land use, elevation, and lithology are scored to build a landslide susceptibility map. Then, susceptibility map is overlaid with Utilization Zone.

Landslide susceptibility and risk assessment: specificities for road networks

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Pellicani, Roberta; Argentiero, Ilenia; Parisi, Alessandro; Spilotro, Giuseppe

2017-04-01

A regional-scale assessment of landslide susceptibility and risk along the main road corridors crossing the provincial territory of Matera (Basilicata Region, Southern Italy) was carried out. The entire provincial road network extends for about 1,320 km through a territory, of which represents the main connection infrastructure among thirty-one municipalities due to the lack of an efficient integrated transportation system through the whole regional territory. For this reason, the strategic importance of these roads consists in their uniqueness in connecting every urban center with the socio-economic surrounding context. These roads and their vehicular traffic are continuously exposed to instability processes (about the 40% of the total length is disrupted by landslides), characterized both by high intensity and low frequency and by low intensity and high frequency. This last typology, consisting in small shallow landslides, is particularly hazardous for the roads since it is widespread along the road network, its occurrence is connected to rainfalls and determines high vulnerability conditions for the road in terms of interruption of vehicular traffic. A GIS-based heuristic-bivariate statistical predictive model was performed to assess and map the landslide susceptibility in the study area, by using a polynomial function of eight predisposing factors, weighted according to their influence on the landslide phenomena, recognized and collected in an inventory. Susceptibility associated to small shallow phenomena was assessed by using a polynomial function of specific factors, such as slope angle and aspect, lithological outcrops, rainfalls, etc. In absence of detailed input data, the spatial distribution of landslide risk along the road corridors was assessed and mapped using a qualitative hazard-consequence matrix approach, by which risk is obtained by combining hazard categories with consequence classes pairwise in a two-dimensional table or matrix. Landslide

A GRASS GIS Semi-Stochastic Model for Evaluating the Probability of Landslides Impacting Road Networks in Collazzone, Central Italy

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Taylor, Faith E.; Santangelo, Michele; Marchesini, Ivan; Malamud, Bruce D.

2013-04-01

During a landslide triggering event, the tens to thousands of landslides resulting from the trigger (e.g., earthquake, heavy rainfall) may block a number of sections of the road network, posing a risk to rescue efforts, logistics and accessibility to a region. Here, we present initial results from a semi-stochastic model we are developing to evaluate the probability of landslides intersecting a road network and the network-accessibility implications of this across a region. This was performed in the open source GRASS GIS software, where we took 'model' landslides and dropped them on a 79 km2 test area region in Collazzone, Umbria, Central Italy, with a given road network (major and minor roads, 404 km in length) and already determined landslide susceptibilities. Landslide areas (AL) were randomly selected from a three-parameter inverse gamma probability density function, consisting of a power-law decay of about -2.4 for medium and large values of AL and an exponential rollover for small values of AL; the rollover (maximum probability) occurs at about AL = 400 m.2 The number of landslide areas selected for each triggered event iteration was chosen to have an average density of 1 landslide km-2, i.e. 79 landslide areas chosen randomly for each iteration. Landslides were then 'dropped' over the region semi-stochastically: (i) random points were generated across the study region; (ii) based on the landslide susceptibility map, points were accepted/rejected based on the probability of a landslide occurring at that location. After a point was accepted, it was assigned a landslide area (AL) and length to width ratio. Landslide intersections with roads were then assessed and indices such as the location, number and size of road blockage recorded. The GRASS-GIS model was performed 1000 times in a Monte-Carlo type simulation. Initial results show that for a landslide triggering event of 1 landslide km-2 over a 79 km2 region with 404 km of road, the number of road blockages

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

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Fei Wang

2017-06-01

Full Text Available The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM, and the weight of each factor was acquired through an analytic hierarchy process (AHP. The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.

Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains

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Jacobs, Liesbet; Dewitte, Olivier; Poesen, Jean; Sekajugo, John; Nobile, Adriano; Rossi, Mauro; Thiery, Wim; Kervyn, Matthieu

2018-01-01

The inhabited zone of the Ugandan Rwenzori Mountains is affected by landslides, frequently causing loss of life, damage to infrastructure and loss of livelihood. This area of ca. 1230 km2 is characterized by contrasting geomorphologic, climatic and lithological patterns, resulting in different landslide types. In this study, the spatial pattern of landslide susceptibility is investigated based on an extensive field inventory constructed for five representative areas within the region (153 km2) and containing over 450 landslides. To achieve a reliable susceptibility assessment, the effects of (1) using different topographic data sources and spatial resolutions and (2) changing the scale of assessment by comparing local and regional susceptibility models on the susceptibility model performances are investigated using a pixel-based logistic regression approach. Topographic data are extracted from different digital elevation models (DEMs) based on radar interferometry (SRTM and TanDEM-X) and optical stereophotogrammetry (ASTER DEM). Susceptibility models using the radar-based DEMs tend to outperform the ones using the ASTER DEM. The model spatial resolution is varied between 10, 20, 30 and 90 m. The optimal resolution depends on the location of the investigated area within the region but the lowest model resolution (90 m) rarely yields the best model performances while the highest model resolution (10 m) never results in significant increases in performance compared to the 20 m resolution. Models built for the local case studies generally have similar or better performances than the regional model and better reflect site-specific controlling factors. At the regional level the effect of distinguishing landslide types between shallow and deep-seated landslides is investigated. The separation of landslide types allows us to improve model performances for the prediction of deep-seated landslides and to better understand factors influencing the occurrence of shallow

Evaluating performance of simplified physically based models for shallow landslide susceptibility

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G. Formetta

2016-11-01

Full Text Available Rainfall-induced shallow landslides can lead to loss of life and significant damage to private and public properties, transportation systems, etc. Predicting locations that might be susceptible to shallow landslides is a complex task and involves many disciplines: hydrology, geotechnical science, geology, hydrogeology, geomorphology, and statistics. Two main approaches are commonly used: statistical or physically based models. Reliable model applications involve automatic parameter calibration, objective quantification of the quality of susceptibility maps, and model sensitivity analyses. This paper presents a methodology to systemically and objectively calibrate, verify, and compare different models and model performance indicators in order to identify and select the models whose behavior is the most reliable for particular case studies.The procedure was implemented in a package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslide susceptibility analysis (M1, M2, and M3 and a component for model verification. It computes eight goodness-of-fit indices by comparing pixel-by-pixel model results and measurement data. The integration of the package in NewAge-JGrass uses other components, such as geographic information system tools, to manage input–output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy along the Salerno–Reggio Calabria highway, between Cosenza and Altilia. The area is extensively subject to rainfall-induced shallow landslides mainly because of its complex geology and climatology. The analysis was carried out considering all the combinations of the eight optimized indices and the three models. Parameter calibration, verification, and model performance assessment were performed by a comparison with a detailed landslide

Object-based Classification for Detecting Landslides and Stochastic Procedure to landslide susceptibility maps - A Case at Baolai Village, SW Taiwan

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Lin, Ying-Tong; Chang, Kuo-Chen; Yang, Ci-Jian

2017-04-01

As the result of global warming in the past decades, Taiwan has experienced more and more extreme typhoons with hazardous massive landslides. In this study, we use object-oriented analysis method to classify landslide area at Baolai village by using Formosat-2 satellite images. We used for multiresolution segmented to generate the blocks, and used hierarchical logic to classified 5 different kinds of features. After that, classification the landslide into different type of landslide. Beside, we use stochastic procedure to integrate landslide susceptibility maps. This study assumed that in the extreme event, 2009 Typhoon Morakot, which precipitation goes to 1991.5mm in 5 days, and the highest landslide susceptible area. The results show that study area's landslide area was greatly changes, most of landslide was erosion by gully and made dip slope slide, or erosion by the stream, especially at undercut bank. From the landslide susceptibility maps, we know that the old landslide area have high potential to occur landslides in the extreme event. This study demonstrates the changing of landslide area and the landslide susceptible area. Keywords: Formosat-2, object-oriented, segmentation, classification, landslide, Baolai Village, SW Taiwan, FS

Susceptibility and triggering scenarios at a regional scale for shallow landslides

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Gullà, G.; Antronico, L.; Iaquinta, P.; Terranova, O.

2008-07-01

The work aims at identifying susceptible areas and pluviometric triggering scenarios at a regional scale in Calabria (Italy), with reference to shallow landsliding events. The proposed methodology follows a statistical approach and uses a database linked to a GIS that has been created to support the various steps of spatial data management and manipulation. The shallow landslide predisposing factors taken into account are derived from (i) the 40-m digital terrain model of the region, an ˜ 15,075 km 2 extension; (ii) outcropping lithology; (iii) soils; and (iv) land use. More precisely, a map of the slopes has been drawn from the digital terrain model. Two kinds of covers [prevalently coarse-grained (CG cover) or fine-grained (FG cover)] were identified, referring to the geotechnical characteristics of geomaterial covers and to the lithology map; soilscapes were drawn from soil maps; and finally, the land use map was employed without any prior processing. Subsequently, the inventory maps of some shallow landsliding events, totaling more than 30,000 instabilities of the past and detected by field surveys and photo aerial restitution, were employed to calibrate the relative importance of these predisposing factors. The use of single factors (first level analysis) therefore provides three different susceptibility maps. Second level analysis, however, enables better location of areas susceptible to shallow landsliding events by crossing the single susceptibility maps. On the basis of the susceptibility map obtained by the second level analysis, five different classes of susceptibility to shallow landsliding events have been outlined over the regional territory: 8.9% of the regional territory shows very high susceptibility, 14.3% high susceptibility, 15% moderate susceptibility, 3.6% low susceptibility, and finally, about 58% very low susceptibility. Finally, the maps of two significant shallow landsliding events of the past and their related rainfalls have been

Subsurface geological modeling using GIS and remote sensing data: a case study from Platanos landslide, Western Greece

Science.gov (United States)

Kavoura, K.; Kordouli, M.; Nikolakopoulos, K.; Elias, P.; Sykioti, O.; Tsagaris, V.; Drakatos, G.; Rondoyanni, Th.; Tsiambaos, G.; Sabatakakis, N.; Anastasopoulos, V.

2014-08-01

Landslide phenomena constitute a major geological hazard in Greece and especially in the western part of the country as a result of anthropogenic activities, growing urbanization and uncontrolled land - use. More frequent triggering events and increased susceptibility of the ground surface to instabilities as consequence of climate change impacts (continued deforestation mainly due to the devastating forest wildfires and extreme meteorological events) have also increased the landslide risk. The studied landslide occurrence named "Platanos" has been selected within the framework of "Landslide Vulnerability Model - LAVMO" project that aims at creating a persistently updated electronic platform assessing risks related with landslides. It is a coastal area situated between Korinthos and Patras at the northwestern part of the elongated graben of the Corinth Gulf. The paper presents the combined use of geological-geotechnical insitu data, remote sensing data and GIS techniques for the evaluation of a subsurface geological model. High accuracy Digital Surface Model (DSM), airphotos mosaic and satellite data, with a spatial resolution of 0.5m were used for an othophoto base map compilation of the study area. Geological - geotechnical data obtained from exploratory boreholes were digitized and implemented in a GIS platform with engineering geological maps for a three - dimensional subsurface model evaluation. This model is provided for being combined with inclinometer measurements for sliding surface location through the instability zone.

Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study

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Rubini Mahalingam

2016-11-01

Full Text Available Landslides are a significant geohazard, which frequently result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping using GIS and remote sensing can help communities prepare for these damaging events. Current mapping efforts utilize a wide variety of techniques and consider multiple factors. Unfortunately, each study is relatively independent of others in the applied technique and factors considered, resulting in inconsistencies. Further, input data quality often varies in terms of source, data collection, and generation, leading to uncertainty. This paper investigates if lidar-derived data-sets (slope, slope roughness, terrain roughness, stream power index, and compound topographic index can be used for predictive mapping without other landslide conditioning factors. This paper also assesses the differences in landslide susceptibility mapping using several, widely used statistical techniques. Landslide susceptibility maps were produced from the aforementioned lidar-derived data-sets for a small study area in Oregon using six representative statistical techniques. Most notably, results show that only a few factors were necessary to produce satisfactory maps with high predictive capability (area under the curve >0.7. The sole use of lidar digital elevation models and their derivatives can be used for landslide mapping using most statistical techniques without requiring additional detailed data-sets that are often difficult to obtain or of lower quality.

LANDSLIDE SUSCEPTIBILITY ASSESSMENT THROUGH FUZZY LOGIC INFERENCE SYSTEM (FLIS

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T. Bibi

2016-09-01

Full Text Available Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.

Landslide temporal analysis and susceptibility assessment as bases for landslide mitigation, Machu Picchu, Peru

Czech Academy of Sciences Publication Activity Database

Klimeš, Jan

2013-01-01

Roč. 70, č. 2 (2013), s. 913-925 ISSN 1866-6280 Institutional research plan: CEZ:AV0Z30460519 Keywords : landslide inventory * landslide frequency * susceptibility map Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 1.572, year: 2013

Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains

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L. Jacobs

2018-01-01

Full Text Available The inhabited zone of the Ugandan Rwenzori Mountains is affected by landslides, frequently causing loss of life, damage to infrastructure and loss of livelihood. This area of ca. 1230 km2 is characterized by contrasting geomorphologic, climatic and lithological patterns, resulting in different landslide types. In this study, the spatial pattern of landslide susceptibility is investigated based on an extensive field inventory constructed for five representative areas within the region (153 km2 and containing over 450 landslides. To achieve a reliable susceptibility assessment, the effects of (1 using different topographic data sources and spatial resolutions and (2 changing the scale of assessment by comparing local and regional susceptibility models on the susceptibility model performances are investigated using a pixel-based logistic regression approach. Topographic data are extracted from different digital elevation models (DEMs based on radar interferometry (SRTM and TanDEM-X and optical stereophotogrammetry (ASTER DEM. Susceptibility models using the radar-based DEMs tend to outperform the ones using the ASTER DEM. The model spatial resolution is varied between 10, 20, 30 and 90 m. The optimal resolution depends on the location of the investigated area within the region but the lowest model resolution (90 m rarely yields the best model performances while the highest model resolution (10 m never results in significant increases in performance compared to the 20 m resolution. Models built for the local case studies generally have similar or better performances than the regional model and better reflect site-specific controlling factors. At the regional level the effect of distinguishing landslide types between shallow and deep-seated landslides is investigated. The separation of landslide types allows us to improve model performances for the prediction of deep-seated landslides and to better understand factors influencing the

The comparison of landslide ratio-based and general logistic regression landslide susceptibility models in the Chishan watershed after 2009 Typhoon Morakot

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WU, Chunhung

2015-04-01

The research built the original logistic regression landslide susceptibility model (abbreviated as or-LRLSM) and landslide ratio-based ogistic regression landslide susceptibility model (abbreviated as lr-LRLSM), compared the performance and explained the error source of two models. The research assumes that the performance of the logistic regression model can be better if the distribution of landslide ratio and weighted value of each variable is similar. Landslide ratio is the ratio of landslide area to total area in the specific area and an useful index to evaluate the seriousness of landslide disaster in Taiwan. The research adopted the landside inventory induced by 2009 Typhoon Morakot in the Chishan watershed, which was the most serious disaster event in the last decade, in Taiwan. The research adopted the 20 m grid as the basic unit in building the LRLSM, and six variables, including elevation, slope, aspect, geological formation, accumulated rainfall, and bank erosion, were included in the two models. The six variables were divided as continuous variables, including elevation, slope, and accumulated rainfall, and categorical variables, including aspect, geological formation and bank erosion in building the or-LRLSM, while all variables, which were classified based on landslide ratio, were categorical variables in building the lr-LRLSM. Because the count of whole basic unit in the Chishan watershed was too much to calculate by using commercial software, the research took random sampling instead of the whole basic units. The research adopted equal proportions of landslide unit and not landslide unit in logistic regression analysis. The research took 10 times random sampling and selected the group with the best Cox & Snell R2 value and Nagelkerker R2 value as the database for the following analysis. Based on the best result from 10 random sampling groups, the or-LRLSM (lr-LRLSM) is significant at the 1% level with Cox & Snell R2 = 0.190 (0.196) and Nagelkerke R2

Producing landslide susceptibility maps by utilizing machine learning methods. The case of Finikas catchment basin, North Peloponnese, Greece.

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Tsangaratos, Paraskevas; Ilia, Ioanna; Loupasakis, Constantinos; Papadakis, Michalis; Karimalis, Antonios

2017-04-01

The main objective of the present study was to apply two machine learning methods for the production of a landslide susceptibility map in the Finikas catchment basin, located in North Peloponnese, Greece and to compare their results. Specifically, Logistic Regression and Random Forest were utilized, based on a database of 40 sites classified into two categories, non-landslide and landslide areas that were separated into a training dataset (70% of the total data) and a validation dataset (remaining 30%). The identification of the areas was established by analyzing airborne imagery, extensive field investigation and the examination of previous research studies. Six landslide related variables were analyzed, namely: lithology, elevation, slope, aspect, distance to rivers and distance to faults. Within the Finikas catchment basin most of the reported landslides were located along the road network and within the residential complexes, classified as rotational and translational slides, and rockfalls, mainly caused due to the physical conditions and the general geotechnical behavior of the geological formation that cover the area. Each landslide susceptibility map was reclassified by applying the Geometric Interval classification technique into five classes, namely: very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility, and very high susceptibility. The comparison and validation of the outcomes of each model were achieved using statistical evaluation measures, the receiving operating characteristic and the area under the success and predictive rate curves. The computation process was carried out using RStudio an integrated development environment for R language and ArcGIS 10.1 for compiling the data and producing the landslide susceptibility maps. From the outcomes of the Logistic Regression analysis it was induced that the highest b coefficient is allocated to lithology and slope, which was 2.8423 and 1.5841, respectively. From the

Susceptibility of Shallow Landslide in Fraser Hill Catchment, Pahang Malaysia

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Wan Nor Azmin Sulaiman

2010-01-01

Full Text Available In tropical areas especially during monsoon seasons intense precipitation is the main caused that trigger the natural shallow landslide phenomena. This phenomenon can be disastrous and widespread in occurrence even in undisturbed forested catchment. In this paper, an attempt has been made to evaluate the susceptibility of natural hill slopes to failure for a popular hill resort area, the Fraser Hill Catchment under different rainfall regimes and soil thickness. A Digital Elevation Model (DEM was prepared for the 8.2 km2 catchment. A GIS based deterministic model was then applied to predict the spatial landslide occurrence within catchment. Model input parameters include bulk density, friction angle, cohesion and hydraulic conductivity were gathered through in situ and lab analysis as well as from previous soil analysis records. Landslides locations were recorded using GPS as well as previous air photos and satellite imagery to establish landslide source areas inventory. The landslide susceptibility map was produced under different precipitation event’s simulation to see the effects of precipitation to stability of the hill slopes of the catchment. The results were categorized into naturally unstable (Defended, Upper Threshold, Lower Threshold, marginal instability (Quasi Stable and stable area (Moderately Stable and Stable. Results of the simulation indicated notable change in precipitation effect on Defended area is between 10mm to 40mm range in a single storm event. However, when storm event is exceeded 120mm, the result on Defended area produced by the model tends to be constant further on. For area categorized as naturally unstable (Factor of Safety, SF<1, with 110 mm of precipitation in a single storm event and soil depth at 2 meters and 4 meters could affect 69.51% and 69.88% respectively of the catchment area fall under that class. In addition, the model was able to detect 4% more of the landslide inventory under shallower soil depth of

TRIGRS Application for landslide susceptibility mapping

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Sugiarti, K.; Sukristiyanti, S.

2018-02-01

Research on landslide susceptibility has been carried out using several different methods. TRIGRS is a modeling program for landslide susceptibility by considering pore water pressure changes due to infiltration of rainfall. This paper aims to present a current state-of-the-art science on the development and application of TRIGRS. Some limitations of TRIGRS, some developments of it to improve its modeling capability, and some examples of the applications of some versions of it to model the effect of rainfall variation on landslide susceptibility are reviewed and discussed.

Easy To Use Remote Sensing and GIS Analysis for Landslide Risk Assessment.

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Hayder Dibs

2017-12-01

Full Text Available Many countries throughout the world suffered from the natural risks, they cause a large damage in property and loss in human lives, we cannot prevent the occurring of these hazards but, it is possible to reduce their affect in saving human lives and reducing the damage in properties. Several methodologies have been conducted to predict the suitable model for landslide assessment. The susceptibility maps of landslide hazard generated by combining the remote sensed data with the capability of GIS (geographic information system. We discussed different type of algorithms and factors for modeling the prediction of landslide risk assessment such as SVM (support vector machine, DT (decision tree, ANFIS (adaptive neural-fuzzy inference system, AHP (analytic hierarchy process, ANN (artificial neural network, probability frequency of landslides occurrence factors model and empirical model. The study evaluated various parameters that are responsible for landslide occurrence and the weighting for each parameter and its importance to probable of landslide activity. AHP method, Weights of evidence model, and back propagation method have been applied for weighting the factors.  We found that using ANN algorithm with more than ten factors will give high accuracy result especially if the validation performs by field surveys data.

Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda

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Jean Baptiste Nsengiyumva

2018-01-01

Full Text Available Landslides susceptibility assessment has to be conducted to identify prone areas and guide risk management. Landslides in Rwanda are very deadly disasters. The current research aimed to conduct landslide susceptibility assessment by applying Spatial Multi-Criteria Evaluation Model with eight layers of causal factors including: slope, distance to roads, lithology, precipitation, soil texture, soil depth, altitude and land cover. In total, 980 past landslide locations were mapped. The relationship between landslide factors and inventory map was calculated using the Spatial Multi-Criteria Evaluation. The results revealed that susceptibility is spatially distributed countrywide with 42.3% of the region classified from moderate to very high susceptibility, and this is inhabited by 49.3% of the total population. In addition, Provinces with high to very high susceptibility are West, North and South (40.4%, 22.8% and 21.5%, respectively. Subsequently, the Eastern Province becomes the peak under low susceptibility category (87.8% with no very high susceptibility (0%. Based on these findings, the employed model produced accurate and reliable outcome in terms of susceptibility, since 49.5% of past landslides fell within the very high susceptibility category, which confirms the model’s performance. The outcomes of this study will be useful for future initiatives related to landslide risk reduction and management.

Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda

Science.gov (United States)

Nsengiyumva, Jean Baptiste; Luo, Geping; Nahayo, Lamek; Huang, Xiaotao; Cai, Peng

2018-01-01

Landslides susceptibility assessment has to be conducted to identify prone areas and guide risk management. Landslides in Rwanda are very deadly disasters. The current research aimed to conduct landslide susceptibility assessment by applying Spatial Multi-Criteria Evaluation Model with eight layers of causal factors including: slope, distance to roads, lithology, precipitation, soil texture, soil depth, altitude and land cover. In total, 980 past landslide locations were mapped. The relationship between landslide factors and inventory map was calculated using the Spatial Multi-Criteria Evaluation. The results revealed that susceptibility is spatially distributed countrywide with 42.3% of the region classified from moderate to very high susceptibility, and this is inhabited by 49.3% of the total population. In addition, Provinces with high to very high susceptibility are West, North and South (40.4%, 22.8% and 21.5%, respectively). Subsequently, the Eastern Province becomes the peak under low susceptibility category (87.8%) with no very high susceptibility (0%). Based on these findings, the employed model produced accurate and reliable outcome in terms of susceptibility, since 49.5% of past landslides fell within the very high susceptibility category, which confirms the model’s performance. The outcomes of this study will be useful for future initiatives related to landslide risk reduction and management. PMID:29385096

Using online databases for landslide susceptibility assessment: an example from the Veneto Region (northeastern Italy

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M. Floris

2011-07-01

Full Text Available In this paper, spatial data available in the Italian portals was used to evaluate the landslide susceptibility of the Euganean Hills Regional Park, located SW of Padua (northeastern Italy. Quality, applicability and possible analysis scales of the online data were investigated.

After a brief overview on the WebGIS portals around the world, their contents and tools for natural risk analyses, a susceptibility analysis of the study area was carried out using a simple probabilistic approach that compared landslide distribution and influencing factors. The input factors used in the analysis depended on available data and included landslides, morphometric data (elevation, slope, curvature, profile and plan Curvature and non-morphometric data (land use, distance to roads and distance to rivers. Great attention was paid to the pre-processing step, in particular the re-classification of continuous data that was performed following objective, geologic and geomorphologic criteria.

The results of the study show that the simple probabilistic approach used for the susceptibility evaluation showed quite good accuracy and precision (repeatability. However, heuristic, statistical or deterministic methods could be applied to the online data to improve the prediction.

The data available online for the Italian territory allows susceptibility assessment at medium and large scales. Morphometric factors, such as elevation and slope angle, are important because they implicitly include information that is not available, such as lithologic and structural data. The main drawback of the Italian online databases is the lack of information on the frequency of landslides; thus, a complete hazard analysis is not possible.

Despite the good results achieved to date, collection and sharing of data on natural risks must be improved in Italy and around the world. The creation of spatial data infrastructure and more WebGIS portals is desirable.

Landslide susceptibility map: from research to application

Science.gov (United States)

Fiorucci, Federica; Reichenbach, Paola; Ardizzone, Francesca; Rossi, Mauro; Felicioni, Giulia; Antonini, Guendalina

2014-05-01

Susceptibility map is an important and essential tool in environmental planning, to evaluate landslide hazard and risk and for a correct and responsible management of the territory. Landslide susceptibility is the likelihood of a landslide occurring in an area on the basis of local terrain conditions. Can be expressed as the probability that any given region will be affected by landslides, i.e. an estimate of "where" landslides are likely to occur. In this work we present two examples of landslide susceptibility map prepared for the Umbria Region and for the Perugia Municipality. These two maps were realized following official request from the Regional and Municipal government to the Research Institute for the Hydrogeological Protection (CNR-IRPI). The susceptibility map prepared for the Umbria Region represents the development of previous agreements focused to prepare: i) a landslide inventory map that was included in the Urban Territorial Planning (PUT) and ii) a series of maps for the Regional Plan for Multi-risk Prevention. The activities carried out for the Umbria Region were focused to define and apply methods and techniques for landslide susceptibility zonation. Susceptibility maps were prepared exploiting a multivariate statistical model (linear discriminant analysis) for the five Civil Protection Alert Zones defined in the regional territory. The five resulting maps were tested and validated using the spatial distribution of recent landslide events that occurred in the region. The susceptibility map for the Perugia Municipality was prepared to be integrated as one of the cartographic product in the Municipal development plan (PRG - Piano Regolatore Generale) as required by the existing legislation. At strategic level, one of the main objectives of the PRG, is to establish a framework of knowledge and legal aspects for the management of geo-hydrological risk. At national level most of the susceptibility maps prepared for the PRG, were and still are obtained

Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium

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M. Van Den Eeckhaut

2009-03-01

Full Text Available For a 277 km² study area in the Flemish Ardennes, Belgium, a landslide inventory and two landslide susceptibility zonations were combined to obtain an optimal landslide susceptibility assessment, in five classes. For the experiment, a regional landslide inventory, a 10 m × 10 m digital representation of topography, and lithological and soil hydrological information obtained from 1:50 000 scale maps, were exploited. In the study area, the regional inventory shows 192 landslides of the slide type, including 158 slope failures occurred before 1992 (model calibration set, and 34 failures occurred after 1992 (model validation set. The study area was partitioned in 2.78×10⁶ grid cells and in 1927 topographic units. The latter are hydro-morphological units obtained by subdividing slope units based on terrain gradient. Independent models were prepared for the two terrain subdivisions using discriminant analysis. For grid cells, a single pixel was identified as representative of the landslide depletion area, and geo-environmental information for the pixel was obtained from the thematic maps. The landslide and geo-environmental information was used to model the propensity of the terrain to host landslide source areas. For topographic units, morphologic and hydrologic information and the proportion of lithologic and soil hydrological types in each unit, were used to evaluate landslide susceptibility, including the depletion and depositional areas. Uncertainty associated with the two susceptibility models was evaluated, and the model performance was tested using the independent landslide validation set. An heuristic procedure was adopted to combine the landslide inventory and the susceptibility zonations. The procedure makes optimal use of the available landslide and susceptibility information, minimizing the limitations inherent in the inventory and the susceptibility maps. For the established susceptibility classes, regulations to

Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium

Science.gov (United States)

van den Eeckhaut, M.; Reichenbach, P.; Guzzetti, F.; Rossi, M.; Poesen, J.

2009-03-01

For a 277 km2 study area in the Flemish Ardennes, Belgium, a landslide inventory and two landslide susceptibility zonations were combined to obtain an optimal landslide susceptibility assessment, in five classes. For the experiment, a regional landslide inventory, a 10 m × 10 m digital representation of topography, and lithological and soil hydrological information obtained from 1:50 000 scale maps, were exploited. In the study area, the regional inventory shows 192 landslides of the slide type, including 158 slope failures occurred before 1992 (model calibration set), and 34 failures occurred after 1992 (model validation set). The study area was partitioned in 2.78×106 grid cells and in 1927 topographic units. The latter are hydro-morphological units obtained by subdividing slope units based on terrain gradient. Independent models were prepared for the two terrain subdivisions using discriminant analysis. For grid cells, a single pixel was identified as representative of the landslide depletion area, and geo-environmental information for the pixel was obtained from the thematic maps. The landslide and geo-environmental information was used to model the propensity of the terrain to host landslide source areas. For topographic units, morphologic and hydrologic information and the proportion of lithologic and soil hydrological types in each unit, were used to evaluate landslide susceptibility, including the depletion and depositional areas. Uncertainty associated with the two susceptibility models was evaluated, and the model performance was tested using the independent landslide validation set. An heuristic procedure was adopted to combine the landslide inventory and the susceptibility zonations. The procedure makes optimal use of the available landslide and susceptibility information, minimizing the limitations inherent in the inventory and the susceptibility maps. For the established susceptibility classes, regulations to link terrain domains to appropriate land

Spatial prediction of landslide hazard using discriminant analysis and GIS

Science.gov (United States)

Peter V. Gorsevski; Paul Gessler; Randy B. Foltz

2000-01-01

Environmental attributes relevant for spatial prediction of landslides triggered by rain and snowmelt events were derived from digital elevation model (DEM). Those data in conjunction with statistics and geographic information system (GIS) provided a detailed basis for spatial prediction of landslide hazard. The spatial prediction of landslide hazard in this paper is...

Citizen science, GIS, and the global hunt for landslides

Science.gov (United States)

Juang, C.; Stanley, T.; Kirschbaum, D.

2017-12-01

Landslides occur across the United States and around the world, causing much suffering and infrastructure damage. Many of these events have been recorded in the Global Landslide Catalog (GLC), a worldwide record of recently rainfall-triggered landslides. The extent and composition of this database has been affected by the limits of media search tools and available staffing. Citizen scientists could expand the effort exponentially, as well as diversify the knowledge base of the research team. In order to enable this collaboration the NASA Center for Climate Simulation has created a GIS portal for viewing, editing, and managing the GLC. The data is also exposed through a Rest API, for easy incorporation into geospatial websites by third parties. Future developments may include the ability to store polygons delineating large landslides, digitization from recent satellite imagery, and the establishment of a community for international landslide research that is open to both lay and academic users.

APPLICATION OF LiDAR DATE TO ASSESS THE LANDSLIDE SUSCEPTIBILITY MAP USING WEIGHTS OF EVIDENCE METHOD – AN EXAMPLE FROM PODHALE REGION (SOUTHERN POLAND

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M. Kamiński

2016-06-01

Full Text Available Podhale is a region in southern Poland, which is the northernmost part of the Central Carpathian Mountains. It is characterized by the presence of a large number of landslides that threaten the local infrastructure. In an article presents application of LiDAR data and geostatistical methods to assess landslides susceptibility map. Landslide inventory map were performed using LiDAR data and field work. The Weights of Evidence method was applied to assess landslides susceptibility map. Used factors for modeling: slope gradient, slope aspect, elevation, drainage density, faults density, lithology and curvature. All maps were subdivided into different classes. Then were converted to grid format in the ArcGIS 10.0. The conditional independence test was carried out to determine factors that are conditionally independent of each other with landslides. As a result, chi-square test for further GIS analysis used only five factors: slope gradient, slope aspect, elevation, drainage density and lithology. The final prediction results, it is concluded that the susceptibility map gives useful information both on present instability of the area and its possible future evolution in agreement with the morphological evolution of the area.

Landslides and vegetation cover in the 2005 North Pakistan earthquake: a GIS and statistical quantitative approach

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P. Peduzzi

2010-04-01

Full Text Available The growing concern for loss of services once provided by natural ecosystems is getting increasing attention. However, the accelerating rate of natural resources destruction calls for rapid and global action. With often very limited budgets, environmental agencies and NGOs need cost-efficient ways to quickly convince decision-makers that sound management of natural resources can help to protect human lives and their welfare. The methodology described in this paper, is based on geospatial and statistical analysis, involving simple Geographical Information System (GIS and remote sensing algorithms. It is based on free or very low-cost data. It aims to scientifically assess the potential role of vegetation in mitigating landslides triggered by earthquakes by normalising for other factors such as slopes and distance from active fault. The methodology was applied to the 2005 North Pakistan/India earthquake which generated a large number of victims and hundreds of landslides. The study shows that if slopes and proximity from active fault are the main susceptibility factors for post landslides triggered by earthquakes in this area, the results clearly revealed that areas covered by denser vegetation suffered less and smaller landslides than areas with thinner (or devoid of vegetation cover. Short distance from roads/trails and rivers also proved to be pertinent factors in increasing landslides susceptibility. This project is a component of a wider initiative involving the Global Resource Information Database Europe from the United Nations Environment Programme, the International Union for Conservation of Nature, the Institute of Geomatics and Risk Analysis from the University of Lausanne and the "institut universitaire d'études du développement" from the University of Geneva.

Effects of Inventory Bias on Landslide Susceptibility Calculations

Science.gov (United States)

Stanley, T. A.; Kirschbaum, D. B.

2017-01-01

Many landslide inventories are known to be biased, especially inventories for large regions such as Oregon's SLIDO or NASA's Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modeling landslide susceptibility with heavily biased inventories.

Integration of landslide susceptibility products in the environmental plans

Science.gov (United States)

Fiorucci, Federica; Reichenbach, Paola; Rossi, Mauro; Cardinali, Mauro; Guzzetti, Fausto

2015-04-01

Landslides are one of the most destructive natural hazard that causes damages to urban area worldwide. The knowledge of where a landslide could occur is essential for the strategic management of the territory and for a good urban planning . In this contest landslide susceptibility zoning (LSZ) is crucial to provide information on the degree to which an area can be affected by future slope movements. Despite landslide susceptibility maps have been prepared extensively during the last decades, there are few examples of application is in the environmental plans (EP). In this work we present a proposal for the integration of the landslide inventory map with the following landslide susceptibility products: (i) landslide susceptibility zonation , (ii) the associated error map and (iii) the susceptibility uncertainty map. Moreover we proposed to incorporate detailed morphological studies for the evaluation of landslide risk associated to local parceling plan. The integration of all this information is crucial for the management of landslide risk in urban expansions forecasts. Municipality, province and regional administration are often not able to support the costs of landslide risk evaluation for extensive areas but should concentrate their financial resources to specific hazardous and unsafe situations defined by the result of the integration of landslide susceptibility products. Zonation and detail morphological analysis should be performed taking into account the existing laws and regulations, and could become a starting point to discuss new regulations for the landslide risk management.

Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India)

Science.gov (United States)

Das, Iswar; Sahoo, Sashikant; van Westen, Cees; Stein, Alfred; Hack, Robert

2010-02-01

Landslide studies are commonly guided by ground knowledge and field measurements of rock strength and slope failure criteria. With increasing sophistication of GIS-based statistical methods, however, landslide susceptibility studies benefit from the integration of data collected from various sources and methods at different scales. This study presents a logistic regression method for landslide susceptibility mapping and verifies the result by comparing it with the geotechnical-based slope stability probability classification (SSPC) methodology. The study was carried out in a landslide-prone national highway road section in the northern Himalayas, India. Logistic regression model performance was assessed by the receiver operator characteristics (ROC) curve, showing an area under the curve equal to 0.83. Field validation of the SSPC results showed a correspondence of 72% between the high and very high susceptibility classes with present landslide occurrences. A spatial comparison of the two susceptibility maps revealed the significance of the geotechnical-based SSPC method as 90% of the area classified as high and very high susceptible zones by the logistic regression method corresponds to the high and very high class in the SSPC method. On the other hand, only 34% of the area classified as high and very high by the SSPC method falls in the high and very high classes of the logistic regression method. The underestimation by the logistic regression method can be attributed to the generalisation made by the statistical methods, so that a number of slopes existing in critical equilibrium condition might not be classified as high or very high susceptible zones.

Rainfall induced landslide susceptibility mapping using weight-of-evidence, linear and quadratic discriminant and logistic model tree method

Science.gov (United States)

Hong, H.; Zhu, A. X.

2017-12-01

Climate change is a common phenomenon and it is very serious all over the world. The intensification of rainfall extremes with climate change is of key importance to society and then it may induce a large impact through landslides. This paper presents GIS-based new ensemble data mining techniques that weight-of-evidence, logistic model tree, linear and quadratic discriminant for landslide spatial modelling. This research was applied in Anfu County, which is a landslide-prone area in Jiangxi Province, China. According to a literature review and research the study area, we select the landslide influencing factor and their maps were digitized in a GIS environment. These landslide influencing factors are the altitude, plan curvature, profile curvature, slope degree, slope aspect, topographic wetness index (TWI), Stream Power Index (SPI), Topographic Wetness Index (SPI), distance to faults, distance to rivers, distance to roads, soil, lithology, normalized difference vegetation index and land use. According to historical information of individual landslide events, interpretation of the aerial photographs, and field surveys supported by the government of Jiangxi Meteorological Bureau of China, 367 landslides were identified in the study area. The landslide locations were divided into two subsets, namely, training and validating (70/30), based on a random selection scheme. In this research, Pearson's correlation was used for the evaluation of the relationship between the landslides and influencing factors. In the next step, three data mining techniques combined with the weight-of-evidence, logistic model tree, linear and quadratic discriminant, were used for the landslide spatial modelling and its zonation. Finally, the landslide susceptibility maps produced by the mentioned models were evaluated by the ROC curve. The results showed that the area under the curve (AUC) of all of the models was > 0.80. At the same time, the highest AUC value was for the linear and quadratic

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

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Qiaomei Su

2017-07-01

Full Text Available Landslide susceptibility mapping is the first and most important step involved in landslide hazard assessment. The purpose of the present study is to compare three nonlinear approaches for landslide susceptibility mapping and test whether coal mining has a significant impact on landslide occurrence in coal mine areas. Landslide data collected by the Bureau of Land and Resources are represented by the X, Y coordinates of its central point; causative factors were calculated from topographic and geologic maps, as well as satellite imagery. The five-fold cross-validation method was adopted and the landslide/non-landslide datasets were randomly split into a ratio of 80:20. From this, five subsets for 20 times were acquired for training and validating models by GIS Geostatistical analysis methods, and all of the subsets were employed in a spatially balanced sample design. Three landslide models were built using support vector machine (SVM, logistic regression (LR, and artificial neural network (ANN models by selecting the median of the performance measures. Then, the three fitted models were compared using the area under the receiver operating characteristics (ROC curves (AUC and the performance measures. The results show that the prediction accuracies are between 73.43% and 87.45% in the training stage, and 67.16% to 73.13% in the validating stage for the three models. AUCs vary from 0.807 to 0.906 and 0.753 to 0.944 in the two stages, respectively. Additionally, three landslide susceptibility maps were obtained by classifying the range of landslide probabilities into four classes representing low (0–0.02, medium (0.02–0.1, high (0.1–0.85, and very high (0.85–1 probabilities of landslides. For the distributions of landslide and area percentages under different susceptibility standards, the SVM model has more relative balance in the four classes compared to the LR and the ANN models. The result reveals that the SVM model possesses better

Assessing landslide susceptibility by applying fuzzy sets, possibility evidence-based theories

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Ibsen Chivatá Cárdenas

2008-01-01

Full Text Available A landslide susceptibility model was developed for the city of Manizales, Colombia; landslides have been the city’s main environmental problem. Fuzzy sets and possibility and evidence-based theories were used to construct the mo-del due to the set of circumstances and uncertainty involved in the modelling; uncertainty particularly concerned the lack of representative data and the need for systematically coordinating subjective information. Susceptibility and the uncertainty were estimated via data processing; the model contained data concerning mass vulnerability and uncer-tainty. Output data was expressed on a map defined by linguistic categories or uncertain labels as having low, me-dium, high and very high susceptibility; this was considered appropriate for representing susceptibility. A fuzzy spec-trum was developed for classifying susceptibility levels according to perception and expert opinion. The model sho-wed levels of susceptibility in the study area, ranging from low to high susceptibility (medium susceptibility being mo-re frequent. This article shows the details concerning systematic data processing by presenting theories and tools regarding uncertainty. The concept of fuzzy parameters is introduced; this is useful in modelling phenomena regar-ding uncertainty, complexity and nonlinear performance, showing that susceptibility modelling can be feasible. The paper also shows the great convenience of incorporating uncertainty into modelling and decision-making. However, quantifying susceptibility is not suitable when modelling identified uncertainty because incorporating model output information cannot be reduced into exact or real numerical quantities when the nature of the variables is particularly uncertain. The latter concept is applicable to risk assessment.

Assessment of landslide risk using gis and statistical methods in kysuce region

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Barančoková Mária

2014-03-01

Full Text Available The landslide susceptibility was assessed based on multivariation analysis. The input parameters were represented by lithology, land use, slope inclination and average annual precipitation. These parameters were evaluated as independent variables, and the existing landslides as dependent variables. The individual input parameters were reclassified and spatially adjusted. Spatial analysis resulted in 15 988 combinations of input parameters representing the homogeneous condition unit (HCU . Based on the landslide density within individual units, the HCU polygons have been classified according to landslide risk into stable, conditionally stable, conditionally stable and unstable (subdivided into low, medium and high landslide risk. A total of 2002 HCU s were affected by landslides, and the remaining 13 986 were not affected. The total HCU area affected by landslides is about 156.92 km2 (20.1%. Stable areas covered 623.01 km2 (79.8%, and conditionally stable areas covered 228.77 km2 (29.33% out of this area. Unstable areas were divided into three levels of landslide risk - low, medium and high risk. An area of 111.19 km2 (14.3% represents low landslide risk, medium risk 29.7 km2 (3.8% and 16.01 km2 (2% represents high risk. Since Zlín Formation lithological unit covers approximately one-third of the study area, it also influences the overall landslide risk assessment. This lithological formation covers the largest area within all landslide risk classes as well as in conditionally stable areas. The most frequent slope class was in the range of 14-19. The higher susceptibility of Zlín Formation to landslides is caused mainly by different geomorphological value of claystone and sandstone sequence. The higher share of claystone results in higher susceptibility of this formation to exogenous degradation processes.

Landslide Susceptibility Statistical Methods: A Critical and Systematic Literature Review

Science.gov (United States)

Mihir, Monika; Malamud, Bruce; Rossi, Mauro; Reichenbach, Paola; Ardizzone, Francesca

2014-05-01

Landslide susceptibility assessment, the subject of this systematic review, is aimed at understanding the spatial probability of slope failures under a set of geomorphological and environmental conditions. It is estimated that about 375 landslides that occur globally each year are fatal, with around 4600 people killed per year. Past studies have brought out the increasing cost of landslide damages which primarily can be attributed to human occupation and increased human activities in the vulnerable environments. Many scientists, to evaluate and reduce landslide risk, have made an effort to efficiently map landslide susceptibility using different statistical methods. In this paper, we do a critical and systematic landslide susceptibility literature review, in terms of the different statistical methods used. For each of a broad set of studies reviewed we note: (i) study geography region and areal extent, (ii) landslide types, (iii) inventory type and temporal period covered, (iv) mapping technique (v) thematic variables used (vi) statistical models, (vii) assessment of model skill, (viii) uncertainty assessment methods, (ix) validation methods. We then pulled out broad trends within our review of landslide susceptibility, particularly regarding the statistical methods. We found that the most common statistical methods used in the study of landslide susceptibility include logistic regression, artificial neural network, discriminant analysis and weight of evidence. Although most of the studies we reviewed assessed the model skill, very few assessed model uncertainty. In terms of geographic extent, the largest number of landslide susceptibility zonations were in Turkey, Korea, Spain, Italy and Malaysia. However, there are also many landslides and fatalities in other localities, particularly India, China, Philippines, Nepal and Indonesia, Guatemala, and Pakistan, where there are much fewer landslide susceptibility studies available in the peer-review literature. This

Evaluation of earthquake-triggered landslides in el Salvador using a Gis based newmark model

OpenAIRE

García Rodríguez, María José; Havenith, Hans; Benito Oterino, Belen

2008-01-01

In this work, a model for evaluating earthquake-triggered landslides hazard following the Newmark methodology is developed in a Geographical Information System (GIS). It is applied to El Salvador, one of the most seismically active regions in Central America, where the last severe destructive earthquakes occurred in January 13th and February 13th, 2001. The first of these earthquakes triggered more the 500 landslides and killed at least 844 people. This study is centred on the area (10x6km) w...

Regional analysis assessment of landslide hazard and zoning map for transmission line route selection using GIS

International Nuclear Information System (INIS)

Baharuddin, I N Z; Omar, R C; Usman, F; Mejan, M A; Halim, M K Abd; Zainol, M A; Zulkarnain, M S

2013-01-01

The stability of ground as foundation for infrastructure development is always associated with geology and geomorphology aspects. Failure to carefully analyze these aspects may induce ground instability such subsidence and landslide which eventually can cause catastrophe to the infrastructure i.e. instability of transmission tower. However, in some cases such as the study area this is unavoidable. A GIS system for analysis of route was favoured to perform optimal route predictions based selection by incorporating multiple influence factors into its analysis by incorporating the Landslide Hazard Map (LHM) that was produced on basis of slope map, aspect map, land use map and geological map with the help of ArcGIS using weighted overlay method. Based on LHM it is safe to conclude that the proposed route for Ulu Jelai- Neggiri-Lebir-LILO transmission line has very low risk in term of landslides.

Assessments on landslide susceptibility in the Tseng-wen reservoir watershed, Taiwan

Science.gov (United States)

Chen, Yu-Chin; Chen, Yung-Chau; Chen, Wen-Fu

2014-05-01

Typhoon Morakot under the strong influence of southwestern monsoon wind struck Taiwan on 8 August 2009, and dumped record-breaking rains in southern Taiwan. It triggered enormous landslides in mountains and severe flooding in low-lying areas. In addition, it destroyed or damaged houses, agricultural fields, roads, bridges, and other infrastructure facilities, causing massive economic loss and, more tragically, human casualties. In order to evaluate landslide hazard and risk assessment, it is important to understand the potential sites of landslide and their spatial distribution. Multi-temporal satellite images and geo-spatial data are used to build landslide susceptibility map for the post-disaster in the Tseng-wen reservoir watershed in this research. Elevation, slope, aspect, NDVI (normalized differential vegetation index), relief, roughness, distance to river, and distance to road are the considered factors for estimating landslide susceptibility. Maximum hourly rainfall and total rainfall, accompanied with typhoon event, are selected as the trigger factors of landslide events. Logistic regression analysis is adopted as the statistical method to model landslide susceptibility. The assessed susceptibility is represented in 4 levels which are high, high-intermediate, intermediate, and low level, respectively. Landslide spatial distribution can be depicted as a landslide susceptibility map with respect to each considered influence factors for a specified susceptible level. The landslide areas are about 358 ha and 1,485 ha before and after typhoon Morakot. The new landslide area, induced by typhoon Morakot, is as almost 4 times as the landslide area before typhoon Morakot. In addition, there is about 44.56% landslide area elevation ranging from 500m to 1000m and about 57.22% average slope ranging from 30° to 45° of landslide area. Furthermore, the devastating landslides were happened at those sites close to rivers, exposed area, and area with big land cover change

Transmission tower classification based on landslide risk map generated by Geographical Information System (GIS) at Cameron Highlands

International Nuclear Information System (INIS)

Hazwani N K; Rohayu C O; Fathoni U; Baharuddin, Inz

2013-01-01

Transmission tower is usually locates at remote area which is covered by hilly topography. Landslide is mainly occurring at hilly area and causing failure to the tower structure. This phenomenon subsequently will affect the national electricity supply. A landslide risk hazard map is generated using Geographical Information System (GIS). Risk classification is introduced to initiate the monitoring process along Jor-Bintang transmission line, Cameron Highland, Pahang. The classification has been divided into three categories, which are low, medium and high. This method can be applied in slope monitoring activities since all towers have been classified based on their risk level. Therefore, maintenance schedule can be planned smoothly and efficiently.

Transmission tower classification based on landslide risk Map generated by Geographical Information System (GIS) at Cameron Highlands

International Nuclear Information System (INIS)

Hazwani N K; Rohayu C O; Fathoni U; Baharuddin, I N Z; Azwin Z A

2013-01-01

Transmission tower is usually locates at remote area which is covered by hilly topography. Landslide is mainly occurring at hilly area and causing failure to the tower structure. This phenomenon subsequently will affect the national electricity supply. A landslide risk hazard map is generated using Geographical Information System (GIS). Risk classification is introduced to initiate the monitoring process along Jor-Bintang transmission line, Cameron Highland, Pahang. The classification has been divided into three categories, which are low, medium and high. This method can be applied in slope monitoring activities since all towers have been classified based on their risk level. Therefore, maintenance schedule can be planned smoothly and efficiently.

Spatial probabilistic approach on landslide susceptibility assessment from high resolution sensors derived parameters

International Nuclear Information System (INIS)

Aman, S N A; Latif, Z Abd; Pradhan, B

2014-01-01

Landslide occurrence depends on various interrelating factors which consequently initiate to massive mass of soil and rock debris that move downhill due to the gravity action. LiDAR has come with a progressive approach in mitigating landslide by permitting the formation of more accurate DEM compared to other active space borne and airborne remote sensing techniques. The objective of this research is to assess the susceptibility of landslide in Ulu Klang area by investigating the correlation between past landslide events with geo environmental factors. A high resolution LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive second deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI) as well as NDVI generated from IKONOS imagery. Subsequently, a probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factor. Factor ratings were summed up to obtain Landslide Susceptibility Index (LSI) to construct the landslide susceptibility map

Landslide Susceptibility Assessment Using Frequency Ratio Technique with Iterative Random Sampling

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Hyun-Joo Oh

2017-01-01

Full Text Available This paper assesses the performance of the landslide susceptibility analysis using frequency ratio (FR with an iterative random sampling. A pair of before-and-after digital aerial photographs with 50 cm spatial resolution was used to detect landslide occurrences in Yongin area, Korea. Iterative random sampling was run ten times in total and each time it was applied to the training and validation datasets. Thirteen landslide causative factors were derived from the topographic, soil, forest, and geological maps. The FR scores were calculated from the causative factors and training occurrences repeatedly ten times. The ten landslide susceptibility maps were obtained from the integration of causative factors that assigned FR scores. The landslide susceptibility maps were validated by using each validation dataset. The FR method achieved susceptibility accuracies from 89.48% to 93.21%. And the landslide susceptibility accuracy of the FR method is higher than 89%. Moreover, the ten times iterative FR modeling may contribute to a better understanding of a regularized relationship between the causative factors and landslide susceptibility. This makes it possible to incorporate knowledge-driven considerations of the causative factors into the landslide susceptibility analysis and also be extensively used to other areas.

Landslide Susceptibility Evaluation on agricultural terraces of DOURO VALLEY (PORTUGAL), using physically based mathematical models.

Science.gov (United States)

Faria, Ana; Bateira, Carlos; Laura, Soares; Fernandes, Joana; Gonçalves, José; Marques, Fernando

2016-04-01

The work focuses the evaluation of landslide susceptibility in Douro Region agricultural terraces, supported by dry stone walls and earth embankments, using two physically based models. The applied models, SHALSTAB (Montgomery et al.,1994; Dietrich et al., 1995) and SINMAP (PACK et al., 2005), combine an infinite slope stability model with a steady state hydrological model, and both use the following geophysical parameters: cohesion, friction angle, specific weight and soil thickness. The definition of the contributing areas is different in both models. The D∞ methodology used by SINMAP model suggests a great influence of the terraces morphology, providing a much more diffuse flow on the internal flow modelling. The MD8 used in SHALSTAB promotes an important degree of flow concentration, representing an internal flow based on preferential paths of the runoff as the areas more susceptible to saturation processes. The model validation is made through the contingency matrix method (Fawcett, 2006; Raia et al., 2014) and implies the confrontation with the inventory of past landslides. The True Positive Rate shows that SHALSTAB classifies 77% of the landslides on the high susceptibility areas, while SINMAP reaches 90%. The SINMAP has a False Positive Rate (represents the percentage of the slipped area that is classified as unstable but without landslides) of 83% and the SHALSTAB has 67%. The reliability (analyzes the areas that were correctly classified on the total area) of SHALSTAB is better (33% against 18% of SINMAP). Relative to Precision (refers to the ratio of the slipped area correctly classified over the whole area classified as unstable) SHALSTAB has better results (0.00298 against 0.00283 of SINMAP). It was elaborate the index TPR/FPR and better results obtained by SHALSTAB (1.14 against 1.09 of SINMAP). SHALSTAB shows a better performance in the definition of susceptibility most prone areas to instability processes. One of the reasons for the difference of

A multi-annual landslide inventory for the assessment of shallow landslide susceptibility - Two test cases in Vorarlberg, Austria

Science.gov (United States)

Zieher, Thomas; Perzl, Frank; Rössel, Monika; Rutzinger, Martin; Meißl, Gertraud; Markart, Gerhard; Geitner, Clemens

2016-04-01

Geomorphological landslide inventories provide crucial input data for any study on the assessment of landslide susceptibility, hazard or risk. Several approaches for assessing landslide susceptibility have been proposed to identify areas particularly vulnerable to this natural hazard. What they have in common is the need for data of observed landslides. Therefore the first step of any study on landslide susceptibility is usually the compilation of a geomorphological landslide inventory using a geographical information system. Recent research has proved the feasibility of orthophoto interpretation for the preparation of an inventory aimed at the delineation of landslides with the use of distinctive signs in the imagery data. In this study a multi-annual landslide inventory focusing on shallow landslides (i.e. translational soil slides of 0-2 m in depth) was compiled for two study areas in Vorarlberg (Austria) from the interpretation of nine orthophoto series. In addition, derivatives of two generations of airborne laser scanning data aided the mapping procedure. Landslide scar areas were delineated on the basis of a high-resolution differential digital terrain model. The derivation of landslide volumes, depths and depth-to-length ratios are discussed. Results show that most mapped landslides meet the definition of a shallow landslide. The inventory therefore provides the data basis for the assessment of shallow landslide susceptibility and allows for the application of various modelling techniques.

Combined rock slope stability and shallow landslide susceptibility assessment of the Jasmund cliff area (Rügen Island, Germany

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A. Günther

2009-05-01

Full Text Available In this contribution we evaluated both the structurally-controlled failure susceptibility of the fractured Cretaceous chalk rocks and the topographically-controlled shallow landslide susceptibility of the overlying glacial sediments for the Jasmund cliff area on Rügen Island, Germany. We employed a combined methodology involving spatially distributed kinematical rock slope failure testing with tectonic fabric data, and both physically- and inventory-based shallow landslide susceptibility analysis. The rock slope failure susceptibility model identifies areas of recent cliff collapses, confirming its value in predicting the locations of future failures. The model reveals that toppling is the most important failure type in the Cretaceous chalk rocks of the area. The shallow landslide susceptibility analysis involves a physically-based slope stability evaluation which utilizes material strength and hydraulic conductivity data, and a bivariate landslide susceptibility analysis exploiting landslide inventory data and thematic information on ground conditioning factors. Both models show reasonable success rates when evaluated with the available inventory data, and an attempt was made to combine the individual models to prepare a map displaying both terrain instability and landslide susceptibility. This combination highlights unstable cliff portions lacking discrete landslide areas as well as cliff sections highly affected by past landslide events. Through a spatial integration of the rock slope failure susceptibility model with the combined shallow landslide assessment we produced a comprehensive landslide susceptibility map for the Jasmund cliff area.

Susceptibility mapping in the Río El Estado watershed, Pico de Orizaba volcano, Mexico

Science.gov (United States)

Legorreta Paulin, G.; Bursik, M. I.; Lugo Hubp, J.; Paredes Mejía, L.; Aceves Quesada, F.

2013-12-01

In volcanic terrains, dormant stratovolcanoes are very common and can trigger landslides and debris flows continually along stream systems, thereby affecting human settlements and economic activities. It is important to assess their potential impact and damage through the use of landslide inventory maps and landslide models. This poster provides an overview of the on-going research project (Grant SEP-CONACYT no 167495) from the Institute of Geography at the National Autonomous University of Mexico (UNAM) that seeks to conduct a multi-temporal landslide inventory and produce a landslide susceptibility map by using Geographic Information Systems (GIS). The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano, the highest mountain in Mexico, is selected as a study area. The catchment covers 5.2 km2 with elevations ranging from 2676.79 to 4248.2 m a.s.l. and hillslopes between 5° and 56°. The stream system of Río El Estado catchment erodes Tertiary and Quaternary lavas, pyroclastic flows, and fall deposits. The geologic and geomorphologic factors in combination with high seasonal precipitation, high degree of weathering, and steep slopes predispose the study area to landslides. The method encompasses two main levels of analysis to assess landslide susceptibility. The first level builds a historic landslide inventory. In the study area, an inventory of more than 100 landslides was mapped from interpretation of multi-temporal aerial orthophotographs and local field surveys to assess and describe landslide distribution. All landslides were digitized into a GIS, and the spatial geo-database of landslides was constructed from standardized GIS datasets. The second level calculates the susceptibility for the watershed. Multiple Logistic Regression (MLR) was used to examine the relationship between landsliding and several independent variables (elevation, slope, terrain curvature, flow direction, saturation, contributing area, land use, and geology

Data management with a landslide inventory of the Franconian Alb (Germany) using a spatial database and GIS tools

Science.gov (United States)

Bemm, Stefan; Sandmeier, Christine; Wilde, Martina; Jaeger, Daniel; Schwindt, Daniel; Terhorst, Birgit

2014-05-01

), informations on location, landslide types and causes, geomorphological positions, geometries, hazards and damages, as well as assessments related to the activity of landslides. Furthermore, there are stored spatial objects, which represent the components of a landslide, in particular the scarps and the accumulation areas. Besides, waterways, map sheets, contour lines, detailed infrastructure data, digital elevation models, aspect and slope data are included. Examples of spatial queries to the database are intersections of raster and vector data for calculating values for slope gradients or aspects of landslide areas and for creating multiple, overlaying sections for the comparison of slopes, as well as distances to the infrastructure or to the next receiving drainage. Furthermore, getting informations on landslide magnitudes, distribution and clustering, as well as potential correlations concerning geomorphological or geological conditions. The data management concept in this study can be implemented for any academic, public or private use, because it is independent from any obligatory licenses. The created spatial database offers a platform for interdisciplinary research and socio-economic questions, as well as for landslide susceptibility and hazard indication mapping. Obe, R.O., Hsu, L.S. 2011. PostGIS in action. - pp 492, Manning Publications, Stamford

Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model

KAUST Repository

Camilo, Daniela Castro; Lombardo, Luigi; Mai, Paul Martin; Dou, Jie; Huser, Raphaë l

2017-01-01

Grid-based landslide susceptibility models at regional scales are computationally demanding when using a fine grid resolution. Conversely, Slope-Unit (SU) based susceptibility models allows to investigate the same areas offering two main advantages: 1) a smaller computational burden and 2) a more geomorphologically-oriented interpretation. In this contribution, we generate SU-based landslide susceptibility for the Sado Island in Japan. This island is characterized by deep-seated landslides which we assume can only limitedly be explained by the first two statistical moments (mean and variance) of a set of predictors within each slope unit. As a consequence, in a nested experiment, we first analyse the distributions of a set of continuous predictors within each slope unit computing the standard deviation and quantiles from 0.05 to 0.95 with a step of 0.05. These are then used as predictors for landslide susceptibility. In addition, we combine shape indices for polygon features and the normalized extent of each class belonging to the outcropping lithology in a given SU. This procedure significantly enlarges the size of the predictor hyperspace, thus producing a high level of slope-unit characterization. In a second step, we adopt a LASSO-penalized Generalized Linear Model to shrink back the predictor set to a sensible and interpretable number, carrying only the most significant covariates in the models. As a result, we are able to document the geomorphic features (e.g., 95% quantile of Elevation and 5% quantile of Plan Curvature) that primarily control the SU-based susceptibility within the test area while producing high predictive performances. The implementation of the statistical analyses are included in a parallelized R script (LUDARA) which is here made available for the community to replicate analogous experiments.

Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model

KAUST Repository

Camilo, Daniela Castro

2017-08-30

Grid-based landslide susceptibility models at regional scales are computationally demanding when using a fine grid resolution. Conversely, Slope-Unit (SU) based susceptibility models allows to investigate the same areas offering two main advantages: 1) a smaller computational burden and 2) a more geomorphologically-oriented interpretation. In this contribution, we generate SU-based landslide susceptibility for the Sado Island in Japan. This island is characterized by deep-seated landslides which we assume can only limitedly be explained by the first two statistical moments (mean and variance) of a set of predictors within each slope unit. As a consequence, in a nested experiment, we first analyse the distributions of a set of continuous predictors within each slope unit computing the standard deviation and quantiles from 0.05 to 0.95 with a step of 0.05. These are then used as predictors for landslide susceptibility. In addition, we combine shape indices for polygon features and the normalized extent of each class belonging to the outcropping lithology in a given SU. This procedure significantly enlarges the size of the predictor hyperspace, thus producing a high level of slope-unit characterization. In a second step, we adopt a LASSO-penalized Generalized Linear Model to shrink back the predictor set to a sensible and interpretable number, carrying only the most significant covariates in the models. As a result, we are able to document the geomorphic features (e.g., 95% quantile of Elevation and 5% quantile of Plan Curvature) that primarily control the SU-based susceptibility within the test area while producing high predictive performances. The implementation of the statistical analyses are included in a parallelized R script (LUDARA) which is here made available for the community to replicate analogous experiments.

Neural Network-Based Model for Landslide Susceptibility and Soil Longitudinal Profile Analyses

DEFF Research Database (Denmark)

Farrokhzad, F.; Barari, Amin; Choobbasti, A. J.

2011-01-01

The purpose of this study was to create an empirical model for assessing the landslide risk potential at Savadkouh Azad University, which is located in the rural surroundings of Savadkouh, about 5 km from the city of Pol-Sefid in northern Iran. The soil longitudinal profile of the city of Babol......, located 25 km from the Caspian Sea, also was predicted with an artificial neural network (ANN). A multilayer perceptron neural network model was applied to the landslide area and was used to analyze specific elements in the study area that contributed to previous landsliding events. The ANN models were...... studies in landslide susceptibility zonation....

Evaluation of Landslide Mapping Techniques and LiDAR-based Conditioning Factors

Science.gov (United States)

Mahalingam, R.; Olsen, M. J.

2014-12-01

Landslides are a major geohazard, which result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping can help communities to plan and prepare for these damaging events. Mapping landslide susceptible locations using GIS and remote sensing techniques is gaining popularity in the past three decades. These efforts use a wide variety of procedures and consider a wide range of factors. Unfortunately, each study is often completed differently and independently of others. Further, the quality of the datasets used varies in terms of source, data collection, and generation, which can propagate errors or inconsistencies into the resulting output maps. Light detection and ranging (LiDAR) has proved to have higher accuracy in representing the continuous topographic surface, which can help minimize this uncertainty. The primary objectives of this paper are to investigate the applicability and performance of terrain factors in landslide hazard mapping, determine if LiDAR-derived datasets (slope, slope roughness, terrain roughness, stream power index and compound topographic index) can be used for predictive mapping without data representing other common landslide conditioning factors, and evaluate the differences in landslide susceptibility mapping using widely-used statistical approaches. The aforementioned factors were used to produce landslide susceptibility maps for a 140 km2 study area in northwest Oregon using six representative techniques: frequency ratio, weights of evidence, logistic regression, discriminant analysis, artificial neural network, and support vector machine. Most notably, the research showed an advantage in selecting fewer critical conditioning factors. The most reliable factors all could be derived from a single LiDAR DEM, reducing the need for laborious and costly data gathering. Most of the six techniques showed similar statistical results; however, ANN showed less accuracy for predictive mapping. Keywords : Li

From rainfall to slope instability: an automatic GIS procedure for susceptibility analyses over wide areas

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Bianca Federici

2015-07-01

Full Text Available The paper proposes an automatic procedure in geographic information system (GIS for the analysis and prediction of landslides due to rainfall events over wide areas. It runs, for each unit cell, a hydrological balance based on the Curve Number method (USDA-SCS 1985–1986, computing the evolution of groundwater as a result of precipitation and then checks the overcoming, or not, of limit equilibrium conditions of the land in the domain of interest. The mathematical model was implemented in the free and open source GIS GRASS. For any sequence of consecutive days of rain, according to the conditions of soil moisture prior to the time history under study, the hydro-geotechnical model allows (1 the determination of the oscillations of the phreatic table, (2 the part of saturated soil and (3 the slope stability analysis, by taking into proper account the pore pressures buildup. The results of this procedure are returned in raster format, allowing an easy and intuitive interpretation of the land mass sensitivity to meteoric actions. The suggested procedure was applied on an extensive kinematic phenomenon surrounding the city of Santo Stefano d’Aveto (Liguria, Italy. The realized maps of landslide susceptibility are in excellent agreement with what is evident on site.

Weights of Evidence Method for Landslide Susceptibility Mapping in Takengon, Central Aceh, Indonesia

Science.gov (United States)

Pamela; Sadisun, Imam A.; Arifianti, Yukni

2018-02-01

Takengon is an area prone to earthquake disaster and landslide. On July 2, 2013, Central Aceh earthquake induced large numbers of landslides in Takengon area, which resulted in casualties of 39 people. This location was chosen to assess the landslide susceptibility of Takengon, using a statistical method, referred to as the weight of evidence (WoE). This WoE model was applied to indicate the main factors influencing the landslide susceptible area and to derive landslide susceptibility map of Takengon. The 251 landslides randomly divided into two groups of modeling/training data (70%) and validation/test data sets (30%). Twelve thematic maps of evidence are slope degree, slope aspect, lithology, land cover, elevation, rainfall, lineament, peak ground acceleration, curvature, flow direction, distance to river and roads used as landslide causative factors. According to the AUC, the significant factor controlling the landslide is the slope, the slope aspect, peak ground acceleration, elevation, lithology, flow direction, lineament, and rainfall respectively. Analytical result verified by using test data of landslide shows AUC prediction rate is 0.819 and AUC success rate with all landslide data included is 0.879. This result showed the selective factors and WoE method as good models for assessing landslide susceptibility. The landslide susceptibility map of Takengon shows the probabilities, which represent relative degrees of susceptibility for landslide proneness in Takengon area.

Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models

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S. Pereira

2012-04-01

Full Text Available The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been developed using variables that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km² located in the Northern Portugal.

In order to identify the best combination of landslide predisposing factors, all possible combinations using up to seven predisposing factors were performed, which resulted in 120 predictions that were assessed with a landside inventory containing 767 shallow translational slides. The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. The best model was developed with only three landslide predisposing factors (slope angle, inverse wetness index, and land use and was compared with a model developed using all seven landslide predisposing factors.

Results showed that it is possible to produce a reliable landslide susceptibility model using fewer landslide predisposing factors, which contributes towards higher conditional independence.

A comparison between univariate probabilistic and multivariate (logistic regression) methods for landslide susceptibility analysis: the example of the Febbraro valley (Northern Alps, Italy)

Science.gov (United States)

Rossi, M.; Apuani, T.; Felletti, F.

2009-04-01

The aim of this paper is to compare the results of two statistical methods for landslide susceptibility analysis: 1) univariate probabilistic method based on landslide susceptibility index, 2) multivariate method (logistic regression). The study area is the Febbraro valley, located in the central Italian Alps, where different types of metamorphic rocks croup out. On the eastern part of the studied basin a quaternary cover represented by colluvial and secondarily, by glacial deposits, is dominant. In this study 110 earth flows, mainly located toward NE portion of the catchment, were analyzed. They involve only the colluvial deposits and their extension mainly ranges from 36 to 3173 m2. Both statistical methods require to establish a spatial database, in which each landslide is described by several parameters that can be assigned using a main scarp central point of landslide. The spatial database is constructed using a Geographical Information System (GIS). Each landslide is described by several parameters corresponding to the value of main scarp central point of the landslide. Based on bibliographic review a total of 15 predisposing factors were utilized. The width of the intervals, in which the maps of the predisposing factors have to be reclassified, has been defined assuming constant intervals to: elevation (100 m), slope (5 °), solar radiation (0.1 MJ/cm2/year), profile curvature (1.2 1/m), tangential curvature (2.2 1/m), drainage density (0.5), lineament density (0.00126). For the other parameters have been used the results of the probability-probability plots analysis and the statistical indexes of landslides site. In particular slope length (0 ÷ 2, 2 ÷ 5, 5 ÷ 10, 10 ÷ 20, 20 ÷ 35, 35 ÷ 260), accumulation flow (0 ÷ 1, 1 ÷ 2, 2 ÷ 5, 5 ÷ 12, 12 ÷ 60, 60 ÷27265), Topographic Wetness Index 0 ÷ 0.74, 0.74 ÷ 1.94, 1.94 ÷ 2.62, 2.62 ÷ 3.48, 3.48 ÷ 6,00, 6.00 ÷ 9.44), Stream Power Index (0 ÷ 0.64, 0.64 ÷ 1.28, 1.28 ÷ 1.81, 1.81 ÷ 4.20, 4.20 ÷ 9

Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models

Science.gov (United States)

Schlögel, R.; Marchesini, I.; Alvioli, M.; Reichenbach, P.; Rossi, M.; Malet, J.-P.

2018-01-01

We perform landslide susceptibility zonation with slope units using three digital elevation models (DEMs) of varying spatial resolution of the Ubaye Valley (South French Alps). In so doing, we applied a recently developed algorithm automating slope unit delineation, given a number of parameters, in order to optimize simultaneously the partitioning of the terrain and the performance of a logistic regression susceptibility model. The method allowed us to obtain optimal slope units for each available DEM spatial resolution. For each resolution, we studied the susceptibility model performance by analyzing in detail the relevance of the conditioning variables. The analysis is based on landslide morphology data, considering either the whole landslide or only the source area outline as inputs. The procedure allowed us to select the most useful information, in terms of DEM spatial resolution, thematic variables and landslide inventory, in order to obtain the most reliable slope unit-based landslide susceptibility assessment.

Rainfall-induced landslide vulnerability Assessment in urban area reflecting Urban structure and building characteristics

Science.gov (United States)

Park, C.; Cho, M.; Lee, D.

2017-12-01

Landslide vulnerability assessment methodology of urban area is proposed with urban structure and building charateristics which can consider total damage cost of climate impacts. We used probabilistic analysis method for modeling rainfall-induced shallow landslide susceptibility by slope stability analysis and Monte Carlo simulations. And We combined debris flows with considering spatial movements under topographical condition and built environmental condition. Urban vulnerability of landslide is assessed by two categories: physical demages and urban structure aspect. Physical vulnerability is related to buildings, road, other ubran infra. Urban structure vulnerability is considered a function of the socio-economic factors, trigger factor of secondary damage, and preparedness level of the local government. An index-based model is developed to evaluate the life and indirect damage under landslide as well as the resilience ability against disasters. The analysis was performed in a geographic information system (GIS) environment because GIS can deal efficiently with a large volume of spatial data. The results of the landslide susceptibility assessment were compared with the landslide inventory, and the proposed approach demonstrated good predictive performance. The general trend found in this study indicates that the higher population density areas under a weaker fiscal condition that are located at the downstream of mountainous areas are more vulnerable than the areas in opposite conditions.

An offline-online Web-GIS Android application for fast data acquisition of landslide hazard and risk

Science.gov (United States)

Olyazadeh, Roya; Sudmeier-Rieux, Karen; Jaboyedoff, Michel; Derron, Marc-Henri; Devkota, Sanjaya

2017-04-01

Regional landslide assessments and mapping have been effectively pursued by research institutions, national and local governments, non-governmental organizations (NGOs), and different stakeholders for some time, and a wide range of methodologies and technologies have consequently been proposed. Land-use mapping and hazard event inventories are mostly created by remote-sensing data, subject to difficulties, such as accessibility and terrain, which need to be overcome. Likewise, landslide data acquisition for the field navigation can magnify the accuracy of databases and analysis. Open-source Web and mobile GIS tools can be used for improved ground-truthing of critical areas to improve the analysis of hazard patterns and triggering factors. This paper reviews the implementation and selected results of a secure mobile-map application called ROOMA (Rapid Offline-Online Mapping Application) for the rapid data collection of landslide hazard and risk. This prototype assists the quick creation of landslide inventory maps (LIMs) by collecting information on the type, feature, volume, date, and patterns of landslides using open-source Web-GIS technologies such as Leaflet maps, Cordova, GeoServer, PostgreSQL as the real DBMS (database management system), and PostGIS as its plug-in for spatial database management. This application comprises Leaflet maps coupled with satellite images as a base layer, drawing tools, geolocation (using GPS and the Internet), photo mapping, and event clustering. All the features and information are recorded into a GeoJSON text file in an offline version (Android) and subsequently uploaded to the online mode (using all browsers) with the availability of Internet. Finally, the events can be accessed and edited after approval by an administrator and then be visualized by the general public.

Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods

Science.gov (United States)

Pourghasemi, Hamid Reza; Rossi, Mauro

2017-10-01

Landslides are identified as one of the most important natural hazards in many areas throughout the world. The essential purpose of this study is to compare general linear model (GLM), general additive model (GAM), multivariate adaptive regression spline (MARS), and modified analytical hierarchy process (M-AHP) models and assessment of their performances for landslide susceptibility modeling in the west of Mazandaran Province, Iran. First, landslides were identified by interpreting aerial photographs, and extensive field works. In total, 153 landslides were identified in the study area. Among these, 105 landslides were randomly selected as training data (i.e. used in the models training) and the remaining 48 (30 %) cases were used for the validation (i.e. used in the models validation). Afterward, based on a deep literature review on 220 scientific papers (period between 2005 and 2012), eleven conditioning factors including lithology, land use, distance from rivers, distance from roads, distance from faults, slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, and profile curvature were selected. The Certainty Factor (CF) model was used for managing uncertainty in rule-based systems and evaluation of the correlation between the dependent (landslides) and independent variables. Finally, the landslide susceptibility zonation was produced using GLM, GAM, MARS, and M-AHP models. For evaluation of the models, the area under the curve (AUC) method was used and both success and prediction rate curves were calculated. The evaluation of models for GLM, GAM, and MARS showed 90.50, 88.90, and 82.10 % for training data and 77.52, 70.49, and 78.17 % for validation data, respectively. Furthermore, The AUC value of the produced landslide susceptibility map using M-AHP showed a training value of 77.82 % and validation value of 82.77 % accuracy. Based on the overall assessments, the proposed approaches showed reasonable results for landslide

Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan

NARCIS (Netherlands)

Bacha, Alam Sher; Shafique, Muhammad; van der Werff, H.M.A.

2018-01-01

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known

Detection of local site conditions influencing earthquake shaking and secondary effects in Southwest-Haiti using remote sensing and GIS-methods

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B. Theilen-Willige

2010-06-01

Full Text Available The potential contribution of remote sensing and GIS techniques to earthquake hazard analysis was investigated in SW-Haiti in order to improve the systematic, standardized inventory of those areas that are more susceptible to earthquake ground motions or to earthquake related secondary effects such as landslides, liquefaction, soil amplifications, compaction or even tsunami-waves. Geophysical, topographical, geological data and satellite images were collected, processed, and integrated into a spatial database using Geoinformation Systems (GIS and image processing techniques. The GIS integrated evaluation of satellite imageries, of digital topographic data and of various open-source geodata can contribute to the acquisition of those specific tectonic, geomorphologic/topographic settings influencing local site conditions in Haiti and, thus, to a first data base stock. Using the weighted overlay techniques in GIS susceptibility maps were produced indicating areas where causal factors influencing surface-near earthquake shock occur aggregated and interfering each other and, thus, rise the susceptibility to soil amplification. This approach was used as well to create landslide and flooding susceptibility maps.

Weights of evidence method for landslide susceptibility mapping in Tangier, Morocco

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Bousta Mahfoud

2018-01-01

Full Text Available Tangier region is known by a high density of mass movements which cause several human and economic losses. The goal of this paper is to assess the landslide susceptibility of Tangier using the Weight of Evidence method (WofE. The method is founded on the principle that an event (landslide is more likely to occur based on the relationship between the presence or absence of a predictive variable (predisposing factors and the occurrence of this event. The inventory, description and analysis of mass movements were prepared. Then the main factors governing their occurrence (lithology, fault, slope, elevation, exposure, drainage and land use were mapped before applying WofE. Finally, the ROC curves were established and the areas under curves (AUC were calculated to evaluate the degree of fit of the model and to choose the best landslide susceptibility zonation. The prediction accuracy was found to be 70%. Obtained susceptibility map shows that 60% of inventoried landslides are in the high to very high susceptibility zones, which is very satisfactory for the validation of the adopted model and the obtained results. These zones are mainly located in the N-E and E part of the Tangier region in the soft and fragile facies of the marls and clays of the Tangier unit, where landuse is characterized by dominance of arable and agricultural land (lack of forest cover. From a purely spatial point of view, the localization of these two classes of susceptibility is completely corresponding to the ground truth data, that is to say that all the environmental and anthropogenic conditions are in place for making this area prone to landslide hazards. The obtained map is a decision-making tool for presenting, comparing and discussing development and urban scenarios in Tangier. These results fall within the context of sustainable development and will help to mitigate the socio-economic impacts usually observed when landslides are triggered.

Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods

Science.gov (United States)

Lee, Jung-Hyun; Sameen, Maher Ibrahim; Pradhan, Biswajeet; Park, Hyuck-Jin

2018-02-01

This study evaluated the generalizability of five models to select a suitable approach for landslide susceptibility modeling in data-scarce environments. In total, 418 landslide inventories and 18 landslide conditioning factors were analyzed. Multicollinearity and factor optimization were investigated before data modeling, and two experiments were then conducted. In each experiment, five susceptibility maps were produced based on support vector machine (SVM), random forest (RF), weight-of-evidence (WoE), ridge regression (Rid_R), and robust regression (RR) models. The highest accuracy (AUC = 0.85) was achieved with the SVM model when either the full or limited landslide inventories were used. Furthermore, the RF and WoE models were severely affected when less landslide samples were used for training. The other models were affected slightly when the training samples were limited.

A Hybrid Physical and Maximum-Entropy Landslide Susceptibility Model

Directory of Open Access Journals (Sweden)

Jerry Davis

2015-06-01

Full Text Available The clear need for accurate landslide susceptibility mapping has led to multiple approaches. Physical models are easily interpreted and have high predictive capabilities but rely on spatially explicit and accurate parameterization, which is commonly not possible. Statistical methods can include other factors influencing slope stability such as distance to roads, but rely on good landslide inventories. The maximum entropy (MaxEnt model has been widely and successfully used in species distribution mapping, because data on absence are often uncertain. Similarly, knowledge about the absence of landslides is often limited due to mapping scale or methodology. In this paper a hybrid approach is described that combines the physically-based landslide susceptibility model “Stability INdex MAPping” (SINMAP with MaxEnt. This method is tested in a coastal watershed in Pacifica, CA, USA, with a well-documented landslide history including 3 inventories of 154 scars on 1941 imagery, 142 in 1975, and 253 in 1983. Results indicate that SINMAP alone overestimated susceptibility due to insufficient data on root cohesion. Models were compared using SINMAP stability index (SI or slope alone, and SI or slope in combination with other environmental factors: curvature, a 50-m trail buffer, vegetation, and geology. For 1941 and 1975, using slope alone was similar to using SI alone; however in 1983 SI alone creates an Areas Under the receiver operator Curve (AUC of 0.785, compared with 0.749 for slope alone. In maximum-entropy models created using all environmental factors, the stability index (SI from SINMAP represented the greatest contributions in all three years (1941: 48.1%; 1975: 35.3; and 1983: 48%, with AUC of 0.795, 0822, and 0.859, respectively; however; using slope instead of SI created similar overall AUC values, likely due to the combined effect with plan curvature indicating focused hydrologic inputs and vegetation identifying the effect of root cohesion

Comparative assessment of landslide susceptibility. Case study: the Niraj river basin (Transylvania depression, Romania

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RoŞca Sanda

2016-05-01

Full Text Available This study represents a comparison between two independent models used to evaluate landslide susceptibility in Romania: first, the model derived from the Romanian Governmental Decision no. 447/2003 (H.G. 447 and second, the bivariate statistical analysis. Considering the numerous objections to the first approach, which is also imposed by law, the accuracy of the results was analyzed using an alternative method which takes into consideration the reality from the field to a greater extent (the inventory of the existing landslides. The case study is focused on the Niraj catchment area (658 km2, a representative area for frequent landslide occurrence. The H.G. 447 model implies the estimation of the importance of eight factors involved in landslide occurrence: lithology, geomorphology, structure, hydro-climatic factors, hydrogeology, seismicity, forest cover and the anthropogenic factor. A thematic map was generated and analyzed for each one of the eight factors influencing slope instability and a specific coefficient was assigned. The statistical model, based on the bivariate probability analysis, was applied in order to predict the spatial distribution of the susceptibility classes. The probability of landslide occurrence was estimated based on the assumption that the prediction of the spatial distributions of landslides starts from the existing ones. In order to validate the model, the resulting maps were compared with the existing landslide maps: the relative landslide density index (R and the relative operation curve (ROC value were calculated, which indicate that the statistical model emphasizes a better correlation between the susceptibility classes and the active landslides (ROC value 0.972, the causative factors selected being relevant for the applied models.

Landslide susceptibility estimations in the Gerecse hills (Hungary).

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Gerzsenyi, Dávid; Gáspár, Albert

2017-04-01

Surface movement processes are constantly posing threat to property in populated and agricultural areas in the Gerecse hills (Hungary). The affected geological formations are mainly unconsolidated sediments. Pleistocene loess and alluvial terrace sediments are overwhelmingly present, but fluvio-lacustrine sediments of the latest Miocene, and consolidated Eocene and Mesozoic limestones and marls can also be found in the area. Landslides and other surface movement processes are being studied for a long time in the area, but a comprehensive GIS-based geostatistical analysis have not yet been made for the whole area. This was the reason for choosing the Gerecse as the focus area of the study. However, the base data of our study are freely accessible from online servers, so the used method can be applied to other regions in Hungary. Qualitative data was acquired from the landslide-inventory map of the Hungarian Surface Movement Survey and from the Geological Map of Hungary (1 : 100 000). Morphometric parameters derived from the SRMT-1 DEM were used as quantitative variables. Using these parameters the distribution of elevation, slope gradient, aspect and categorized geological features were computed, both for areas affected and not affected by slope movements. Then likelihood values were computed for each parameters by comparing their distribution in the two areas. With combining the likelihood values of the four parameters relative hazard values were computed for each cell. This method is known as the "empirical probability estimation" originally published by Chung (2005). The map created this way shows each cell's place in their ranking based on the relative hazard values as a percentage for the whole study area (787 km2). These values provide information about how similar is a certain area to the areas already affected by landslides based on the four predictor variables. This map can also serve as a base for more complex landslide vulnerability studies involving

ANALYSIS OF JURE LANDSLIDE DAM, SINDHUPALCHOWK USING GIS AND REMOTE SENSING

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T. D. Acharya

2016-06-01

Full Text Available On 2nd August 2014, a rainfall-induced massive landslide hit Jure village, Sindhupalchowk killing 156 people at a distance of 70 km North-East of Kathmandu, Nepal. The landslide was a typical slope failure with massive rock fragments, sand and soil. A total of estimated 6 million cubic meters debris raised more than 100 m from the water level and affected opposite side of the bank. The landslide blocked the Sunkoshi River completely forming an estimated 8 million cubic meter lake of 3km length and 300-350m width upstream. It took nearly 12 hour to fill the lake and overflow the debris dam. The lake affected five Village Development Committees (VDC including highway, school, health post, postal service, police station, VDC office and temple upstream. The bottom of the dam was composed of highly cemented material and the derbies affected Sunkoshi hydropower downstream. Moreover, it caused the potential threat of Lake Outburst Flood. The lake was released by blasting off part of the landslide blockade and facilitated release of water from the lake. With the help of Remote Sensing (RS, series satellite images were used to identified, compared with previous state and quick estimation of potential treat was analysed. Using geographic information System (GIS technology, estimation of volume, affected households, service centres, parcels etc. in the area was possible. In such hilly regions where disaster are very frequent, using GIS and RS technology comes very handy for immediate planning and response.

Landslide susceptibility mapping on a global scale using the method of logistic regression

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L. Lin

2017-08-01

Full Text Available This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected for model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building the model, 70 % of landslide and nonlandslide points were randomly selected for logistic regression, and the others were used for model validation. To evaluate the accuracy of predictive models, this paper adopts several criteria including a receiver operating characteristic (ROC curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on a global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such a result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

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Yao, X.; Tham, L. G.; Dai, F. C.

2008-11-01

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only "failed" case information is usually available in landslide susceptibility mapping.

Spatially explicit shallow landslide susceptibility mapping over large areas

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Bellugi, Dino; Dietrich, William E.; Stock, Jonathan D.; McKean, Jim; Kazian, Brian; Hargrove, Paul

2011-01-01

Recent advances in downscaling climate model precipitation predictions now yield spatially explicit patterns of rainfall that could be used to estimate shallow landslide susceptibility over large areas. In California, the United States Geological Survey is exploring community emergency response to the possible effects of a very large simulated storm event and to do so it has generated downscaled precipitation maps for the storm. To predict the corresponding pattern of shallow landslide susceptibility across the state, we have used the model Shalstab (a coupled steady state runoff and infinite slope stability model) which susceptibility spatially explicit estimates of relative potential instability. Such slope stability models that include the effects of subsurface runoff on potentially destabilizing pore pressure evolution require water routing and hence the definition of upslope drainage area to each potential cell. To calculate drainage area efficiently over a large area we developed a parallel framework to scale-up Shalstab and specifically introduce a new efficient parallel drainage area algorithm which produces seamless results. The single seamless shallow landslide susceptibility map for all of California was accomplished in a short run time, and indicates that much larger areas can be efficiently modelled. As landslide maps generally over predict the extent of instability for any given storm. Local empirical data on the fraction of predicted unstable cells that failed for observed rainfall intensity can be used to specify the likely extent of hazard for a given storm. This suggests that campaigns to collect local precipitation data and detailed shallow landslide location maps after major storms could be used to calibrate models and improve their use in hazard assessment for individual storms.

GIS-BASED ANALYSIS FOR ASSESSING LANDSLIDE AND DROUGHT HAZARD IN THE CORRIDOR OF MT. MERAPI AND MT. MERBABU NATIONAL PARK, INDONESIA

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Hero Marhaento

2016-04-01

Full Text Available A corridor is an area located between two or more protected areas that are important to support the sustainability of the protected areas. This study is aimed at assessing landslide and drought hazard in the corridor area between Mt. Merapi National Park (MMNP and Mt. Merbabu National Park (MMbNP as a part of the corridor management strategy. The corridor area of MMNP and MMbNP comprises four sub-districts in Central Java Province, namely, Sawangan, Selo, Ampel, and Cepogo. A spatial analysis of ArcGIS 10.1 software was used to assess landslide hazard map and the Thorntwaite-Matter Water Balance approach was used to assess drought hazard map. The results have shown that three villages in Cepogo Sub-district and all villages in Selo Sub-district are highly prone to landslide hazard. Furthermore, two villages in Cepogo Sub-district and four villages in Selo Sub-district are prone to drought hazard. This study suggests that these villages should initiate a program called conservation village model based on disaster mitigation for mitigating future landslide and drought disasters.

An application of adaptive neuro-fuzzy inference system to landslide susceptibility mapping (Klang valley, Malaysia)

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Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan

2010-05-01

Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. Recently, the Klang Valley area of Selangor state has faced numerous landslide and mudflow events and much damage occurred in these areas. However, only little effort has been made to assess or predict these events which resulted in serious damages. Through scientific analyses of these landslides, one can assess and predict landslide-susceptible areas and even the events as such, and thus reduce landslide damages through proper preparation and/or mitigation. For this reason , the purpose of the present paper is to produce landslide susceptibility maps of a part of the Klang Valley areas in Malaysia by employing the results of the adaptive neuro-fuzzy inference system (ANFIS) analyses. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments and NDVI were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient

Landslide susceptibility mapping by combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process in Dozain basin

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E. Tazik

2014-10-01

Full Text Available Landslides are among the most important natural hazards that lead to modification of the environment. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic, and geomorphologic characteristics of the region, the purpose of this study was landslide hazard assessment using Fuzzy Logic, frequency ratio and Analytical Hierarchy Process method in Dozein basin, Iran. At first, landslides occurred in Dozein basin were identified using aerial photos and field studies. The influenced landslide parameters that were used in this study including slope, aspect, elevation, lithology, precipitation, land cover, distance from fault, distance from road and distance from river were obtained from different sources and maps. Using these factors and the identified landslide, the fuzzy membership values were calculated by frequency ratio. Then to account for the importance of each of the factors in the landslide susceptibility, weights of each factor were determined based on questionnaire and AHP method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method. At the end, for computing prediction accuracy, the produced map was verified by comparing to existing landslide locations. These results indicate that the combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility map about 51% of the occurred landslide fall into the high and very high susceptibility zones of the landslide susceptibility map, but approximately 26 % of them indeed located in the low and very low susceptibility zones.

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

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Hyun-Joo Oh

2017-09-01

Full Text Available The main purpose of this paper is to present some potential applications of sophisticated data mining techniques, such as artificial neural network (ANN and boosted tree (BT, for landslide susceptibility modeling in the Yongin area, Korea. Initially, landslide inventory was detected from visual interpretation using digital aerial photographic maps with a high resolution of 50 cm taken before and after the occurrence of landslides. The debris flows were randomly divided into two groups: training and validation sets with a 50:50 proportion. Additionally, 18 environmental factors related to landslide occurrence were derived from the topography, soil, and forest maps. Subsequently, the data mining techniques were applied to identify the influence of environmental factors on landslide occurrence of the training set and assess landslide susceptibility. Finally, the landslide susceptibility indexes from ANN and BT were compared with a validation set using a receiver operating characteristics curve. The slope gradient, topographic wetness index, and timber age appear to be important factors in landslide occurrence from both models. The validation result of ANN and BT showed 82.25% and 90.79%, which had reasonably good performance. The study shows the benefit of selecting optimal data mining techniques in landslide susceptibility modeling. This approach could be used as a guideline for choosing environmental factors on landslide occurrence and add influencing factors into landslide monitoring systems. Furthermore, this method can rank landslide susceptibility in urban areas, thus providing helpful information when selecting a landslide monitoring site and planning land-use.

Rainfall-induced landslide susceptibility zonation of Puerto Rico

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Chiara Lepore; Sameer A. Kamal; Peter Shanahan; Rafael L. Bras

2011-01-01

Landslides are a major geologic hazard with estimated tens of deaths and $1–2 billion in economic losses per year in the US alone. The island of Puerto Rico experiences one or two large events per year, often triggered in steeply sloped areas by prolonged and heavy rainfall. Identifying areas susceptible to landslides thus has great potential value for Puerto Rico and...

Implementation of landslide susceptibility maps in Lower Austria as part of risk governance

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Bell, Rainer; Petschko, Helene; Bauer, Christian; Glade, Thomas; Granica, Klaus; Heiss, Gerhard; Leopold, Philip; Pomaroli, Gilbert; Proske, Herwig; Schweigl, Joachim

2013-04-01

Landslides frequently cause damage to agricultural land and infrastructure in Lower Austria - a province of Austria. Also settlements and people are threatened by landslides. To reduce landslide risks and to prevent the establishment of new settlements in highly landslide prone areas, the project "MoNOE" (Method development for landslide susceptibility modeling in Lower Austria) was set up by the provincial government. The main aim of the project is the development of methods to model rock fall and slide susceptibility for an area of approx. 15,900 km2 and to implement the resulting susceptibility maps into the spatial planning strategies of the state. Right from the beginning of the project a close cooperation between the involved scientists and the stakeholders from the Geological Survey of Lower Austria and the Department of Spatial Planning and Regional Policy of Lower Austria was established to ensure that method development and final susceptibility maps meet exactly the needs and demands of the stakeholders. This posed huge challenges, together with its realization in the large study area and a (heterogeneous) complex geological situation,. Limitations were given by restricted data availability (e.g. for geology or landslide inventories) in such a large study area. Rock fall susceptibility was modeled by a combined approach of determining rock fall release areas by empirical slope thresholds (dependent on geology) followed by empirical run-out modeling. Slide susceptibility was modeled based on the statistical approaches of weights of evidence (WofE) and generalized additive models (GAM) by two different research groups. Huge efforts were spent on the validation of all susceptibility models. In a later stage of the project we found that the best scientific maps are not necessarily the best maps to be implemented in spatial planning strategies. Thus, in close cooperation with the stakeholders, decisions had to be taken to find the best resolution of the maps

Comparison and applicability of landslide susceptibility models based on landslide ratio-based logistic regression, frequency ratio, weight of evidence, and instability index methods in an extreme rainfall event

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Wu, Chunhung

2016-04-01

Few researches have discussed about the applicability of applying the statistical landslide susceptibility (LS) model for extreme rainfall-induced landslide events. The researches focuses on the comparison and applicability of LS models based on four methods, including landslide ratio-based logistic regression (LRBLR), frequency ratio (FR), weight of evidence (WOE), and instability index (II) methods, in an extreme rainfall-induced landslide cases. The landslide inventory in the Chishan river watershed, Southwestern Taiwan, after 2009 Typhoon Morakot is the main materials in this research. The Chishan river watershed is a tributary watershed of Kaoping river watershed, which is a landslide- and erosion-prone watershed with the annual average suspended load of 3.6×107 MT/yr (ranks 11th in the world). Typhoon Morakot struck Southern Taiwan from Aug. 6-10 in 2009 and dumped nearly 2,000 mm of rainfall in the Chishan river watershed. The 24-hour, 48-hour, and 72-hours accumulated rainfall in the Chishan river watershed exceeded the 200-year return period accumulated rainfall. 2,389 landslide polygons in the Chishan river watershed were extracted from SPOT 5 images after 2009 Typhoon Morakot. The total landslide area is around 33.5 km2, equals to the landslide ratio of 4.1%. The main landslide types based on Varnes' (1978) classification are rotational and translational slides. The two characteristics of extreme rainfall-induced landslide event are dense landslide distribution and large occupation of downslope landslide areas owing to headward erosion and bank erosion in the flooding processes. The area of downslope landslide in the Chishan river watershed after 2009 Typhoon Morakot is 3.2 times higher than that of upslope landslide areas. The prediction accuracy of LS models based on LRBLR, FR, WOE, and II methods have been proven over 70%. The model performance and applicability of four models in a landslide-prone watershed with dense distribution of rainfall

Predictive susceptibility analysis of typhoon induced landslides in Central Taiwan

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Shou, Keh-Jian; Lin, Zora

2017-04-01

Climate change caused by global warming affects Taiwan significantly for the past decade. The increasing frequency of extreme rainfall events, in which concentrated and intensive rainfalls generally cause geohazards including landslides and debris flows. The extraordinary, such as 2004 Mindulle and 2009 Morakot, hit Taiwan and induced serious flooding and landslides. This study employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to understand the temporal rainfall trends, distributions, and intensities in the adopted Wu River watershed in Central Taiwan. To assess the spatial hazard of the landslides, landslide susceptibility analysis was also applied. Different types of rainfall factors were tested in the susceptibility models for a better accuracy. In addition, the routes of typhoons were also considered in the predictive analysis. The results of predictive analysis can be applied for risk prevention and management in the study area.

An integrated approach coupling physically based models and probabilistic method to assess quantitatively landslide susceptibility at different scale: application to different geomorphological environments

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Vandromme, Rosalie; Thiéry, Yannick; Sedan, Olivier; Bernardie, Séverine

2016-04-01

Landslide hazard assessment is the estimation of a target area where landslides of a particular type, volume, runout and intensity may occur within a given period. The first step to analyze landslide hazard consists in assessing the spatial and temporal failure probability (when the information is available, i.e. susceptibility assessment). Two types of approach are generally recommended to achieve this goal: (i) qualitative approach (i.e. inventory based methods and knowledge data driven methods) and (ii) quantitative approach (i.e. data-driven methods or deterministic physically based methods). Among quantitative approaches, deterministic physically based methods (PBM) are generally used at local and/or site-specific scales (1:5,000-1:25,000 and >1:5,000, respectively). The main advantage of these methods is the calculation of probability of failure (safety factor) following some specific environmental conditions. For some models it is possible to integrate the land-uses and climatic change. At the opposite, major drawbacks are the large amounts of reliable and detailed data (especially materials type, their thickness and the geotechnical parameters heterogeneity over a large area) and the fact that only shallow landslides are taking into account. This is why they are often used at site-specific scales (> 1:5,000). Thus, to take into account (i) materials' heterogeneity , (ii) spatial variation of physical parameters, (iii) different landslide types, the French Geological Survey (i.e. BRGM) has developed a physically based model (PBM) implemented in a GIS environment. This PBM couples a global hydrological model (GARDENIA®) including a transient unsaturated/saturated hydrological component with a physically based model computing the stability of slopes (ALICE®, Assessment of Landslides Induced by Climatic Events) based on the Morgenstern-Price method for any slip surface. The variability of mechanical parameters is handled by Monte Carlo approach. The

Database Organisation in a Web-Enabled Free and Open-Source Software (foss) Environment for Spatio-Temporal Landslide Modelling

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Das, I.; Oberai, K.; Sarathi Roy, P.

2012-07-01

Landslides exhibit themselves in different mass movement processes and are considered among the most complex natural hazards occurring on the earth surface. Making landslide database available online via WWW (World Wide Web) promotes the spreading and reaching out of the landslide information to all the stakeholders. The aim of this research is to present a comprehensive database for generating landslide hazard scenario with the help of available historic records of landslides and geo-environmental factors and make them available over the Web using geospatial Free & Open Source Software (FOSS). FOSS reduces the cost of the project drastically as proprietary software's are very costly. Landslide data generated for the period 1982 to 2009 were compiled along the national highway road corridor in Indian Himalayas. All the geo-environmental datasets along with the landslide susceptibility map were served through WEBGIS client interface. Open source University of Minnesota (UMN) mapserver was used as GIS server software for developing web enabled landslide geospatial database. PHP/Mapscript server-side application serve as a front-end application and PostgreSQL with PostGIS extension serve as a backend application for the web enabled landslide spatio-temporal databases. This dynamic virtual visualization process through a web platform brings an insight into the understanding of the landslides and the resulting damage closer to the affected people and user community. The landslide susceptibility dataset is also made available as an Open Geospatial Consortium (OGC) Web Feature Service (WFS) which can be accessed through any OGC compliant open source or proprietary GIS Software.

Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey by artificial neural networks

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M. Ercanoglu

2005-01-01

Full Text Available Landslides are significant natural hazards in Turkey, second only to earthquakes with respect to economic losses and casualties. The West Black Sea region of Turkey is known as one of the most landslide-prone regions in the country. The work presented in this paper is aimed at evaluating landslide susceptibility in a selected area in the West Black Sea region using Artificial Neural Network (ANN method. A total of 317 landslides were identified and mapped in the area by extensive field work and by use of air photo interpretations to build a landslide inventory map. A landslide database was then derived automatically from the landslide inventory map. To evaluate landslide susceptibility, six input parameters (slope angle, slope aspect, topographical elevation, topographical shape, wetness index, and vegetation index were used. To obtain maps of these parameters, Digital Elevation Model (DEM and ASTER satellite imagery of the study area were used. At the first stage, all data were normalized in [0, 1] interval, and parameter effects on landslide occurrence were expressed using Statistical Index values (Wi. Then, landslide susceptibility analyses were performed using an ANN. Finally, performance of the resulting map and the applied methodology is discussed relative to performance indicators, such as predicted areal extent of landslides and the strength of relation (rij value. Much of the areal extents of the landslides (87.2% were classified as susceptible to landsliding, and rij value of 0.85 showed a high degree of similarity. In addition to these, at the final stage, an independent validation strategy was followed by dividing the landslide data set into two parts and 82.5% of the validation data set was found to be correctly classified as landslide susceptible areas. According to these results, it is concluded that the map produced by the ANN is reliable and methodology applied in the study produced high performance, and satisfactory results.

Multi-Collinearity Based Model Selection for Landslide Susceptibility Mapping: A Case Study from Ulus District of Karabuk, Turkey

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Sahin, E. K.; Colkesen, I., , Dr; Kavzoglu, T.

2017-12-01

Identification of localities prone to landslide areas plays an important role for emergency planning, disaster management and recovery planning. Due to its great importance for disaster management, producing accurate and up-to-date landslide susceptibility maps is essential for hazard mitigation purpose and regional planning. The main objective of the present study was to apply multi-collinearity based model selection approach for the production of a landslide susceptibility map of Ulus district of Karabuk, Turkey. It is a fact that data do not contain enough information to describe the problem under consideration when the factors are highly correlated with each other. In such cases, choosing a subset of the original features will often lead to better performance. This paper presents multi-collinearity based model selection approach to deal with the high correlation within the dataset. Two collinearity diagnostic factors (Tolerance (TOL) and the Variance Inflation Factor (VIF)) are commonly used to identify multi-collinearity. Values of VIF that exceed 10.0 and TOL values less than 1.0 are often regarded as indicating multi-collinearity. Five causative factors (slope length, curvature, plan curvature, profile curvature and topographical roughness index) were found highly correlated with each other among 15 factors available for the study area. As a result, the five correlated factors were removed from the model estimation, and performances of the models including the remaining 10 factors (aspect, drainage density, elevation, lithology, land use/land cover, NDVI, slope, sediment transport index, topographical position index and topographical wetness index) were evaluated using logistic regression. The performance of prediction model constructed with 10 factors was compared to that of 15-factor model. The prediction performance of two susceptibility maps was evaluated by overall accuracy and the area under the ROC curve (AUC) values. Results showed that overall

The preparation of landslide map by Landslide Numerical Risk Factor (LNRF model and Geographic Information System (GIS

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Ali Mohammadi Torkashvand

2014-12-01

Full Text Available One of the risks to threaten mountainous areas is that hillslope instability caused damage to lands. One of the most dangerous instabilities is mass movement and much movement occurs due to slip. The aim of this study is zonation of landslide hazards in a basin of the Ardebil province, the eastern slopes of Sabalan, Iran. Geological and geomorphologic conditions, climate and type of land use have caused susceptibility of this watershed to landslides. Firstly, maps of the main factors affecting landslide occurrence including slope, distance from faults, lithology, elevation and precipitation were prepared and digitized. Then, by using interpretation of aerial photos and satellite images and field views, the ground truth map of landslides was prepared. Each basic layer (factor and landslide map were integrated to compute the numeric value of each factor with the help of a Landslide Numerical Risk Factor (LNRF model and landslide occurrence percent obtained in different units from each of the maps. Finally, with overlapping different data layers, a landslide hazard zonation map was prepared. Results showed that 67.85% of the basin has high instability, 7.76% moderate instability and 24.39% low instability.

Establish susceptibility and risk assessment models for rainfall-induced landslide: A case in Central Taiwan

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Wu, Chunhung; Huang, Jyuntai

2017-04-01

Most of the landslide cases in Taiwan were triggered by rainfall or earthquake events. The heavy rainfall in the typhoon seasons, from June to October, causes the landslide hazard more serious. Renai Towhship is of the most large landslide cases after 2009 Typhoon Morakot (from Aug. 5 to Aug. 10, 2009) in Taiwan. Around 2,744 landslides cases with the total landslide area of 21.5 km2 (landslide ratio =1.8%), including 26 large landslide cases, induced after 2009 Typhoon Morakot in Renai Towhship. The area of each large landslides case is more than 0.1 km2, and the area of the largest case is around 0.96 km2. 58% of large landslide cases locate in the area with metamorphosed sandstone. The mean slope of 26 large landslide cases ranges from 15 degree to 56 degree, and the accumulated rainfall during 2009 Typhoon Morakot ranges from 530 mm to 937 mm. Three methods, including frequency ratio method (abbreviated as FR), weights of evidence method (abbreviated as WOE), and logistic regression method (abbreviated as LR), are used in this study to establish the landslides susceptibility in the Renai Township, Nantou County, Taiwan. Eight landslide related-factors, including elevation, slope, aspect, geology, land use, distance to drainage, distance to fault, accumulation rainfall during 2009 Typhoon Morakot, are used to establish the landslide susceptibility models in this study. The landslide inventory after 2009 Typhoon Morakot is also used to test the model performance in this study. The mean accumulated rainfall in Renai Township during 2009 typhoon Morakot was around 735 mm with the maximum 1-hr, 3-hrs, and 6-hrs rainfall intensity of 44 mm/1-hr, 106 mm/3-hrs and 204 mm/6-hrs, respectively. The range of original susceptibility values established by three methods are 4.0 to 20.9 for FR, -33.8 to -16.1 for WOE, and -41.7 to 5.7 for LR, and the mean landslide susceptibility value are 8.0, -24.6 and 0.38, respectively. The AUC values are 0.815 for FR, 0.816 for WOE, and 0

Progress in national-scale landslide susceptibility mapping in Romania using a combined statistical-heuristical approach

Science.gov (United States)

Bălteanu, Dan; Micu, Mihai; Malet, Jean-Philippe; Jurchescu, Marta; Sima, Mihaela; Kucsicsa, Gheorghe; Dumitrică, Cristina; Petrea, Dănuţ; Mărgărint, Ciprian; Bilaşco, Ştefan; Văcăreanu, Radu; Georgescu, Sever; Senzaconi, Francisc

2017-04-01

Landslide processes represent a very widespread geohazard in Romania, affecting mainly the hilly and plateau regions as well as the mountain sectors developed on flysch formations. Two main projects provided the framework for improving the existing national landslide susceptibility map (Bălteanu et al. 2010): the ELSUS (Pan-European and nation-wide landslide susceptibility assessment, EC-CERG) and the RO-RISK (Disaster Risk Evaluation at National Level, ESF-POCA) projects. The latter one, a flagship project aiming at strengthening risk prevention and management in Romania, focused on a national-level evaluation of the main risks in the country including landslides. The strategy for modeling landslide susceptibility was designed based on the experience gained from continental and national level assessments conducted in the frame of the International Programme on Landslides (IPL) project IPL-162, the European Landslides Expert Group - JRC and the ELSUS project. The newly proposed landslide susceptibility model used as input a reduced set of landslide conditioning factor maps available at scales of 1:100,000 - 1:200,000 and consisting of lithology, slope angle and land cover. The input data was further differentiated for specific natural environments, defined here as morpho-structural units in order to incorporate differences induced by elevation (vertical climatic zonation), morpho-structure as well as neotectonic features. In order to best discern the specific landslide conditioning elements, the analysis has been carried out for one single process category, namely slides. The existence of a landslide inventory covering the whole country's territory ( 30,000 records, Micu et al. 2014), although affected by incompleteness and lack of homogeneity, allowed for the application of a semi-quantitative, mixed statistical-heuristical approach having the advantage of combining the objectivity of statistics with expert-knowledge in calibrating class and factor weights. The

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

Science.gov (United States)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross

A proposed cell model for multiple-occurrence regional landslide events: Implications for landslide susceptibility mapping

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Crozier, M. J.

2017-10-01

Multiple-occurrence regional landslide events (MORLEs) consist of hundreds to thousands of shallow landslides occurring more or less simultaneously within defined areas, ranging from tens to thousands of square kilometres. While MORLEs can be triggered by rainstorms and earthquakes, this paper is confined to those landslide events triggered by rainstorms. Globally, MORLEs occur in a range of geological settings in areas of moderate to steep slopes subject to intense rainstorms. Individual landslides in rainstorm-triggered events are dominantly small, shallow debris and earth flows, and debris and earth slides involving regolith or weathered bedrock. The model used to characterise these events assumes that energy distribution within the event area is represented on the land surface by a cell structure; with maximum energy expenditure within an identifiable core and rapid dissipation concentrically away from the centre. The version of the model presented here has been developed for rainfall-triggered landslide events. It proposes that rainfall intensity can be used to determine different critical landslide response zones within the cell (referred to as core, middle, and periphery zones). These zones are most readily distinguished by two conditions: the proportion of the slope that fails and the particular type of the slope stability factor that assumes dominance in determining specific sites of landslide occurrence. The latter condition means that the power of any slope stability factor to distinguish between stable and unstable sites varies throughout the affected area in accordance with the landslide response zones within the cell; certain factors critical for determining the location of landslide sites in one part of the event area have little influence in other parts of the event area. The implication is that landslide susceptibility maps (and subsequently derived mitigation measures) based on conventional slope stability factors may have only limited validity

Landslide susceptibility analysis using Probabilistic Certainty Factor ...

Indian Academy of Sciences (India)

done using many different methods and techniques. A detailed outline of .... of depressions where water is accumulated, espe- cially when the ..... The two decision rules that must be satisfied for a good landslide .... making the susceptibility zonation relative. This is ..... tional Conference on Imaging Systems and Techniques,.

Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods

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V. Vakhshoori

2016-09-01

Full Text Available A regional scale basin susceptible to landslide located in Qaemshahr area in northern Iran was chosen for comparing the reliability of weight of evidence (WofE, fuzzy logic, and frequency ratio (FR methods for landslide susceptibility mapping. The locations of 157 landslides were identified using Google Earth® or extracted from archived data, from which, 22 rockslides were eliminated from the data-set due to their different conditions. The 135 remaining landslides were randomly divided into two groups of modelling (70% and validation (30% data-sets. Elevation, slope degree, slope aspect, lithology, land use/cover, normalized difference vegetation index, rainfall, distance to drainage network, roads, and faults were considered as landslide causative factors. The landslide susceptibility maps were prepared using the three mentioned methods. The validation process was measured by the success and prediction rates calculated by area under receiver operating characteristic curve. The ‘OR’, ‘AND’, ‘SUM’, and ‘PRODUCT’ operators of the fuzzy logic method were unacceptable because these operators classify the target area into either very high or very low susceptible zones that are inconsistent with the physical conditions of the study area. The results of fuzzy ‘GAMMA’ operators were relatively reliable while, FR and WofE methods showed results that are more reliable.

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

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Duman, T. Y.; Can, T.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.

2006-11-01

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.

Land use change and landslide characteristics analysis for community-based disaster mitigation.

Science.gov (United States)

Chen, Chien-Yuan; Huang, Wen-Lin

2013-05-01

On August 8, 2009, Typhoon Morakot brought heavy rain to Taiwan, causing numerous landslides and debris flows in the Taihe village area of Meishan Township, Chiayi County, in south-central Taiwan. In the Taihe land is primary used for agriculture and land use management may be a factor in the area's landslides. This study explores Typhoon Morakot-induced landslides and land use changes between 1999 and 2009 using GIS with the aid of field investigation. Spot 5 satellite images with a resolution of 2.5 m are used for landslide interpretation and manually digitalized in GIS. A statistical analysis for landslide frequency-area distribution was used to identify the landslide characteristics associated with different types of land use. There were 243 landslides with a total area of 2.75 km(2) in the study area. The area is located in intrinsically fragile combinations of sandstone and shale. Typhoon Morakot-induced landslides show a power-law distribution in the study area. Landslides were mainly located in steep slope areas containing natural forest and in areas planted with bamboo, tea, and betel nut. Land covered with natural forest shows the highest landslide ratio, followed by bamboo, betel nut, and tea. Landslides thus show a higher ratio in areas planted with shallow root vegetation such as bamboo, betel nut, and tea. Furthermore, the degree of basin development is proportional to the landslide ratio. The results show that a change in vegetation cover results in a modified landslide area and frequency and changed land use areas have higher landslide ratios than non-changed. Land use management and community-based disaster prevention are needed in mountainous areas of Taiwan for hazard mitigation.

The National Landslide Database of Great Britain: Acquisition, communication and the role of social media

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Pennington, Catherine; Freeborough, Katy; Dashwood, Claire; Dijkstra, Tom; Lawrie, Kenneth

2015-11-01

The British Geological Survey (BGS) is the national geological agency for Great Britain that provides geoscientific information to government, other institutions and the public. The National Landslide Database has been developed by the BGS and is the focus for national geohazard research for landslides in Great Britain. The history and structure of the geospatial database and associated Geographical Information System (GIS) are explained, along with the future developments of the database and its applications. The database is the most extensive source of information on landslides in Great Britain with over 17,000 records of landslide events to date, each documented as fully as possible for inland, coastal and artificial slopes. Data are gathered through a range of procedures, including: incorporation of other databases; automated trawling of current and historical scientific literature and media reports; new field- and desk-based mapping technologies with digital data capture, and using citizen science through social media and other online resources. This information is invaluable for directing the investigation, prevention and mitigation of areas of unstable ground in accordance with Government planning policy guidelines. The national landslide susceptibility map (GeoSure) and a national landslide domains map currently under development, as well as regional mapping campaigns, rely heavily on the information contained within the landslide database. Assessing susceptibility to landsliding requires knowledge of the distribution of failures, an understanding of causative factors, their spatial distribution and likely impacts, whilst understanding the frequency and types of landsliding present is integral to modelling how rainfall will influence the stability of a region. Communication of landslide data through the Natural Hazard Partnership (NHP) and Hazard Impact Model contributes to national hazard mitigation and disaster risk reduction with respect to weather and

A GIS-Based Procedure for Landslide Intensity Evaluation and Specific risk Analysis Supported by Persistent Scatterers Interferometry (PSI

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Silvia Bianchini

2017-10-01

Full Text Available The evaluation of landslide specific risk, defined as the expected degree of loss due to landslides, requires the parameterization and the combination of a number of socio-economic and geological factors, which often needs the interaction of different skills and expertise (geologists, engineers, planners, administrators, etc.. The specific risk sub-components, i.e., hazard and vulnerability of elements at risk, can be determined with different levels of detail depending on the available auxiliary data and knowledge of the territory. These risk factors are subject to short-term variations and nowadays turn out to be easily mappable and evaluable through remotely sensed data and GIS (Geographic Information System tools. In this work, we propose a qualitative approach at municipal scale for producing a “specific risk” map, supported by recent satellite PSI (Persistent Scatterer Interferometry data derived from SENTINEL-1 C-band images in the spanning time 2014–2017, implemented in a GIS environment. In particular, PSI measurements are useful for the updating of a landslide inventory map of the area of interest and are exploited for the zonation map of the intensity of ground movements, needed for evaluating the vulnerability over the study area. Our procedure is presented throughout the application to the Volterra basin and the output map could be useful to support the local authorities with updated basic information required for environmental knowledge and planning at municipal level. Moreover, the proposed procedure is easily managed and repeatable in other case studies, as well as exploiting different SAR sensors in L- or X-band.

DTMs Assessment to the Definition of Shallow Landslides Prone Areas

Science.gov (United States)

Martins, Tiago D.; Oka-Fiori, Chisato; Carvalho Vieira, Bianca; Montgomery, David R.

2017-04-01

Predictive methods have been developed, especially since the 1990s, to identify landslide prone areas. One of the examples it is the physically based model SHALSTAB (Shallow Landsliding Stability Model), that calculate the potential instability for shallow landslides based on topography and physical soil properties. Normally, in such applications in Brazil, the Digital Terrain Model (DTM), is obtained mainly from conventional contour lines. However, recently the LiDAR (Light Detection and Ranging) system has been largely used in Brazil. Thus, this study aimed to evaluate different DTM's, generated from conventional data and LiDAR, and their influence in generating susceptibility maps to shallow landslides using SHALSTAB model. For that were analyzed the physical properties of soil, the response of the model when applying conventional topographical data and LiDAR's in the generation of DTM, and the shallow landslides susceptibility maps based on different topographical data. The selected area is in the urban perimeter of the municipality of Antonina (PR), affected by widespread landslides in March 2011. Among the results, it was evaluated different LiDAR data interpolation, using GIS tools, wherein the Triangulation/Natural Neighbor presented the best performance. It was also found that in one of evaluation indexes (Scars Concentration), the LiDAR derived DTM presented the best performance when compared with the one originated from contour lines, however, the Landslide Potential index, has presented a small increase. Consequently, it was possible to assess the DTM's, and the one derived from LiDAR improved very little the certitude percentage. It is also noted a gap in researches carried out in Brazil on the use of products generated from LiDAR data on geomorphological analysis.

Impact of geo-environmental factors on landslide susceptibility using an AHP method: A case study of Fruška Gora Mt., Serbia

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Marjanović Miloš

2013-01-01

Full Text Available The paper considers the outcome of multi-criteria analysis of landslide susceptibility on the NW outskirts of Fruška Gora Mountain, Serbia. The area of the interest is known for landslide occurrences, and to focus on the most affected areas, it was necessary to consider some principal factors (lithology, slope inclination, rainfall, erosion, vegetation, altitude and slope aspect and sort them by their importance to the phenomena. Prior to any criteria assessment, available data records had been assembled and refashioned as raster datasets. Thereafter, the criteria arising from an analytical hierarchy process (AHP provided their weights of preference in the final model. In addition, the model was analyzed for the information gain and classified in accordance to the optimal informativeness. Being tailored in the context of raster modelling, aided by the GIS spatial tools, our result gained substantial correlation to the control reference map (a digital photo-geological interpretation map of active and potential landslides.

A comparative study on the landslide susceptibility mapping using ...

Indian Academy of Sciences (India)

from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power ... economic losses and creating high maintenance costs, are ..... evidence method to landslide susceptibility map- ping using ...

Spatial Analysis GIS Model for Identifying the Risk Induced by Landslides. A Case Study: A.T.U. of Șieu

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Dorel Colniţă

2016-11-01

Full Text Available The risk induced by landslides on residential infrastructure, transport infrastructure and agricultural land causes problems of local management that need to be solved by reducing negative effects and decrease the frequency of their occurrence. This study followed the development and implementation of a model for identifying the risk induced by landslides through the analysis of spatial occurrence probability for landslides at the administrative territorial unit of Șieu, following the semi-quantitative method governed in Romania by G.D. no 447/2003 and then through the exposure of housing infrastructure at landslides was possible to frame landslides on risk classes. The entire approach was based on GIS spatial analysis, creating a specific detailed database of causing and triggering factors of landslides and not at least, a database for risk receptors, in this study, represented by the constructions of villages associated with the studied administrative territorial units. The final result of the model highlights the framing of constructions on qualitative risk classes at landslides, revealing the elements of infrastructure that need post and pre event measures of protection.

A preliminary regional assessment of earthquake-induced landslide susceptibility for Vrancea Seismic Region

Science.gov (United States)

Micu, Mihai; Balteanu, Dan; Ionescu, Constantin; Havenith, Hans; Radulian, Mircea; van Westen, Cees; Damen, Michiel; Jurchescu, Marta

2015-04-01

) with head scarps near mountain tops and close to faults is similar to the one of large mass movements for which a seismic origin is proved (such as in the Tien Shan, Pamir, Longmenshan, etc.). Thus, correlations between landslide occurrence and combined seismotectonic and climatic factors are needed to support a regional multi-hazard risk assessment. The purpose of this paper is to harmonize for the first time at a regional scale the landslide predisposing factors and seismotectonic triggers and to present a first qualitative insight into the earthquake-induced landslide susceptibility for the Vrancea Seismic Region in terms of a GIS-based analysis of Newmark displacement (ND). In this way, it aims at better defining spatial and temporal distribution patterns of earthquake-triggered landslides. Arias Intensity calculation involved in the assessment considers both regional seismic hazard aspects and singular earthquake scenarios (adjusted by topography amplification factors). The known distribution of landslides mapped through digital stereographic interpretation of high-resolution aerial photos is compared with digital active fault maps and the computed ND maps to statistically outline the seismotectonic influence on slope stability in the study area. The importance of this approach resides in two main outputs. The fist one, of a fundamental nature, by providing the first regional insight into the seismic landslides triggering framework, is allowing us to understand if deep-focus earthquakes may trigger massive slope failures in an area with a relatively smooth relief (compared to the high mountain regions in Central Asia, the Himalayas), considering possible geologic and topographic site effects. The second one, more applied, will allow a better accelerometer instrumentation and monitoring of slopes and also will provide a first correlation of different levels of seismic shaking with precipitation recurrences, an important relationship within a multi-hazard risk

Landslide early warning system prototype with GIS analysis indicates by soil movement and rainfall

Science.gov (United States)

Artha, Y.; Julian, E. S.

2018-01-01

The aim of this paper is developing and testing of landslide early warning system. The early warning system uses accelerometersas ground movement and tilt-sensing device and a water flow sensor. A microcentroller is used to process the input signal and activate the alarm. An LCD is used to display the acceleration in x,y and z axis. When the soil moved or shifted and rainfall reached 100 mm/day, the alarm rang and signal were sentto the monitoring center via a telemetry system.Data logging information and GIS spatial data can be monitored remotely as tables and graphics as well as in the form of geographical map with the help of web-GIS interface. The system were tested at Kampung Gerendong, Desa Putat Nutug, Kecamatan Ciseeng, Kabupaten Bogor. This area has 3.15 cumulative score, which mean vulnerable to landslide. The results show that the early warning system worked as planned.

Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea

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Sunmin Lee

2017-07-01

Full Text Available The application of data mining models has become increasingly popular in recent years in assessments of a variety of natural hazards such as landslides and floods. Data mining techniques are useful for understanding the relationships between events and their influencing variables. Because landslides are influenced by a combination of factors including geomorphological and meteorological factors, data mining techniques are helpful in elucidating the mechanisms by which these complex factors affect landslide events. In this study, spatial data mining approaches based on data on landslide locations in the geographic information system environment were investigated. The topographical factors of slope, aspect, curvature, topographic wetness index, stream power index, slope length factor, standardized height, valley depth, and downslope distance gradient were determined using topographical maps. Additional soil and forest variables using information obtained from national soil and forest maps were also investigated. A total of 17 variables affecting the frequency of landslide occurrence were selected to construct a spatial database, and support vector machine (SVM and artificial neural network (ANN models were applied to predict landslide susceptibility from the selected factors. In the SVM model, linear, polynomial, radial base function, and sigmoid kernels were applied in sequence; the model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy, respectively. The ANN model yielded a validity accuracy of 78.41%. The results of this study are useful in guiding effective strategies for the prevention and management of landslides in urban areas.

Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping

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Rubini Mahalingam

2016-11-01

Full Text Available Landslides are a major geohazard, which result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping can help communities plan and prepare for these damaging events. Digital elevation models (DEMs are one of the most important data-sets used in landslide hazard assessment. Despite their frequent use, limited research has been completed to date on how the DEM source and spatial resolution can influence the accuracy of the produced landslide susceptibility maps. The aim of this paper is to analyse the influence of spatial resolutions and source of DEMs on landslide susceptibility mapping. For this purpose, Advanced Spaceborne Thermal Emission and Reflection (ASTER, National Elevation Dataset (NED, and Light Detection and Ranging (LiDAR DEMs were obtained for two study sections of approximately 140 km2 in north-west Oregon. Each DEM was resampled to 10, 30, and 50 m and slope and aspect grids were derived for each resolution. A set of nine spatial databases was constructed using geoinformation science (GIS for each of the spatial resolution and source. Additional factors such as distance to river and fault maps were included. An analytical hierarchical process (AHP, fuzzy logic model, and likelihood ratio-AHP representing qualitative, quantitative, and hybrid landslide mapping techniques were used for generating landslide susceptibility maps. The results from each of the techniques were verified with the Cohen's kappa index, confusion matrix, and a validation index based on agreement with detailed landslide inventory maps. The spatial resolution of 10 m, derived from the LiDAR data-set showed higher predictive accuracy in all the three techniques used for producing landslide susceptibility maps. At a resolution of 10 m, the output maps based on NED and ASTER had higher misclassification compared to the LiDAR-based outputs. Further, the 30-m LiDAR output showed improved results over the 10-m NED and 10-m

Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

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Yongliang Lin

2016-10-01

Full Text Available In this paper, we propose a multiple kernel relevance vector machine (RVM method based on the adaptive cloud particle swarm optimization (PSO algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC and landslide dot density (LDD were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions had a larger area under the ROC curve (0.7616 and a lower prediction error ratio (0.28% than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km2 and the sum of two low susceptibility zone LDDs (0.82/100 km2 demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area.

Regional landslide susceptibility assessment using multi-stage remote sensing data along the coastal range highway in northeastern Taiwan

Science.gov (United States)

Lee, Ching-Fang; Huang, Wei-Kai; Chang, Yu-Lin; Chi, Shu-Yeong; Liao, Wu-Chang

2018-01-01

Typhoons Megi (2010) and Saola (2012) brought torrential rainfall which triggered regional landslides and flooding hazards along Provincial Highway No. 9 in northeastern Taiwan. To reduce property loss and saving lives, this study combines multi-hazard susceptibility assessment with environmental geology map a rock mass rating system (RMR), remote sensing analysis, and micro-topography interpretation to develop an integrated landslide hazard assessment approach and reflect the intrinsic state of slopeland from the past toward the future. First, the degree of hazard as indicated by historical landslides was used to determine many landslide regions in the past. Secondly, geo-mechanical classification of rock outcroppings was performed by in-situ investigation along the vulnerable road sections. Finally, a high-resolution digital elevation model was extracted from airborne LiDAR and multi-temporal remote sensing images which was analyzed to discover possible catastrophic landslide hotspot shortly. The results of the analysis showed that 37% of the road sections in the study area were highly susceptible to landslide hazards. The spatial distribution of the road sections revealed that those characterized by high susceptibility were located near the boundaries of fault zones and in areas of lithologic dissimilarity. Headward erosion of gullies and concave-shaped topographic features had an adverse effect and was the dominant factor triggering landslides. Regional landslide reactivation on this coastal highway are almost related to the past landslide region based on hazard statistics. The final results of field validation demonstrated that an accuracy of 91% could be achieved for forecasting geohazard followed by intense rainfall events and typhoons.

Rainfall thresholds and susceptibility mapping for shallow landslides and debris flows in Scotland

Science.gov (United States)

Postance, Benjamin; Hillier, John; Dijkstra, Tom; Dixon, Neil

2017-04-01

Shallow translational slides and debris flows (hereafter 'landslides') pose a significant threat to life and cause significant annual economic impacts (e.g. by damage and disruption of infrastructure). The focus of this research is on the definition of objective rainfall thresholds using a weather radar system and landslide susceptibility mapping. In the study area Scotland, an inventory of 75 known landslides was used for the period 2003 to 2016. First, the effect of using different rain records (i.e. time series length) on two threshold selection techniques in receiver operating characteristic (ROC) analysis was evaluated. The results show that thresholds selected by 'Threat Score' (minimising false alarms) are sensitive to rain record length and which is not routinely considered, whereas thresholds selected using 'Optimal Point' (minimising failed alarms) are not; therefore these may be suited to establishing lower limit thresholds and be of interest to those developing early warning systems. Robust thresholds are found for combinations of normalised rain duration and accumulation at 1 and 12 day's antecedence respectively; these are normalised using the rainy-day normal and an equivalent measure for rain intensity. This research indicates that, in Scotland, rain accumulation provides a better indicator than rain intensity and that landslides may be generated by threshold conditions lower than previously thought. Second, a landslide susceptibility map is constructed using a cross-validated logistic regression model. A novel element of the approach is that landslide susceptibility is calculated for individual hillslope sections. The developed thresholds and susceptibility map are combined to assess potential hazards and impacts posed to the national highway network in Scotland.

Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China

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Wei Chen

2017-11-01

Full Text Available A landslide susceptibility map plays an essential role in urban and rural planning. The main purpose of this study is to establish a variable-weighted linear combination model (VWLC and assess its potential for landslide susceptibility mapping. Firstly, different objective methods are employed for data processing rather than the frequently-used subjective judgments: K-means clustering is used for classification; binarization is introduced to determine buffer length thresholds for locational elements (road, river, and fault; landslide area density is adopted as the contribution index; and a correlation analysis is conducted for suitable factor selection. Secondly, considering the dimension changes of the preference matrix varying with the different locations of the mapping cells, the variable weights of each optimal factor are determined based on the improved analytic hierarchy process (AHP. On this basis, the VWLC model is established and applied to regional landslide susceptibility mapping for the Shennongjia Forestry District, China, where shallow landslides frequently occur. The obtained map is then compared with a map using the traditional WLC, and the results of the comparison show that VWLC is more reasonable, with a higher accuracy, and can be used anywhere that has the same or similar geological and topographical conditions.

Characterization of past landslides and slope susceptibility analysis for Lima and Callao provinces, Peru

Science.gov (United States)

Tatard, Lucile; Villacorta, Sandra; Metzger, Pascale

2013-04-01

85% of people exposed to earthquakes, hurricanes, floods and drought live in developing countries (IPU, 2010). This population is also exposed to the landslide risk as this phenomenon is mainly triggered by earthquakes and rainfall. There is an urgent need to propose methods to evaluate and mitigate the landslide risk for developing countries, where few studies were undergone and data, and information on data, are scarce. In this study, we characterize a landslide inventory set up for the megalopolis of Lima, Peru, by the local geological bureau (INGEMMET). This inventory was set up using satellite images and includes landslides of all ages. It is composed of two landslide types: rockfalls and debris flows (huaycos) that we investigate together and separately. First, we describe qualitatively the landslide occurrences in terms of geology, slope steepness, altitude, etc. We notably find that debris flows occur at altitudes larger than the ones of the rockfalls, probably due to the climatic conditions. Then we find that the rockfalls and debris flows area distributions follow a power law when investigated separately whereas it does not follow a power law when investigated together. This highlights a logical difference of mechanics between the two landslide types. Then, using the dimension of correlation D (Grassberger and Procaccia, 1983) we show that the event spatial occurrences are not uniformly distributed but clustered. It supports the existence of controlling parameters on the spatial occurrence of landslides and the research to identify them. Last, we investigate the relationships between different landslide parameters (geology, altitude, slope steepness, ...) using the linear correlation coefficient r, and we find that all these parameters are independent to each other. This allows us to investigate each parameter separately in terms of landslide susceptibility and to define values for which the landslide susceptibility is low, medium or high for each

Landslide Hazard-Prevention in Balakot Region, Pakistan

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Abdul Salam Soomro

2012-04-01

Full Text Available The earthquake triggered enormous landslides on 8th October 2005 in the various areas of Pakistan especially in Balakot Region. This research paper fulfilled the urge to develop alternative landslide planning based on the consideration of landslide preventing measures using GIS (Geographical Informaton Systems techniques. This specific type of land use planning differs from traditional type of land use planning due to consideration of the probable hazard of landslide disaster by applying zonation methodology for finding the appropriate suitable areas for various development purposes. The various parameters e.g. elevation, slope angle, forest/ vegetations, faults, landslide zones, and rainfall were utilized as GIS data themes in the vector format. The different GIS techniques were used: (i Clipping the data layers; (ii Spatial analysis by converting the vector layers into raster format; (iii Classification of data themes into certain classes; (iv Overlaying the data themes and (v Map calculation techniques through GIS standard software. This applied research has found that various different regions such as high suitable, moderate suitable, low suitable and unsuitable may be considered as preventive measures from the probable hazard of the landslide disaster in future for rehabilitation and redevelopment purpose which can save human lives, residential and commercial infrastructure in future. It is believed that the various predicted regions for preventing landslide hazards can be very beneficial to the decision makers for the redevelopment of the region in future.

Landslide Hazard-Prevention in Balakot Region, Pakistan

International Nuclear Information System (INIS)

Soomro, A.S.

2011-01-01

The earthquake triggered enormous landslides on October 8, 2005 in the various areas of Pakistan especially in Balakot Region. This research paper fulfilled the urge to develop alternative landslide planning based on the consideration of landslide preventing measures using GIS (Geographical Information Systems) techniques. This specific type of land use planning differs from traditional type of land use planning due to consideration of the probable hazard of landslide disaster by applying zonation methodology for finding the appropriate suitable areas for various development purposes. The various parameters e.g. elevation, slope angle, forest/ vegetations, faults, landslide zones, and rainfall were utilized as GIS data themes in the vector format. The different GIS techniques were used: (i) Clipping the data layers; (II) Spatial analysis by converting the vector layers into raster format; (III) Classification of data themes into certain classes; (IV) Overlaying the data themes and (v) Map calculation techniques through GIS standard software. This applied research has found that various different regions such as high suitable, moderate suitable, low suitable and unsuitable may be considered as preventive measures from the probable hazard of the landslide disaster in future for rehabilitation and redevelopment purpose which can save human lives, residential and commercial infrastructure in future. It is believed that the various predicted regions for preventing landslide hazards can be very beneficial to the decision makers for the redevelopment of the region in future. (author)

Reprint of "A proposed cell model for multiple-occurrence regional landslide events: Implications for landslide susceptibility mapping"

Science.gov (United States)

Crozier, M. J.

2018-04-01

Multiple-occurrence regional landslide events (MORLEs) consist of hundreds to thousands of shallow landslides occurring more or less simultaneously within defined areas, ranging from tens to thousands of square kilometres. While MORLEs can be triggered by rainstorms and earthquakes, this paper is confined to those landslide events triggered by rainstorms. Globally, MORLEs occur in a range of geological settings in areas of moderate to steep slopes subject to intense rainstorms. Individual landslides in rainstorm-triggered events are dominantly small, shallow debris and earth flows, and debris and earth slides involving regolith or weathered bedrock. The model used to characterise these events assumes that energy distribution within the event area is represented on the land surface by a cell structure; with maximum energy expenditure within an identifiable core and rapid dissipation concentrically away from the centre. The version of the model presented here has been developed for rainfall-triggered landslide events. It proposes that rainfall intensity can be used to determine different critical landslide response zones within the cell (referred to as core, middle, and periphery zones). These zones are most readily distinguished by two conditions: the proportion of the slope that fails and the particular type of the slope stability factor that assumes dominance in determining specific sites of landslide occurrence. The latter condition means that the power of any slope stability factor to distinguish between stable and unstable sites varies throughout the affected area in accordance with the landslide response zones within the cell; certain factors critical for determining the location of landslide sites in one part of the event area have little influence in other parts of the event area. The implication is that landslide susceptibility maps (and subsequently derived mitigation measures) based on conventional slope stability factors may have only limited validity

Landslide susceptibility zonation in part of Tehri reservoir region ...

Indian Academy of Sciences (India)

Validation of the model was performed by using cumulative ..... thrusts, folds and joints of varying shape and size ...... Lee S, Choi J and Min K 2002 Landslide susceptibility analy- ... cross-validation in three test areas using a frequency.

A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

Science.gov (United States)

Ozdemir, Adnan; Altural, Tolga

2013-03-01

This study evaluated and compared landslide susceptibility maps produced with three different methods, frequency ratio, weights of evidence, and logistic regression, by using validation datasets. The field surveys performed as part of this investigation mapped the locations of 90 landslides that had been identified in the Sultan Mountains of south-western Turkey. The landslide influence parameters used for this study are geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transportation capacity index, distance to drainage, distance to fault, drainage density, fault density, and spring density maps. The relationships between landslide distributions and these parameters were analysed using the three methods, and the results of these methods were then used to calculate the landslide susceptibility of the entire study area. The accuracy of the final landslide susceptibility maps was evaluated based on the landslides observed during the fieldwork, and the accuracy of the models was evaluated by calculating each model's relative operating characteristic curve. The predictive capability of each model was determined from the area under the relative operating characteristic curve and the areas under the curves obtained using the frequency ratio, logistic regression, and weights of evidence methods are 0.976, 0.952, and 0.937, respectively. These results indicate that the frequency ratio and weights of evidence models are relatively good estimators of landslide susceptibility in the study area. Specifically, the results of the correlation analysis show a high correlation between the frequency ratio and weights of evidence results, and the frequency ratio and logistic regression methods exhibit correlation coefficients of 0.771 and 0.727, respectively. The frequency ratio model is simple, and its input, calculation and output processes are

Comparative study of landslides susceptibility mapping methods: Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN)

Science.gov (United States)

Salleh, S. A.; Rahman, A. S. A. Abd; Othman, A. N.; Mohd, W. M. N. Wan

2018-02-01

As different approach produces different results, it is crucial to determine the methods that are accurate in order to perform analysis towards the event. This research aim is to compare the Rank Reciprocal (MCDM) and Artificial Neural Network (ANN) analysis techniques in determining susceptible zones of landslide hazard. The study is based on data obtained from various sources such as local authority; Dewan Bandaraya Kuala Lumpur (DBKL), Jabatan Kerja Raya (JKR) and other agencies. The data were analysed and processed using Arc GIS. The results were compared by quantifying the risk ranking and area differential. It was also compared with the zonation map classified by DBKL. The results suggested that ANN method gives better accuracy compared to MCDM with 18.18% higher accuracy assessment of the MCDM approach. This indicated that ANN provides more reliable results and it is probably due to its ability to learn from the environment thus portraying realistic and accurate result.

Estimating volume of deposits associated with landslides on volcanic landscapes in the SW flank of the volcano Pico de Orizaba, Puebla-Veracruz

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Gabriel Legorreta Paulín

2017-03-01

dangerous situation for more than 360 000 people living on the southern flank of the volcano Pico de Orizaba, where landslides along the hillslopes and the river system threaten towns like Cordova, Orizaba, Rio Blanco, Nogales and Ciudad Mendoza. Today the most common and dangerous landslides are associated with unconsolidated volcanic deposits and heavy seasonal rains. In this paper, the cause, distribution, and link between landslides and the volcanic landscape relief susceptibility are analyzed. Similarly, the volume of displaced material is estimated in order to characterize the landslide instability in volcanic terrains. The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano is selected to describe and analyze susceptible areas of gravitational processes. The study area allows to show a systematic methodology for landslide mapping and volume calculation in areas with scarce information. The methodology encompasses three main stages of analysis. In the first stage, background information is collected to provide context and establish a generalized characterization of landslide processes, landsforms and volumes within the study area. Background information includes the following maps: topographic, geologic, land use, climate, slope, slope curvature, contributing area, flow direction, saturation, reclassified hypsomety, reclassified slope, and morphography. By retrieval and on-off switching of the background information in the GIS, a base map is created to assist in the digitizing of landslides. The base map and the theoretical aspects of the geomorphological mapping help to develop a conceptual base of support for mapping landslides. Landslides are digitized directly into a geographic information system (GIS, and in parallel, a spatial geodatabase of landslides attributes (eg. size, volume, activity, landslide type, etc. is constructed. Previous landslide mapping in the study area is verified and new landslides are added to the landslide

Integration of spatial and temporal data for the definition of different landslide hazard scenarios in the area north of Lisbon (Portugal

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J. L. Zêzere

2004-01-01

Full Text Available A general methodology for the probabilistic evaluation of landslide hazard is applied, taking in account both the landslide susceptibility and the instability triggering factors, mainly rainfall. The method is applied in the Fanhões-Trancão test site (north of Lisbon, Portugal where 100 shallow translational slides were mapped and integrated into a GIS database. For the landslide susceptibility assessment it is assumed that future landslides can be predicted by statistical relationships between past landslides and the spatial data set of the predisposing factors (slope angle, slope aspect, transversal slope profile, lithology, superficial deposits, geomorphology, and land use. Susceptibility is evaluated using algorithms based on statistical/probabilistic analysis (Bayesian model over unique-condition terrain units in a raster basis. The landslide susceptibility map is prepared by sorting all pixels according to the pixel susceptibility value in descending order. In order to validate the results of the susceptibility ana- lysis, the landslide data set is divided in two parts, using a temporal criterion. The first subset is used for obtaining a prediction image and the second subset is compared with the prediction results for validation. The obtained prediction-rate curve is used for the quantitative interpretation of the initial susceptibility map. Landslides in the study area are triggered by rainfall. The integration of triggering information in hazard assessment includes (i the definition of thresholds of rainfall (quantity-duration responsible for past landslide events; (ii the calculation of the relevant return periods; (iii the assumption that the same rainfall patterns (quantity/duration which produced slope instability in the past will produce the same effects in the future (i.e. same types of landslides and same total affected area. The landslide hazard is present as the probability of each pixel to be affected by a slope movement

Assessment of landslide distribution map reliability in Niigata prefecture - Japan using frequency ratio approach

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Rahardianto, Trias; Saputra, Aditya; Gomez, Christopher

2017-07-01

Research on landslide susceptibility has evolved rapidly over the few last decades thanks to the availability of large databases. Landslide research used to be focused on discreet events but the usage of large inventory dataset has become a central pillar of landslide susceptibility, hazard, and risk assessment. Indeed, extracting meaningful information from the large database is now at the forth of geoscientific research, following the big-data research trend. Indeed, the more comprehensive information of the past landslide available in a particular area is, the better the produced map will be, in order to support the effective decision making, planning, and engineering practice. The landslide inventory data which is freely accessible online gives an opportunity for many researchers and decision makers to prevent casualties and economic loss caused by future landslides. This data is advantageous especially for areas with poor landslide historical data. Since the construction criteria of landslide inventory map and its quality evaluation remain poorly defined, the assessment of open source landslide inventory map reliability is required. The present contribution aims to assess the reliability of open-source landslide inventory data based on the particular topographical setting of the observed area in Niigata prefecture, Japan. Geographic Information System (GIS) platform and statistical approach are applied to analyze the data. Frequency ratio method is utilized to model and assess the landslide map. The outcomes of the generated model showed unsatisfactory results with AUC value of 0.603 indicate the low prediction accuracy and unreliability of the model.

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Science.gov (United States)

Sdao, F.; Lioi, D. S.; Pascale, S.; Caniani, D.; Mancini, I. M.

2013-02-01

The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the

Utilizing NASA Earth Observations to Assess Landslide Characteristics and Devlelop Susceptibility and Exposure Maps in Malawi

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Klug, M.; Cissell, J.; Grossman, M.

2017-12-01

Malawi has become increasingly prone to landslides in the past few decades. This can be attributed to the terrain, types of soil and vegetation, increased human interference, and heavy flooding after long periods of drought. In addition to the floods and droughts, landslides cause extra stress to farmlands, thus exacerbating the current food security crisis in the country. It can be difficult to pinpoint just how many people are affected by landslides in Malawi because landslides often occur in rural areas or are grouped with other disasters, such as floods or earthquakes. This project created a Landslide Susceptibility Map to assess landslide-prone areas in Malawi using variables such as slope, distance to roads, distance to streams, soil type, and precipitation. These variables were derived using imagery from Landsat 8 Operational Land Imager (OLI), Shuttle Radar Topography Mission Version 3 (SRTM-v3), Global Precipitation Measurement (GPM), and Tropical Rainfall Measuring Mission (TRMM) satellites. Furthermore, this project created a Landslide Exposure Map to estimate how much of the local population lives in susceptible areas by intersecting population data with the Landslide Susceptibility Map. Additionally, an assessment of GPM and TRMM precipitation measurements was generated to better understand the reliability of both measurements for landslide monitoring. Finally, this project updated NASA SERVIR's Global Landslide Catalog (GLC) for Malawi by using WorldView data from Google Earth and Landsat 8 OLI. These end products were used by NASA SERVIR and the Regional Centre for Mapping of Resources for Development (RCMRD) for aiding in disaster management throughout Malawi.

Development of geoportal for landslide monitoring

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Sladić Dubravka

2012-01-01

Full Text Available The paper presents the implementation of geoportal for landslide monitoring which which includes two subsystems: a system for acquisition, storage and distribution of data on landslides and real time alert system. System for acquisition, storage and distribution of data on landslides include raster and vector spatial data on landslides affected areas, as well as metadata. Alert system in real time is associated with a sensor for detecting displacement, which performs constant measurements and signals in case of exceeding the reference value. The system was developed in accordance with the standards in the field of GIS: ISO 19100 series of standards and OpenGIS Consortium and is based on service-oriented architecture and principles of spatial data infrastructures. [Projekat Ministarstva nauke Republike Srbije, br. TR37017: Modeliranje stanja i strukture padinskih procesa primenom GNSS i tehnologija skeniranja laserom i georadarom

State of the art of national landslide databases in Europe and their potential for assessing landslide susceptibility, hazard and risk

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Van Den Eeckhaut, Miet; Hervás, Javier

2012-02-01

A landslide inventory is the most important information source for quantitative zoning of landslide susceptibility, hazard and risk. It should give insight into the location, date, type, size, activity and causal factors of landslides as well as resultant damage. In Europe, many countries have created or are creating national and/or regional landslide databases (LDBs). Yet little is known on their contents, completeness, format, structure, language use and accessibility, and hence on their ability to perform national or transnational landslide zoning. Therefore, this study presents a detailed analysis of existing national LDBs in the EU member states, EU official candidate and potential candidate countries, and EFTA countries, and their possible use for landslide zoning. These national LDBs were compared with a subset of 22 regional databases. Twenty-two out of 37 contacted European countries currently have national LDBs, and six other countries have only regional LDBs. In total, the national LDBs contain 633,696 landslides, of which 485,004 are located in Italy, while Austria, the Czech Republic, France, Norway, Poland, Slovakia, and the UK also have > 10,000 landslides in their LDBs. National LDBs are generally created in the official language of each country and 58% of them contain other natural hazards (e.g. floods and sinkholes). About 68% of the LDBs contain less than 50% of all landslides in each country, but a positive observation is that 60% of the LDBs are updated at least once a year or after a major event. Most landslide locations are collected with traditional methods such as field surveys, aerial photo interpretation and analysis of historical records. Currently, integration of landslide information from different national LDBs is hampered because of differences in language and classification systems for landslide type and activity. Other problems are that currently only half of the national LDBs have a direct link between spatial and alphanumeric

Landslide disaster avoidance: learning from Leyte

Science.gov (United States)

Davies, T. R.

2006-12-01

On 17 February 2006 a gigantic rockslide triggered a debris avalanche that overran the barangay Guinsaugon, St. Bernard in Southern Leyte Province, Philippines, burying 154 victims, with 990 missing including 246 school children. Even with satellite imagery, GIS-based landslide susceptibility modelling and real-time meteorological and seismic data analysis, scientific prediction of every potentially fatal landslide is not possible in most parts of the world. This is particular the case in steep, unstable, densely-populated country in which heavy rain is common. So how can further events of this type be prevented from turning into disasters? A number of precursory phenomena were noted by local inhabitants at Guinsaugon: a crack around the slope that failed was noticed in May 2005; coconut trees near the northern foot of the landslide scarp began to lean increasingly in the down-slope direction in December 2005; a slope around the northern edge of the 17 February 2006 landslide scarp failed on December 17, 2005; in the 9 days prior to the rockslide, 640 mm of rain fell; 450 mm in a 3-day period. Such phenomena are commonly reported by local inhabitants before large landslides (e.g. Elm, Mayunmarca, and many others). In many cases, therefore, it is in principle possible for local people to avoid the consequences of the landslide if they know enough to act appropriately in response to the precursory phenomena. For this possibility to be realized, appropriate information must be provided to and assimilated by the local population. Useful ways of achieving this include pamphlets, video, TV and radio programs and visits from civil defence personnel. The information must be properly presented; scientific language will be ineffective. A communication pyramid, leading from government agencies to local leaders, can facilitate the rapid availability of the information to all potentially susceptible communities. If science can determine those areas not vulnerable to landslide

Changes in Vegetation Cover in Reforested Areas in the State of São Paulo, Brazil and the Implication for Landslide Processes

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Regina Célia dos Santos Alvalá

2012-09-01

Full Text Available In Brazil, plantations of exotic species such as Eucalyptus have expanded substantially in recent years, due in large part to the great demand for cellulose and wood. The combination of the steep slopes in some of these regions, such as the municipalities located close to the Serra do Mar and Serra da Mantiqueira, and the soil exposure that occurs in some stages in the Eucalyptus cultivation cycle, can cause landslides. The use of a geographic information system (GIS assists with the identification of areas that are susceptible to landslides, and one of the GIS tools used is the spatial inference technique. In this work, the landslide susceptibility of areas occupied by Eucalyptus plantations in different stages of development in municipalities in the state of São Paulo was examined. Eight thematic maps were used, and, the fuzzy gamma technique was used for data integration and the generation of susceptibility maps, in which scenarios were created with different gamma values for the dry and rainy seasons. The results for areas planted with Eucalyptus were compared with those obtained for other land uses and covers. In the moderate and high susceptibility classes, the pasture is the land use type that presented the greatest susceptibility, followed by new Eucalyptus plantations and urban areas.

Discrete rough set analysis of two different soil-behavior-induced landslides in National Shei-Pa Park, Taiwan

Directory of Open Access Journals (Sweden)

Shih-Hsun Chang

2015-11-01

Full Text Available The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites. To resolve the problem, this study focused on spatial information technology to collect data and information on geology. GIS, remote sensing and digital elevation model (DEM were used in combination to extract the attribute values of the surface material in the vast study area of Shei-Pa National Park, Taiwan. The factors influencing landslides were collected and quantification values computed. The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems. The major factors were successfully extracted from the influencing factors. Finally, the discrete rough set (DRS classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence, based upon the knowledge database. This rule-based knowledge database provides an effective and urgent system to manage landslides. NDVI (Normalized Difference Vegetation Index, VI (Vegetation Index, elevation, and distance from the road are the four major influencing factors for landslide occurrence. The landslide hazard potential diagrams (landslide susceptibility maps were drawn and a rational accuracy rate of landslide was calculated. This study thus offers a systematic solution to the investigation of landslide disasters.

Automatic landslide detection from LiDAR DTM derivatives by geographic-object-based image analysis based on open-source software

Science.gov (United States)

Knevels, Raphael; Leopold, Philip; Petschko, Helene

2017-04-01

With high-resolution airborne Light Detection and Ranging (LiDAR) data more commonly available, many studies have been performed to facilitate the detailed information on the earth surface and to analyse its limitation. Specifically in the field of natural hazards, digital terrain models (DTM) have been used to map hazardous processes such as landslides mainly by visual interpretation of LiDAR DTM derivatives. However, new approaches are striving towards automatic detection of landslides to speed up the process of generating landslide inventories. These studies usually use a combination of optical imagery and terrain data, and are designed in commercial software packages such as ESRI ArcGIS, Definiens eCognition, or MathWorks MATLAB. The objective of this study was to investigate the potential of open-source software for automatic landslide detection based only on high-resolution LiDAR DTM derivatives in a study area within the federal state of Burgenland, Austria. The study area is very prone to landslides which have been mapped with different methodologies in recent years. The free development environment R was used to integrate open-source geographic information system (GIS) software, such as SAGA (System for Automated Geoscientific Analyses), GRASS (Geographic Resources Analysis Support System), or TauDEM (Terrain Analysis Using Digital Elevation Models). The implemented geographic-object-based image analysis (GEOBIA) consisted of (1) derivation of land surface parameters, such as slope, surface roughness, curvature, or flow direction, (2) finding optimal scale parameter by the use of an objective function, (3) multi-scale segmentation, (4) classification of landslide parts (main scarp, body, flanks) by k-mean thresholding, (5) assessment of the classification performance using a pre-existing landslide inventory, and (6) post-processing analysis for the further use in landslide inventories. The results of the developed open-source approach demonstrated good

Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness

Science.gov (United States)

Kirschbaum, Dalia; Stanley, Thomas

2018-03-01

Determining the time, location, and severity of natural disaster impacts is fundamental to formulating mitigation strategies, appropriate and timely responses, and robust recovery plans. A Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to indicate potential landslide activity in near real-time. LHASA combines satellite-based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. Precipitation data from the Global Precipitation Measurement (GPM) mission are used to identify rainfall conditions from the past 7 days. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a "nowcast" is issued to indicate the times and places where landslides are more probable. When LHASA nowcasts were evaluated with a Global Landslide Catalog, the probability of detection (POD) ranged from 8% to 60%, depending on the evaluation period, precipitation product used, and the size of the spatial and temporal window considered around each landslide point. Applications of the LHASA system are also discussed, including how LHASA is used to estimate long-term trends in potential landslide activity at a nearly global scale and how it can be used as a tool to support disaster risk assessment. LHASA is intended to provide situational awareness of landslide hazards in near real-time, providing a flexible, open-source framework that can be adapted to other spatial and temporal scales based on data availability.

Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey).

Science.gov (United States)

Akgun, Aykut; Kıncal, Cem; Pradhan, Biswajeet

2012-09-01

In this study, landslide risk assessment for Izmir city (west Turkey) was carried out, and the environmental effects of landslides on further urban development were evaluated using geographical information systems and remote sensing techniques. For this purpose, two different data groups, namely conditioning and triggering data, were produced. With the help of conditioning data such as lithology, slope gradient, slope aspect, distance from roads, distance from faults and distance from drainage lines, a landslide susceptibility model was constructed by using logistic regression modelling approach. The accuracy assessment of the susceptibility map was carried out by the area under curvature (AUC) approach, and a 0.810 AUC value was obtained. This value shows that the map obtained is successful. Due to the fact that the study area is located in an active seismic region, earthquake data were considered as primary triggering factor contributing to landslide occurrence. In addition to this, precipitation data were also taken into account as a secondary triggering factor. Considering the susceptibility data and triggering factors, a landslide hazard index was obtained. Furthermore, using the Aster data, a land-cover map was produced with an overall kappa value of 0.94. From this map, settlement areas were extracted, and these extracted data were assessed as elements at risk in the study area. Next, a vulnerability index was created by using these data. Finally, the hazard index and the vulnerability index were combined, and a landslide risk map for Izmir city was obtained. Based on this final risk map, it was observed that especially south and north parts of the Izmir Bay, where urbanization is dense, are threatened to future landsliding. This result can be used for preliminary land use planning by local governmental authorities.

The inner structure of landslides and landslide-prone slopes in south German cuesta landscapes assessed by geophysical, geomorphological and sedimentological approaches

Science.gov (United States)

Schwindt, Daniel; Sandmeier, Christine; Büdel, Christian; Jäger, Daniel; Wilde, Martina; Terhorst, Birgit

2016-04-01

Investigations on landslide activity in the cuesta landscape of Germany, usually characterized by an interbedding of morphologically hard (e.g. sand-/limestones) and soft (clay) sedimentary rocks are relatively sparse. However, spring 2013 has once again revealed a high susceptibility of the slopes in the Franconian and Swabian Alb to mass movements, when enduring rainfalls initiated numerous landslides causing considerable damage to settlements and infrastructure. Many aspects like the spatial distribution of landslides, triggering factors, and process dynamics - especially with view on the reactivation of landslides - require intensive investigations to allow for assessment of the landslide vulnerability and the development of reliable early-warning systems. Aim of the study is to achieve a deeper insight into the triggering factors and the process dynamics of landslides in the cuesta landscape with special regard on landslide proneness of slopes and the potential reactivation of old landslides. A multi-methodological approach was conducted based on geophysical investigations (seismic refraction tomography - SRT, electrical resistivity tomography - ERT), geomorphological mapping, morphometric GIS-based analysis, core soundings and substrate mapping. Study sites are located in the Swabian Alb (southwestern Germany) in the Jurassic escarpment where where Oxfordian marls and limestones superimpose Callovian clays, as well as in the northeastern Franconian Alb, within the escarpment of the so called Rhätolias with with red claystones of the late Norian (Feuerletten formation) below interbedding layers of sand- and claystones of the Rhaetian (Upper Triassic) and Hettangian ( Lower Jurassic). The investigated landslides strongly differ with respect to their age, from young landslides originated in spring 2013 to ancient landslides. Investigations reveal a distinct diversity of landslide types composed of a complex combination of processes. The applied methods allow

A new-old approach for shallow landslide analysis and susceptibility zoning in fine-grained weathered soils of southern Italy

Science.gov (United States)

Cascini, Leonardo; Ciurleo, Mariantonietta; Di Nocera, Silvio; Gullà, Giovanni

2015-07-01

Rainfall-induced shallow landslides involve several geo-environmental contexts and different types of soils. In clayey soils, they affect the most superficial layer, which is generally constituted by physically weathered soils characterised by a diffuse pattern of cracks. This type of landslide most commonly occurs in the form of multiple-occurrence landslide phenomena simultaneously involving large areas and thus has several consequences in terms of environmental and economic damage. Indeed, landslide susceptibility zoning is a relevant issue for land use planning and/or design purposes. This study proposes a multi-scale approach to reach this goal. The proposed approach is tested and validated over an area in southern Italy affected by widespread shallow landslides that can be classified as earth slides and earth slide-flows. Specifically, by moving from a small (1:100,000) to a medium scale (1:25,000), with the aid of heuristic and statistical methods, the approach identifies the main factors leading to landslide occurrence and effectively detects the areas potentially affected by these phenomena. Finally, at a larger scale (1:5000), deterministic methods, i.e., physically based models (TRIGRS and TRIGRS-unsaturated), allow quantitative landslide susceptibility assessment, starting from sample areas representative of those that can be affected by shallow landslides. Considering the reliability of the obtained results, the proposed approach seems useful for analysing other case studies in similar geological contexts.

The landslide database for Germany: Closing the gap at national level

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Damm, Bodo; Klose, Martin

2015-11-01

The Federal Republic of Germany has long been among the few European countries that lack a national landslide database. Systematic collection and inventory of landslide data still has a long research history in Germany, but one focussed on the development of databases with local or regional coverage. This has changed in recent years with the launch of a database initiative aimed at closing the data gap existing at national level. The present paper reports on this project that is based on a landslide database which evolved over the last 15 years to a database covering large parts of Germany. A strategy of systematic retrieval, extraction, and fusion of landslide data is at the heart of the methodology, providing the basis for a database with a broad potential of application. The database offers a data pool of more than 4,200 landslide data sets with over 13,000 single data files and dates back to the 12th century. All types of landslides are covered by the database, which stores not only core attributes, but also various complementary data, including data on landslide causes, impacts, and mitigation. The current database migration to PostgreSQL/PostGIS is focused on unlocking the full scientific potential of the database, while enabling data sharing and knowledge transfer via a web GIS platform. In this paper, the goals and the research strategy of the database project are highlighted at first, with a summary of best practices in database development providing perspective. Next, the focus is on key aspects of the methodology, which is followed by the results of three case studies in the German Central Uplands. The case study results exemplify database application in the analysis of landslide frequency and causes, impact statistics, and landslide susceptibility modeling. Using the example of these case studies, strengths and weaknesses of the database are discussed in detail. The paper concludes with a summary of the database project with regard to previous

Spatially explicit shallow landslide susceptibility mapping over large areas

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Dino Bellugi; William E. Dietrich; Jonathan Stock; Jim McKean; Brian Kazian; Paul Hargrove

2011-01-01

Recent advances in downscaling climate model precipitation predictions now yield spatially explicit patterns of rainfall that could be used to estimate shallow landslide susceptibility over large areas. In California, the United States Geological Survey is exploring community emergency response to the possible effects of a very large simulated storm event and to do so...

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

Science.gov (United States)

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-05-11

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

The Rock Engineering System (RES) applied to landslide susceptibility zonation of the northeastern flank of Etna: methodological approach and results

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Apuani, Tiziana; Corazzato, Claudia

2015-04-01

Ground deformations in the northeastern flank of Etna are well known. Despite only a few landslide events have been documented, these have significantly involved and damaged lifelines and buildings. These events are mainly related to the activity of the volcano-tectonic structures and associated seismicity, as in the case of the 2002 reactivation of the Presa landslide during an increased activity of the Pernicana fault system. In order to highlight the areal distribution of potentially unstable slopes based on a detailed, site-specific study of the factors responsible for landslide, and to ultimately contribute to risk management, a landslide susceptibility analysis of the northeastern flank of Etna in the Pernicana area was carried out, and a susceptibility map at 1:10.000 scale was produced, extending over an area of 168 km2. Different methods are proposed in the literature to obtain the regional distribution of potentially unstable slopes, depending on the problem scale, the slope dynamic evolution in the geological context, and the availability of data. Among semi-quantitative approaches, the present research combines the Rock Engineering System (RES) methodology with parameter zonation mapping in a GIS environment. The RES method represents a structured approach to manage a high number of interacting factors involved in the instability problem. A numerically coded, site-specific interaction matrix (IM) analyzes the cause-effect relationship in these factors, and calculates the degree of interactivity of each parameter, normalized by the overall interactivity of the system (weight factor). In the specific Etna case, the considered parameters are: slope attitude, lithotechnical properties (lithology, structural complexity, soil and rock mass quality), land use, tectonic structures, seismic activity (horizontal acceleration) and hydrogeological conditions (groundwater and drainage). Thematic maps are prepared at 1:10.000 scale for each of these parameters, and

Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale

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L. Montrasio

2011-07-01

Full Text Available In the framework of landslide risk management, it appears relevant to assess, both in space and in time, the triggering of rainfall-induced shallow landslides, in order to prevent damages due to these kind of disasters. In this context, the use of real-time landslide early warning systems has been attracting more and more attention from the scientific community. This paper deals with the application, on a regional scale, of two physically-based stability models: SLIP (Shallow Landslides Instability Prediction and TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis. A back analysis of some recent case-histories of soil slips which occurred in the territory of the central Emilian Apennine, Emilia Romagna Region (Northern Italy is carried out and the main results are shown. The study area is described from geological and climatic viewpoints. The acquisition of geospatial information regarding the topography, the soil properties and the local landslide inventory is also explained.

The paper outlines the main features of the SLIP model and the basic assumptions of TRIGRS. Particular attention is devoted to the discussion of the input data, which have been stored and managed through a Geographic Information System (GIS platform. Results of the SLIP model on a regional scale, over a one year time interval, are finally presented. The results predicted by the SLIP model are analysed both in terms of safety factor (F_s maps, corresponding to particular rainfall events, and in terms of time-varying percentage of unstable areas over the considered time interval. The paper compares observed landslide localizations with those predicted by the SLIP model. A further quantitative comparison between SLIP and TRIGRS, both applied to the most important event occurred during the analysed period, is presented. The limits of the SLIP model, mainly due to some restrictions of simplifying the physically

Analysis of Landslide Hazard Impact Using the Landslide Database for Germany

Science.gov (United States)

Klose, M.; Damm, B.

2014-12-01

The Federal Republic of Germany has long been among the few European countries that lack a national landslide database. Systematic collection and inventory of landslide data still shows a comprehensive research history in Germany, but only one focused on development of databases with local or regional coverage. This has changed in recent years with the launch of a database initiative aimed at closing the data gap existing at national level. The present contribution reports on this project that is based on a landslide database which evolved over the last 15 years to a database covering large parts of Germany. A strategy of systematic retrieval, extraction, and fusion of landslide data is at the heart of the methodology, providing the basis for a database with a broad potential of application. The database offers a data pool of more than 4,200 landslide data sets with over 13,000 single data files and dates back to 12th century. All types of landslides are covered by the database, which stores not only core attributes, but also various complementary data, including data on landslide causes, impacts, and mitigation. The current database migration to PostgreSQL/PostGIS is focused on unlocking the full scientific potential of the database, while enabling data sharing and knowledge transfer via a web GIS platform. In this contribution, the goals and the research strategy of the database project are highlighted at first, with a summary of best practices in database development providing perspective. Next, the focus is on key aspects of the methodology, which is followed by the results of different case studies in the German Central Uplands. The case study results exemplify database application in analysis of vulnerability to landslides, impact statistics, and hazard or cost modeling.

A 3D visible evaluation of landslide risk degree under integration of GIS and artificial intelligence

Institute of Scientific and Technical Information of China (English)

QIAO; Jianping; ZHU; Axing; CHEN; Yongbo; WANG; Rongxun

2003-01-01

Artificial intelligence has been used to obtain background factors (basic environmental factors) from landslide specialists. A 3D visible evaluation map may be charted by fuzzy evaluation, and the traditional plane map may be decoded into a 3D map by using factor weight from specialists system and technology of RS and GIS for quantitative sampling of these factors.

Integrating statistical and process-based models to produce probabilistic landslide hazard at regional scale

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Strauch, R. L.; Istanbulluoglu, E.

2017-12-01

We develop a landslide hazard modeling approach that integrates a data-driven statistical model and a probabilistic process-based shallow landslide model for mapping probability of landslide initiation, transport, and deposition at regional scales. The empirical model integrates the influence of seven site attribute (SA) classes: elevation, slope, curvature, aspect, land use-land cover, lithology, and topographic wetness index, on over 1,600 observed landslides using a frequency ratio (FR) approach. A susceptibility index is calculated by adding FRs for each SA on a grid-cell basis. Using landslide observations we relate susceptibility index to an empirically-derived probability of landslide impact. This probability is combined with results from a physically-based model to produce an integrated probabilistic map. Slope was key in landslide initiation while deposition was linked to lithology and elevation. Vegetation transition from forest to alpine vegetation and barren land cover with lower root cohesion leads to higher frequency of initiation. Aspect effects are likely linked to differences in root cohesion and moisture controlled by solar insulation and snow. We demonstrate the model in the North Cascades of Washington, USA and identify locations of high and low probability of landslide impacts that can be used by land managers in their design, planning, and maintenance.

Root reinforcement and its implications in shallow landsliding susceptibility on a small alpine catchment

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Morandi, M. C.; Farabegoli, E.; Onorevoli, G.

2012-04-01

Roots shear resistance offers a considerable contribution to hill-slope stability on vegetated terrains. Through the pseudo-cohesion of shrubs, trees and turf's roots, the geomechanical properties of soils can be drastically increased, exerting a positive influence on the hillslope stability. We analysed the shallow landsliding susceptibility of a small alpine catchment (Duron valley, Central Dolomites, Italy) that we consider representative of a wide altitude belt of the Dolomites (1800 - 2400 m a.s.l). The catchment is mostly mantled by grass (Nardetum strictae s.l.), with clustered shrubs (Rhododendron hirsutum and Juniperus nana), and trees (Pinus cembra, Larix decidua and Picea abies). The soil depth, investigated with direct and indirect methods, ranges from 0 to 180 cm, with its peak at the hollow axes. Locally, the bedrock, made of Triassic volcanic rocks, is deeply incised by the Holocene drainage network. Intensive grazing of cows and horses pervades the catchment area and cattle-trails occupy ca 20% of the grass cover. We used laboratory and field tests to characterize the geotechnical properties of these alpine soils; moreover we designed and tested an experimental device that measures, in situ, the shear strengths of the grass mantle. In the study area we mapped 18 shallow landslides, mostly related to road cuts and periodically reactivated as retrogressive landslides. The triggering mechanisms of these shallow landslides were qualitatively analysed at large scale and modelled at smaller scale. We used SHALSTAB to model the shallow landsliding susceptibility of the catchment at the basin scale and SLIDE (RocScience) to compute the Safety Factor at the versant scale. Qualitative management solutions are provided, in order to reduce the shallow landsliding susceptibility risk in this alpine context.

Landslide susceptibility assessment in the Peloritani Mts. (Sicily, Italy and clues for tectonic control of relief processes

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G. De Guidi

2013-04-01

Full Text Available Many destructive shallow landslides hit villages in the Peloritani Mountains area (Sicily, Italy on 1 October 2009 after heavy rainfall. The collection of several types of spatial data, together with a landslide inventory, allows the assessment of the landslide susceptibility by applying a statistical technique. The susceptibility model was validated by performing an analysis in a test area using independent landslide information, the results being able to correctly predict more than 70% of the landslides. Furthermore, the susceptibility analysis allowed the identification of which combinations of classes, within the different factors, have greater relevance in slope instability, and afterwards associating the most unstable combinations (with a short–medium term incidence with the endogenic processes acting in the area (huge regional uplift, fault activity. Geological and tectonic history are believed to be key to interpreting morphological processes and landscape evolution. Recent tectonic activity was found to be a very important controlling factor in landscape evolution. A geomorphological model of cyclical relief evolution is proposed in which endogenic processes are directly linked to superficial processes. The results are relevant both to risk reduction and the understanding of active geological dynamics.

Landslide susceptibility assessment in the Upper Orcia Valley (Southern Tuscany, Italy through conditional analysis: a contribution to the unbiased selection of causal factors

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F. Vergari

2011-05-01

Full Text Available In this work the conditional multivariate analysis was applied to evaluate landslide susceptibility in the Upper Orcia River Basin (Tuscany, Italy, where widespread denudation processes and agricultural practices have a mutual impact. We introduced an unbiased procedure for causal factor selection based on some intuitive statistical indices. This procedure is aimed at detecting among different potential factors the most discriminant ones in a given study area. Moreover, this step avoids generating too small and statistically insignificant spatial units by intersecting the factor maps. Finally, a validation procedure was applied based on the partition of the landslide inventory from multi-temporal aerial photo interpretation.

Although encompassing some sources of uncertainties, the applied susceptibility assessment method provided a satisfactory and unbiased prediction for the Upper Orcia Valley. The results confirmed the efficiency of the selection procedure, as an unbiased step of the landslide susceptibility evaluation. Furthermore, we achieved the purpose of presenting a conceptually simple but, at the same time, effective statistical procedure for susceptibility analysis to be used as well by decision makers in land management.

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

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Xianyu Yu

2016-05-01

Full Text Available In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

Binary Logistic Regression Versus Boosted Regression Trees in Assessing Landslide Susceptibility for Multiple-Occurring Regional Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, southern Italy).

Science.gov (United States)

Lombardo, L.; Cama, M.; Maerker, M.; Parisi, L.; Rotigliano, E.

2014-12-01

This study aims at comparing the performances of Binary Logistic Regression (BLR) and Boosted Regression Trees (BRT) methods in assessing landslide susceptibility for multiple-occurrence regional landslide events within the Mediterranean region. A test area was selected in the north-eastern sector of Sicily (southern Italy), corresponding to the catchments of the Briga and the Giampilieri streams both stretching for few kilometres from the Peloritan ridge (eastern Sicily, Italy) to the Ionian sea. This area was struck on the 1st October 2009 by an extreme climatic event resulting in thousands of rapid shallow landslides, mainly of debris flows and debris avalanches types involving the weathered layer of a low to high grade metamorphic bedrock. Exploiting the same set of predictors and the 2009 landslide archive, BLR- and BRT-based susceptibility models were obtained for the two catchments separately, adopting a random partition (RP) technique for validation; besides, the models trained in one of the two catchments (Briga) were tested in predicting the landslide distribution in the other (Giampilieri), adopting a spatial partition (SP) based validation procedure. All the validation procedures were based on multi-folds tests so to evaluate and compare the reliability of the fitting, the prediction skill, the coherence in the predictor selection and the precision of the susceptibility estimates. All the obtained models for the two methods produced very high predictive performances, with a general congruence between BLR and BRT in the predictor importance. In particular, the research highlighted that BRT-models reached a higher prediction performance with respect to BLR-models, for RP based modelling, whilst for the SP-based models the difference in predictive skills between the two methods dropped drastically, converging to an analogous excellent performance. However, when looking at the precision of the probability estimates, BLR demonstrated to produce more robust

Analysis of significance of environmental factors in landslide susceptibility modeling: Case study Jemma drainage network, Ethiopia

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Vít Maca

2017-06-01

Full Text Available Aim of the paper is to describe methodology for calculating significance of environmental factors in landslide susceptibility modeling and present result of selected one. As a study area part of a Jemma basin in Ethiopian Highland is used. This locality is highly affected by mass movement processes. In the first part all major factors and their influence are described briefly. Majority of the work focuses on research of other methodologies used in susceptibility models and design of own methodology. This method is unlike most of the methods used completely objective, therefore it is not possible to intervene in the results. In article all inputs and outputs of the method are described as well as all stages of calculations. Results are illustrated on specific examples. In study area most important factor for landslide susceptibility is slope, on the other hand least important is land cover. At the end of article landslide susceptibility map is created. Part of the article is discussion of results and possible improvements of the methodology.

Landslide Susceptibility Mapping of Tegucigalpa, Honduras Using Artificial Neural Network, Bayesian Network and Decision Trees

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Garcia Urquia, E. L.; Braun, A.; Yamagishi, H.

2016-12-01

Tegucigalpa, the capital city of Honduras, experiences rainfall-induced landslides on a yearly basis. The high precipitation regime and the rugged topography the city has been built in couple with the lack of a proper urban expansion plan to contribute to the occurrence of landslides during the rainy season. Thousands of inhabitants live at risk of losing their belongings due to the construction of precarious shelters in landslide-prone areas on mountainous terrains and next to the riverbanks. Therefore, the city is in the need for landslide susceptibility and hazard maps to aid in the regulation of future development. Major challenges in the context of highly dynamic urbanizing areas are the overlap of natural and anthropogenic slope destabilizing factors, as well as the availability and accuracy of data. Data-driven multivariate techniques have proven to be powerful in discovering interrelations between factors, identifying important factors in large datasets, capturing non-linear problems and coping with noisy and incomplete data. This analysis focuses on the creation of a landslide susceptibility map using different methods from the field of data mining, Artificial Neural Networks (ANN), Bayesian Networks (BN) and Decision Trees (DT). The input dataset of the study contains geomorphological and hydrological factors derived from a digital elevation model with a 10 m resolution, lithological factors derived from a geological map, and anthropogenic factors, such as information on the development stage of the neighborhoods in Tegucigalpa and road density. Moreover, a landslide inventory map that was developed in 2014 through aerial photo interpretation was used as target variable in the analysis. The analysis covers an area of roughly 100 km2, while 8.95 km2 are occupied by landslides. In a first step, the dataset was explored by assessing and improving the data quality, identifying unimportant variables and finding interrelations. Then, based on a training

Large-area landslide susceptibility with optimized slope-units

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Alvioli, Massimiliano; Marchesini, Ivan; Reichenbach, Paola; Rossi, Mauro; Ardizzone, Francesca; Fiorucci, Federica; Guzzetti, Fausto

2017-04-01

A Slope-Unit (SU) is a type of morphological terrain unit bounded by drainage and divide lines that maximize the within-unit homogeneity and the between-unit heterogeneity across distinct physical and geographical boundaries [1]. Compared to other terrain subdivisions, SU are morphological terrain unit well related to the natural (i.e., geological, geomorphological, hydrological) processes that shape and characterize natural slopes. This makes SU easily recognizable in the field or in topographic base maps, and well suited for environmental and geomorphological analysis, in particular for landslide susceptibility (LS) modelling. An optimal subdivision of an area into a set of SU depends on multiple factors: size and complexity of the study area, quality and resolution of the available terrain elevation data, purpose of the terrain subdivision, scale and resolution of the phenomena for which SU are delineated. We use the recently developed r.slopeunits software [2,3] for the automatic, parametric delineation of SU within the open source GRASS GIS based on terrain elevation data and a small number of user-defined parameters. The software provides subdivisions consisting of SU with different shapes and sizes, as a function of the input parameters. In this work, we describe a procedure for the optimal selection of the user parameters through the production of a large number of realizations of the LS model. We tested the software and the optimization procedure in a 2,000 km2 area in Umbria, Central Italy. For LS zonation we adopt a logistic regression model implemented in an well-known software [4,5], using about 50 independent variables. To select the optimal SU partition for LS zonation, we want to define a metric which is able to quantify simultaneously: (i) slope-unit internal homogeneity (ii) slope-unit external heterogeneity (iii) landslide susceptibility model performance. To this end, we define a comprehensive objective function S, as the product of three

Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

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M. Bordoni

2018-06-01

Full Text Available Landslides cause severe damage to the road network of the hit zone, in terms of both direct (partial or complete destruction of a road or blockages and indirect (traffic restriction or the cut-off of a certain area costs. Thus, the identification of the parts of the road network that are more susceptible to landslides is fundamental to reduce the risk to the population potentially exposed and the financial expense caused by the damage. For these reasons, this paper aimed to develop and test a data-driven model for the identification of road sectors that are susceptible to being hit by shallow landslides triggered in slopes upstream from the infrastructure. This model was based on the Generalized Additive Method, where the function relating predictors and response variable is an empirically fitted smooth function that allows fitting the data in the more likely functional form, considering also non-linear relations. This work also analyzed the importance, on the estimation of the susceptibility, of considering or not the sediment connectivity, which influences the path and the travel distance of the materials mobilized by a slope failure until hitting a potential barrier such as a road. The study was carried out in a catchment of northeastern Oltrepò Pavese (northern Italy, where several shallow landslides affected roads in the last 8 years. The most significant explanatory variables were selected by a random partition of the available dataset in two parts (training and test subsets, 100 times according to a bootstrap procedure. These variables (selected 80 times by the bootstrap procedure were used to build the final susceptibility model, the accuracy of which was estimated through a 100-fold repetition of the holdout method for regression, based on the training and test sets created through the 100 bootstrap model selection. The presented methodology allows the identification, in a robust and reliable way, of the most susceptible road

Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy)

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Conoscenti, Christian; Ciaccio, Marilena; Caraballo-Arias, Nathalie Almaru; Gómez-Gutiérrez, Álvaro; Rotigliano, Edoardo; Agnesi, Valerio

2015-08-01

In this paper, terrain susceptibility to earth-flow occurrence was evaluated by using geographic information systems (GIS) and two statistical methods: Logistic regression (LR) and multivariate adaptive regression splines (MARS). LR has been already demonstrated to provide reliable predictions of earth-flow occurrence, whereas MARS, as far as we know, has never been used to generate earth-flow susceptibility models. The experiment was carried out in a basin of western Sicily (Italy), which extends for 51 km2 and is severely affected by earth-flows. In total, we mapped 1376 earth-flows, covering an area of 4.59 km2. To explore the effect of pre-failure topography on earth-flow spatial distribution, we performed a reconstruction of topography before the landslide occurrence. This was achieved by preparing a digital terrain model (DTM) where altitude of areas hosting landslides was interpolated from the adjacent undisturbed land surface by using the algorithm topo-to-raster. This DTM was exploited to extract 15 morphological and hydrological variables that, in addition to outcropping lithology, were employed as explanatory variables of earth-flow spatial distribution. The predictive skill of the earth-flow susceptibility models and the robustness of the procedure were tested by preparing five datasets, each including a different subset of landslides and stable areas. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The results demonstrate that the overall accuracy of LR and MARS earth-flow susceptibility models is from excellent to outstanding. However, AUC values of the validation datasets attest to a higher predictive power of MARS-models (AUC between 0.881 and 0.912) with respect to LR-models (AUC between 0.823 and 0.870). The adopted procedure proved to be resistant to overfitting and stable when changes of the learning and validation samples are

Presence-only approach to assess landslide triggering-thickness susceptibility. A test for the Mili catchment (North-Eastern Sicily, Italy)

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Lombardo, Luigi; Fubelli, Giandomenico; Amato, Gabriele; Bonasera, Mauro; Hochschild, Volker; Rotigliano, Edoardo

2015-04-01

thicknesses. In addition, the role of each predictor within the whole modelling procedure was assessed by applying Jackknife tests. These analyses focussed on evaluating the variation of AUC values across replicates comparing single variable models with models based on the full set of predictors iteratively deprived of one covariate. As a result, relevant differences among main contributors between the two considered classes were also quantitatively derived and geomorphologically interpreted. This work can be considered as an example for creating specific landslide susceptibility maps to be used in master planning in order to establish proportional countermeasures to different activation mechanisms. Keywords: statistical analysis, shallow landslide, landslide susceptibility, triggering factors, presence-only approach

Landslide databases for applied landslide impact research: the example of the landslide database for the Federal Republic of Germany

Science.gov (United States)

Damm, Bodo; Klose, Martin

2014-05-01

This contribution presents an initiative to develop a national landslide database for the Federal Republic of Germany. It highlights structure and contents of the landslide database and outlines its major data sources and the strategy of information retrieval. Furthermore, the contribution exemplifies the database potentials in applied landslide impact research, including statistics of landslide damage, repair, and mitigation. The landslide database offers due to systematic regional data compilation a differentiated data pool of more than 5,000 data sets and over 13,000 single data files. It dates back to 1137 AD and covers landslide sites throughout Germany. In seven main data blocks, the landslide database stores besides information on landslide types, dimensions, and processes, additional data on soil and bedrock properties, geomorphometry, and climatic or other major triggering events. A peculiarity of this landslide database is its storage of data sets on land use effects, damage impacts, hazard mitigation, and landslide costs. Compilation of landslide data is based on a two-tier strategy of data collection. The first step of information retrieval includes systematic web content mining and exploration of online archives of emergency agencies, fire and police departments, and news organizations. Using web and RSS feeds and soon also a focused web crawler, this enables effective nationwide data collection for recent landslides. On the basis of this information, in-depth data mining is performed to deepen and diversify the data pool in key landslide areas. This enables to gather detailed landslide information from, amongst others, agency records, geotechnical reports, climate statistics, maps, and satellite imagery. Landslide data is extracted from these information sources using a mix of methods, including statistical techniques, imagery analysis, and qualitative text interpretation. The landslide database is currently migrated to a spatial database system

Landslides Hazard Assessment Using Different Approaches

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Coman Cristina

2017-06-01

Full Text Available Romania represents one of Europe’s countries with high landslides occurrence frequency. Landslide hazard maps are designed by considering the interaction of several factors which, by their joint action may affect the equilibrium state of the natural slopes. The aim of this paper is landslides hazard assessment using the methodology provided by the Romanian national legislation and a very largely used statistical method. The final results of these two analyses are quantitative or semi-quantitative landslides hazard maps, created in geographic information system environment. The data base used for this purpose includes: geological and hydrogeological data, digital terrain model, hydrological data, land use, seismic action, anthropic action and an inventory of active landslides. The GIS landslides hazard models were built for the geographical area of the Iasi city, located in the north-east side of Romania.

Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling

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M. Alvioli

2016-11-01

Full Text Available Automatic subdivision of landscapes into terrain units remains a challenge. Slope units are terrain units bounded by drainage and divide lines, but their use in hydrological and geomorphological studies is limited because of the lack of reliable software for their automatic delineation. We present the r.slopeunits software for the automatic delineation of slope units, given a digital elevation model and a few input parameters. We further propose an approach for the selection of optimal parameters controlling the terrain subdivision for landslide susceptibility modeling. We tested the software and the optimization approach in central Italy, where terrain, landslide, and geo-environmental information was available. The software was capable of capturing the variability of the landscape and partitioning the study area into slope units suited for landslide susceptibility modeling and zonation. We expect r.slopeunits to be used in different physiographical settings for the production of reliable and reproducible landslide susceptibility zonations.

Landslide susceptibility mapping in Mawat area, Kurdistan Region, NE Iraq: a comparison of different statistical models

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Othman, A. A.; Gloaguen, R.; Andreani, L.; Rahnama, M.

2015-03-01

During the last decades, expansion of settlements into areas prone to landslides in Iraq has increased the importance of accurate hazard assessment. Susceptibility mapping provides information about hazardous locations and thus helps to potentially prevent infrastructure damage due to mass wasting. The aim of this study is to evaluate and compare frequency ratio (FR), weight of evidence (WOE), logistic regression (LR) and probit regression (PR) approaches in combination with new geomorphological indices to determine the landslide susceptibility index (LSI). We tested these four methods in Mawat area, Kurdistan Region, NE Iraq, where landslides occur frequently. For this purpose, we evaluated 16 geomorphological, geological and environmental predicting factors mainly derived from the advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite. The available reference inventory includes 351 landslides representing a cumulative surface of 3.127 km2. This reference inventory was mapped from QuickBird data by manual delineation and partly verified by field survey. The areas under curve (AUC) of the receiver operating characteristic (ROC), and relative landslide density (R index) show that all models perform similarly and that focus should be put on the careful selection of proxies. The results indicate that the lithology and the slope aspects play major roles for landslide occurrences. Furthermore, this paper demonstrates that using hypsometric integral as a prediction factor instead of slope curvature gives better results and increases the accuracy of the LSI.

Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

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Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Li, Yuanyao; Catani, Filippo; Pourghasemi, Hamid Reza

2018-03-01

Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was taken as a case study for landslide susceptibility assessment in order to develop effective risk prevention and mitigation strategies. To begin, 202 landslides were identified, including 95 colluvial landslides and 107 rockfalls. Twelve landslide causal factor maps were prepared initially, and the relationship between these factors and each landslide type was analyzed using the information value model. Later, the unimportant factors were selected and eliminated using the information gain ratio technique. The landslide locations were randomly divided into two groups: 70% for training and 30% for verifying. Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment results of the two landslide types, were classified into five levels. The performance of the LSMs was evaluated using the receiver operating characteristics curve and Friedman test. Results show that the elimination of noise-generating factors and the separated modeling of each landslide type have significantly increased the prediction accuracy. The machine learning models outperformed the multivariate statistical model and SVM model was found ideal for the case study area.

Identification of Suitable Indices for Identification of Potential Sites ...

African Journals Online (AJOL)

system (GIS)-based decision support system (DSS) can be a valuable tool for such a task. However, key to .... The data were processed in a GIS ..... Landslide susceptibility mapping by GIS-based ... remote sensing and GIS in the Makanya.

Landslide susceptibility mapping in the coastal region in the State of São Paulo, Brazil

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Alvala, R. C.; Camarinha, P. I.; Canavesi, V.

2013-05-01

The exposure of populations in risk areas is a matter of global concern, because it is a determining factor for the natural disasters occurrences. Furthermore, it has also been observed an intensification of extreme hydrometeorological events that has triggered disasters in various parts of the globe, further increasing the need for monitoring and alerting for natural disasters, aiming the safeguarding of life and minimize economic losses. Accordingly, different methodologies for risk assessment have been proposed, focusing on the specific natural hazards. Particularly for Brazil, which has economic axis of development in the regions near the coast, it is common to observe the process of urbanization advancing on steep slopes of the mountain regions. This characteristic causes the population exposure to the natural hazards related to the mass movements, which the landslides stood out as the cause of many deaths and economic losses every year. Thus, prior to risk analysis (when human occupation intersect with natural hazard), it is essential to analyze the susceptibility, which reflects the physical and environmental conditions that trigger for such phenomena. However, this task becomes a major challenge due to the difficulty of finding databases with good quality. In this context, this paper presents a methodology based only on spatial information in the public domain, integrated into a Geographic Information System free, in order to analyze the landslides susceptibility. In a first effort, we evaluated four counties of Southeastern Brazil - Santos, Cubatão, Caraguatatuba and Ubatuba - located in a region that includes the rugged reliefs of Serra do Mar and the transition to the coastal region, that have historic of disasters related. It is noteworthy that the methodology takes into account many variables that was weighted and crossed by Fuzzy Gamma technique, such as: topography (horizontal and vertical curvature of the slopes), geology, geomorphology, slope

Camili (Macahel Havzasının (Artvin, KD Türkiye Heyelan Duyarlılık Analizi Landslide Susceptibility Analysis of Camili (Macahel Basin (Artvin, NE Turkey

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Emre ÖZŞAHİN

2013-03-01

Full Text Available One of the most common natural disasters in Turkey and in theworld is landslides. Investigation of Turkey’s profile of the last 50 yearsshows that the landslides are the most commonly occurring naturaldisasters with a ratio of 45%.The current study investigated the landslide susceptibility analysisof Camili (Macahel which is one of the unique places on earth with itsspecific fauna and flora and which is the first Biosphere Reserve Area ofTurkey. The landslides which are among the most common naturaldisaster risks in the basin with international recognizance present crucialthreats both in terms of settlement and planning. In this context, thestudy examined the factors that cause landslides in the basin area, theirimpact levels, ratios of potential landslide zones and their geographicaldistributions.The study employed factor maps of various scales obtained fromdifferent resources. 17 parameter factors obtained with the help of thesemaps were evaluated separately and in connection with conditiondependent overlay method to identify landslide risk zones. The studybased on a topography plate with scaled 1/25.000 made use of maps ofvarious scales about different parameters as well as Landsat satelliteimages. Mapping and analysis phases of the study were based onGeographical Information Systems (GIS and Remote Sensing (RStechnology. In this context, ArcGIS/ArcMap 10 GIS and ERDAS 2012 RSsoftware was utilized.It was identified at the end of the landslide susceptibility study ofthe basin that medium level susceptible zones display the highestdistribution with 56 % ratio (14284 ha followed by high (6972 ha - 28 %and very high (611 ha – 2 % level susceptible zones with 30 % and low3288 ha – 13 % and very low (67 ha – 1 % level susceptible zones with14 %. It was determined that potential risk zones are concentratedaround slopes overlooking north where relief and precipitation values arehigh, mountain and slope morphology is dominant and vegetation

Landslide hazard assessment along a mountain highway in the Indian Himalayan Region (IHR) using remote sensing and computational models

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Krishna, Akhouri P.; Kumar, Santosh

2013-10-01

Landslide hazard assessments using computational models, such as artificial neural network (ANN) and frequency ratio (FR), were carried out covering one of the important mountain highways in the Central Himalaya of Indian Himalayan Region (IHR). Landslide influencing factors were either calculated or extracted from spatial databases including recent remote sensing data of LANDSAT TM, CARTOSAT digital elevation model (DEM) and Tropical Rainfall Measuring Mission (TRMM) satellite for rainfall data. ANN was implemented using the multi-layered feed forward architecture with different input, output and hidden layers. This model based on back propagation algorithm derived weights for all possible parameters of landslides and causative factors considered. The training sites for landslide prone and non-prone areas were identified and verified through details gathered from remote sensing and other sources. Frequency Ratio (FR) models are based on observed relationships between the distribution of landslides and each landslide related factor. FR model implementation proved useful for assessing the spatial relationships between landslide locations and factors contributing to its occurrence. Above computational models generated respective susceptibility maps of landslide hazard for the study area. This further allowed the simulation of landslide hazard maps on a medium scale using GIS platform and remote sensing data. Upon validation and accuracy checks, it was observed that both models produced good results with FR having some edge over ANN based mapping. Such statistical and functional models led to better understanding of relationships between the landslides and preparatory factors as well as ensuring lesser levels of subjectivity compared to qualitative approaches.

Using Multi-criteria Evaluation and GIS for Flood Risk Analysis in ...

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were then mapped in the GIS to show the spatial disparities in risk. ..... Ayalew, L. & Yamagishi, H 2005, 'The application of GIS-based logistic regression for landslide .... Saaty, TL 1980, The analytic hierarchy process, Mcgraw-Hill, New York.

Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling.

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Guri, Pardeep Kumar; Ray, P K Champati; Patel, Ramesh Chandra

2015-06-01

Garhwal Himalaya in northern India has emerged as one of the most prominent hot spots of landslide occurrences in the Himalaya mainly due to geological causes related to mountain building processes, steep topography and frequent occurrences of extreme precipitation events. As this region has many pilgrimage and tourist centres, it is visited by hundreds of thousands of people every year, and in the recent past, there has been rapid development to provide adequate roads and building infrastructure. Additionally, attempts are also made to harness hydropower by constructing tunnels, dams and reservoirs and thus altering vulnerable slopes at many places. As a result, the overall risk due to landslide hazards has increased many folds and, therefore, an attempt was made to assess landslide susceptibility using 'Weights of Evidence (WofE)', a well-known bivariate statistical modelling technique implemented in a much improved way using remote sensing and Geographic Information System. This methodology has dual advantage as it demonstrates how to derive critical parameters related to geology, geomorphology, slope, land use and most importantly temporal landslide distribution in one of the data scarce region of the world. Secondly, it allows to experiment with various combination of parameters to assess their cumulative effect on landslides. In total, 15 parameters related to geology, geomorphology, terrain, hydrology and anthropogenic factors and 2 different landslide inventories (prior to 2007 and 2008-2011) were prepared from high-resolution Indian remote sensing satellite data (Cartosat-1 and Resourcesat-1) and were validated by field investigation. Several combinations of parameters were carried out using WofE modelling, and finally using best combination of eight parameters, 76.5 % of overall landslides were predicted in 24 % of the total area susceptible to landslide occurrences. The study has highlighted that using such methodology landslide susceptibility assessment

An integrated approach of analytical network process and fuzzy based spatial decision making systems applied to landslide risk mapping

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Abedi Gheshlaghi, Hassan; Feizizadeh, Bakhtiar

2017-09-01

Landslides in mountainous areas render major damages to residential areas, roads, and farmlands. Hence, one of the basic measures to reduce the possible damage is by identifying landslide-prone areas through landslide mapping by different models and methods. The purpose of conducting this study is to evaluate the efficacy of a combination of two models of the analytical network process (ANP) and fuzzy logic in landslide risk mapping in the Azarshahr Chay basin in northwest Iran. After field investigations and a review of research literature, factors affecting the occurrence of landslides including slope, slope aspect, altitude, lithology, land use, vegetation density, rainfall, distance to fault, distance to roads, distance to rivers, along with a map of the distribution of occurred landslides were prepared in GIS environment. Then, fuzzy logic was used for weighting sub-criteria, and the ANP was applied to weight the criteria. Next, they were integrated based on GIS spatial analysis methods and the landslide risk map was produced. Evaluating the results of this study by using receiver operating characteristic curves shows that the hybrid model designed by areas under the curve 0.815 has good accuracy. Also, according to the prepared map, a total of 23.22% of the area, amounting to 105.38 km2, is in the high and very high-risk class. Results of this research are great of importance for regional planning tasks and the landslide prediction map can be used for spatial planning tasks and for the mitigation of future hazards in the study area.

GEOSTATISTICAL BASED SUSCEPTIBILITY MAPPING OF SOIL EROSION AND OPTIMIZATION OF ITS CAUSATIVE FACTORS: A CONCEPTUAL FRAMEWORK

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ABDULKADIR T. SHOLAGBERU

2017-11-01

Full Text Available Soil erosion hazard is the second biggest environmental challenges after population growth causing land degradation, desertification and water deterioration. Its impacts on watersheds include loss of soil nutrients, reduced reservoir capacity through siltation which may lead to flood risk, landslide, high water turbidity, etc. These problems become more pronounced in human altered mountainous areas through intensive agricultural activities, deforestation and increased urbanization among others. However, due to challenging nature of soil erosion management, there is great interest in assessing its spatial distribution and susceptibility levels. This study is thus intend to review the recent literatures and develop a novel framework for soil erosion susceptibility mapping using geostatistical based support vector machine (SVM, remote sensing and GIS techniques. The conceptual framework is to bridge the identified knowledge gaps in the area of causative factors’ (CFs selection. In this research, RUSLE model, field studies and the existing soil erosion maps for the study area will be integrated for the development of inventory map. Spatial data such as Landsat 8, digital soil and geological maps, digital elevation model and hydrological data shall be processed for the extraction of erosion CFs. GISbased SVM techniques will be adopted for the establishment of spatial relationships between soil erosion and its CFs, and subsequently for the development of erosion susceptibility maps. The results of this study include evaluation of predictive capability of GIS-based SVM in soil erosion mapping and identification of the most influential CFs for erosion susceptibility assessment. This study will serve as a guide to watershed planners and to alleviate soil erosion challenges and its related hazards.

Slope stability susceptibility evaluation parameter (SSEP) rating scheme - An approach for landslide hazard zonation

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Raghuvanshi, Tarun Kumar; Ibrahim, Jemal; Ayalew, Dereje

2014-11-01

In this paper a new slope susceptibility evaluation parameter (SSEP) rating scheme is presented which is developed as an expert evaluation approach for landslide hazard zonation. The SSEP rating scheme is developed by considering intrinsic and external triggering parameters that are responsible for slope instability. The intrinsic parameters which are considered are; slope geometry, slope material (rock or soil type), structural discontinuities, landuse and landcover and groundwater. Besides, external triggering parameters such as, seismicity, rainfall and manmade activities are also considered. For SSEP empirical technique numerical ratings are assigned to each of the intrinsic and triggering parameters on the basis of logical judgments acquired from experience of studies of intrinsic and external triggering factors and their relative impact in inducing instability to the slope. Further, the distribution of maximum SSEP ratings is based on their relative order of importance in contributing instability to the slope. Finally, summation of all ratings for intrinsic and triggering parameter based on actual observation will provide the expected degree of landslide in a given land unit. This information may be utilized to develop a landslide hazard zonation map. The SSEP technique was applied in the area around Wurgessa Kebelle of North Wollo Zonal Administration, Amhara National Regional State in northern Ethiopia, some 490 km from Addis Ababa. The results obtained indicates that 8.33% of the area fall under Moderately hazard and 83.33% fall within High hazard whereas 8.34% of the area fall under Very high hazard. Further, in order to validate the LHZ map prepared during the study, active landslide activities and potential instability areas, delineated through inventory mapping was overlain on it. All active landslide activities and potential instability areas fall within very high and high hazard zone. Thus, the satisfactory agreement confirms the rationality of

Remote Sensing and Geographic Information Systems (GIS Contribution to the Inventory of Infrastructure Susceptible to Earthquake and Flooding Hazards in North-Eastern Greece

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Ioanna Papadopoulou

2012-09-01

Full Text Available For civil protection reasons there is a strong need to improve the inventory of areas that are more vulnerable to earthquake ground motions or to earthquake-related secondary effects, such as landslides, liquefaction or soil amplifications. The use of remote sensing and Geographic Information Systems (GIS methods along with the related geo-databases can assist local and national authorities to be better prepared and organized. Remote sensing and GIS techniques are investigated in north-eastern Greece in order to contribute to the systematic, standardized inventory of those areas that are more susceptible to earthquake ground motions, to earthquake-related secondary effects and to tsunami-waves. Knowing areas with aggregated occurrence of causal (“negative” factors influencing earthquake shock and, thus, the damage intensity, this knowledge can be integrated into disaster preparedness and mitigation measurements. The evaluation of satellite imageries, digital topographic data and open source geodata contributes to the acquisition of the specific tectonic, geologic and geomorphologic settings influencing local site conditions in an area and, thus, estimate possible damage to be suffered.

Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

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Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

2017-11-01

The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

Geospatial Assessment of Coseismic Landslides in Baturagung Area

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Aditya Saputra

2016-02-01

Full Text Available Java, the most densely populated island in Indonesia, is located on top of the most seismically active areas in Southeast Asia: the Sunda Megathrust. This area is frequently hit by strong earthquake. More than 3,300 M>5earthquakesoccurred between 1973-2014. The wide range of mountainous areas and high intensity of rainfall, make several part of the island one of the most exposed regions for coseismic landslides such as Baturagung area, the Southeast mountainous area of Yogyakarta Province. An integrated method between RS and GIS was used to conduct the vulnerability assessment due to the lack of the site specific slope instability analysis and coseismic landslides data. The seismic zonation of Baturagung area was obtained based on the analysis of Kanai attenuation. The geologic information was extracted using remote sensing interpretation based on the 1:100,000 geologic map of Yogyakarta and geomorphologic map of Baturagung area as well. The coseismic landslide hazard assessment has been estimated using scoring analysis in the GIS platform proposed by Mora and Vahrson (1993 with several modification. The accomplished coseismic landslide hazard map shows medium hazard coverage in the eastern areas, in the upper slope of Baturagung area, which consists of Semilir Formation. The result provides a distinct description of coseismic landslides hazard distribution in Batuaragung area. However, it should only be the preliminary assessment of the site specific investigation especially on valuable area or asset.

Landslide hazard and risk assessment using semi-automatically created landslide inventories

NARCIS (Netherlands)

Martinez, J.A.; van Westen, C.J.; Kerle, N.; Jetten, V.G.; Kumar, K.V.

2013-01-01

Landslide inventories prepared manually from remote sensing data or through field surveys have shown to be useful for preparation of landslide susceptibility and hazard maps. Recent literatures show several studies have been carried out to prepare landslide inventories from satellite data by

Determination of Areas Susceptible to Landsliding Using Spatial Patterns of Rainfall from Tropical Rainfall Measuring Mission Data, Rio de Janeiro, Brazil

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Renato Fontes Guimarães

2017-10-01

Full Text Available Spatial patterns of shallow landslide initiation reflect both spatial patterns of heavy rainfall and areas susceptible to mass movements. We determine the areas most susceptible to shallow landslide occurrence through the calculation of critical soil cohesion and spatial patterns of rainfall derived from TRMM (Tropical Rainfall Measuring Mission data for Paraty County, State of Rio de Janeiro, Brazil. Our methodology involved: (a creating the digital elevation model (DEM and deriving attributes such as slope and contributing area; (b incorporating spatial patterns of rainfall derived from TRMM into the shallow slope stability model SHALSTAB; and (c quantitative assessment of the correspondence of mapped landslide scars to areas predicted to be most prone to shallow landsliding. We found that around 70% of the landslide scars occurred in less than 10% of the study area identified as potentially unstable. The greatest concentration of landslides occurred in areas where the root strength of vegetation is an important contribution to slope stability in regions of orographically-enhanced rainfall on the coastal topographic flank. This approach helps quantify landslide hazards in areas with similar geomorphological characteristics, but different spatial patterns of rainfall.

Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia.

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Pradhan, Biswajeet; Chaudhari, Amruta; Adinarayana, J; Buchroithner, Manfred F

2012-01-01

In this paper, an attempt has been made to assess, prognosis and observe dynamism of soil erosion by universal soil loss equation (USLE) method at Penang Island, Malaysia. Multi-source (map-, space- and ground-based) datasets were used to obtain both static and dynamic factors of USLE, and an integrated analysis was carried out in raster format of GIS. A landslide location map was generated on the basis of image elements interpretation from aerial photos, satellite data and field observations and was used to validate soil erosion intensity in the study area. Further, a statistical-based frequency ratio analysis was carried out in the study area for correlation purposes. The results of the statistical correlation showed a satisfactory agreement between the prepared USLE-based soil erosion map and landslide events/locations, and are directly proportional to each other. Prognosis analysis on soil erosion helps the user agencies/decision makers to design proper conservation planning program to reduce soil erosion. Temporal statistics on soil erosion in these dynamic and rapid developments in Penang Island indicate the co-existence and balance of ecosystem.

IDENTIFICATION OF LANDSLIDES SUSCEPTIBILITY IN THE DOBRIC CATCHMENT AREA USING THE FREQUENCY RATE MODEL

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ROXANA VĂIDEAN

2013-11-01

Full Text Available The landslides susceptibility of the Dobric catchment area (Ilişua river. The territorial geomorfological investigation focuses mainly on the analysis of the present situation, as context of future events occurrence. The previous evolutionary context is secondary in place due also to the particular attention it has received so far. The significance of the knowledge regarding the present events and their evolution is explicit in the attempt to mitigate their impact on the built area and on the resources. The identification of areas characterized by maximum susceptibility in the landslides occurrence is absolutely necessary. The method which makes the identification of these areas possible is none other than the method considering the conditional factors, as well as the spatial distribution of the events that have already occurred. In this regard, the use of the frequency rate model is considered to be ideal.

GIS-based soil liquefaction susceptibility map of Mumbai city for earthquake events

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Mhaske, Sumedh Yamaji; Choudhury, Deepankar

2010-03-01

The problem of liquefaction of soil during seismic event is one of the important topics in the field of Geotechnical Earthquake Engineering. Liquefaction of soil is generally occurs in loose cohesionless saturated soil when pore water pressure increases suddenly due to induced ground motion and shear strength of soil decreases to zero and leading the structure situated above to undergo a large settlement, or failure. The failures took place due to liquefaction induced soil movement spread over few square km area continuously. Hence this is a problem where spatial variation involves and to represent this spatial variation Geographic Information System (GIS) is very useful in decision making about the area subjected to liquefaction. In this paper, GIS software GRAM++ is used to prepare soil liquefaction susceptibility map for entire Mumbai city in India by marking three zones viz. critically liquefiable soil, moderately liquefiable soil and non liquefiable soil. Extensive field borehole test data for groundwater depth, standard penetration test (SPT) blow counts, dry density, wet density and specific gravity, etc. have been collected from different parts of Mumbai. Simplified procedure of Youd et al. (2001) is used for calculation of factor of safety against soil liquefaction potential. Mumbai city and suburban area are formed by reclaiming lands around seven islands since 1865 till current date and still it is progressing in the area such as Navi Mumbai and beyond Borivali to Mira road suburban area. The factors of safety against soil liquefaction were determined for earthquake moment magnitude ranging from Mw = 5.0 to 7.5. It is found that the areas like Borivali, Malad, Dahisar, Bhandup may prone to liquefaction for earthquake moment magnitude ranging from Mw = 5.0 to 7.5. The liquefaction susceptibility maps were created by using GRAM++ by showing the areas where the factor of safety against the soil liquefaction is less than one. Proposed liquefaction

Expert opinion on landslide susceptibility elicted by probabilistic inversion from scenario rankings

Science.gov (United States)

Lee, Katy; Dashwood, Claire; Lark, Murray

2016-04-01

For many natural hazards the opinion of experts, with experience in assessing susceptibility under different circumstances, is a valuable source of information on which to base risk assessments. This is particularly important where incomplete process understanding, and limited data, limit the scope to predict susceptibility by mechanistic or statistical modelling. The expert has a tacit model of a system, based on their understanding of processes and their field experience. This model may vary in quality, depending on the experience of the expert. There is considerable interest in how one may elicit expert understanding by a process which is transparent and robust, to provide a basis for decision support. One approach is to provide experts with a set of scenarios, and then to ask them to rank small overlapping subsets of these with respect to susceptibility. Methods of probabilistic inversion have been used to compute susceptibility scores for each scenario, implicit in the expert ranking. It is also possible to model these scores as functions of measurable properties of the scenarios. This approach has been used to assess susceptibility of animal populations to invasive diseases, to assess risk to vulnerable marine environments and to assess the risk in hypothetical novel technologies for food production. We will present the results of a study in which a group of geologists with varying degrees of expertise in assessing landslide hazards were asked to rank sets of hypothetical simplified scenarios with respect to land slide susceptibility. We examine the consistency of their rankings and the importance of different properties of the scenarios in the tacit susceptibility model that their rankings implied. Our results suggest that this is a promising approach to the problem of how experts can communicate their tacit model of uncertain systems to those who want to make use of their expertise.

Application of a complex assessment of landslide hazards in mountain regions

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Kateryna E. Boyko

2017-09-01

Full Text Available The main regional factors of occurrence and activation of landslides within the mountain region were examined. As a result of study of recommendations made by experts, geologists, and gap analysis of existing methods of forecasting the landslide process, an algorithm of comprehensive assessment of landslide hazard areas based on the construction of models in a GIS environment was proposed. These models describe the spatial patterns of landslides. All factors determining the tendency of the studies area to the landslide process development were divided into actual factors, reflecting the regional peculiarities of the territory and forming the landslide-prone slopes (static model, as well as triggering factors, initiating the landslide process and determining its activity (dynamic model. The first cartographic model was built, showing the distribution of the deterministic indirect indicator of landslide hazard, i.e. stability index.

Landslides in Flanders (Belgium): Where science meets public policy

Science.gov (United States)

van den Eeckhaut, M.; Poesen, J.; Vandekerckhove, L.

2009-04-01

Although scientific research on landslides in the Flemish Ardennes (710 km²; Belgium), has been conducted over the last decades, the Flemish Government only took account of slope failure as a soil degradation process after the occurrence of several damaging landslides in the beginning of the 21st century. Here we aim to present the successful collaboration between the Physical and Regional Geography Research Group (FRG; Dept. Earth and Environmental Sciences K.U.Leuven) and the Environment, Nature and Energy Department (LNE; Flemish Government) in landslide management. We will demonstrate how geomorphologists produced practical tools for landslide management which can be directly applied by LNE as well as other local and regional authorities and planners. Since 2004 three projects on landslide inventory mapping and susceptibility assessment in the Flemish Ardennes have been funded by LNE, and a fourth one on landslide susceptibility assessment in remaining hilly regions in Flanders west of Brussels recently started. Together with a steering committee composed of stakeholders, persons from LNE supervise the research carried out by geomorphologists experienced in landslide studies. For the establishment of the landslide inventory map of the Flemish Ardennes we combined the analysis of LIDAR-derived hillshade and contour line maps with detailed field controls. Additional information was collected through interviews with local authorities and inhabitants and from analysis of newspaper articles and technical reports. Then, a statistical model, logistic regression, was applied to produce a high quality classified landslide susceptibility map. The unique part of this collaboration is that all end products are online available at user-friendly websites designed by LNE. The scientific report containing (1) general information on landslides, (2) a description of the study area, (3) an explanation of the materials and methods used, (4) a presentation of the resulting

Landslide-susceptibility analysis using light detection and ranging-derived digital elevation models and logistic regression models: a case study in Mizunami City, Japan

Science.gov (United States)

Wang, Liang-Jie; Sawada, Kazuhide; Moriguchi, Shuji

2013-01-01

To mitigate the damage caused by landslide disasters, different mathematical models have been applied to predict landslide spatial distribution characteristics. Although some researchers have achieved excellent results around the world, few studies take the spatial resolution of the database into account. Four types of digital elevation model (DEM) ranging from 2 to 20 m derived from light detection and ranging technology to analyze landslide susceptibility in Mizunami City, Gifu Prefecture, Japan, are presented. Fifteen landslide-causative factors are considered using a logistic-regression approach to create models for landslide potential analysis. Pre-existing landslide bodies are used to evaluate the performance of the four models. The results revealed that the 20-m model had the highest classification accuracy (71.9%), whereas the 2-m model had the lowest value (68.7%). In the 2-m model, 89.4% of the landslide bodies fit in the medium to very high categories. For the 20-m model, only 83.3% of the landslide bodies were concentrated in the medium to very high classes. When the cell size decreases from 20 to 2 m, the area under the relative operative characteristic increases from 0.68 to 0.77. Therefore, higher-resolution DEMs would provide better results for landslide-susceptibility mapping.

Landslide and flood hazard assessment in urban areas of Levoča region (Eastern Slovakia)

Science.gov (United States)

Magulova, Barbora; Caporali, Enrica; Bednarik, Martin

2010-05-01

The case study presents the use of statistical methods and analysis tools, for hazard assessment of "urbanization units", implemented in a Geographic Information Systems (GIS) environment. As a case study, the Levoča region (Slovakia) is selected. The region, with a total area of about 351 km2, is widely affected by landslides and floods. The problem, for small urbanization areas, is nowadays particularly significant from the socio-economic point of view. It is considered, presently, also an increasing problem, mainly because of climate change and more frequent extreme rainfall events. The geo-hazards are evaluated using a multivariate analysis. The landslide hazard assessment is based on the comparison and subsequent statistical elaboration of territorial dependence among different input factors influencing the instability of the slopes. Particularly, five factors influencing slope stability are evaluated, i.e. lithology, slope aspect, slope angle, hypsographic level and present land use. As a result a new landslide susceptibility map is compiled and different zones of stable, dormant and non-stable areas are defined. For flood hazard map a detailed digital elevation model is created. A compose index of flood hazard is derived from topography, land cover and pedology related data. To estimate flood discharge, time series of stream flow and precipitation measurements are used. The assessment results are prognostic maps of landslide hazard and flood hazard, which presents the optimal base for urbanization planning.

Landslide prediction system for rainfall induced landslides in Slovenia (Masprem

Directory of Open Access Journals (Sweden)

Mateja Jemec Auflič

2016-12-01

Full Text Available In this paper we introduce a landslide prediction system for modelling the probabilities of landslides through time in Slovenia (Masprem. The system to forecast rainfall induced landslides is based on the landslide susceptibility map, landslide triggering rainfall threshold values and the precipitation forecasting model. Through the integrated parameters a detailed framework of the system, from conceptual to operational phases, is shown. Using fuzzy logic the landslide prediction is calculated. Potential landslide areas are forecasted on a national scale (1: 250,000 and on a local scale (1: 25,000 for fie selected municipalities where the exposure of inhabitants, buildings and different type of infrastructure is displayed, twice daily. Due to different rainfall patterns that govern landslide occurrences, the system for landslide prediction considers two different rainfall scenarios (M1 and M2. The landslides predicted by the two models are compared with a landslide inventory to validate the outputs. In this study we highlight the rainfall event that lasted from the 9th to the 14th of September 2014 when abundant precipitation triggered over 800 slope failures around Slovenia and caused large material damage. Results show that antecedent rainfall plays an important role, according to the comparisons of the model (M1 where antecedent rainfall is not considered. Although in general the landslides areas are over-predicted and largely do not correspond to the landslide inventory, the overall performance indicates that the system is able to capture the crucial factors in determining the landslide location. Additional calibration of input parameters and the landslide inventory as well as improved spatially distributed rainfall forecast data can further enhance the model's prediction.

Impacts of Landuse Management and Climate Change on Landslides Susceptibility over the Olympic Peninsula of Washington State

Science.gov (United States)

Barik, M. G.; Adam, J. C.

2009-12-01

The commercial forests on the western side of the Olympic Mountains in Washington State are a region of steep slopes and high annual rainfall (2500-6000 mm/year) and are therefore highly susceptible to landslides. Potential climatic change (more intense and frequent winter storms) may exacerbate landslide susceptibility unless forest management practices are changed. As this area is a critical habitat for numerous organisms, including salmon, this may result in potentially severe consequences to riparian habitat due to increased sediment loads. Therefore, there is a need to investigate potential forest management plans to promote the economic viability of timber extraction while protecting the natural habitat, particularly in riparian areas. The objective of this study is to predict the long term effects of forest management decisions under projected climate change on slope stability. We applied the physically-based Distributed Hydrology Soil Vegetation Model (DHSVM) with its sediment module to simulate mass wasting and sediment delivery under different vegetation and climate scenarios. Sub-basins were selected and classified according to elevation, slope, land cover and soil type. Various land management practices (such as clear-cutting in riparian areas, logging under short rotations, varying amount of timbers left intact in riparian areas) were applied to each of the selected sub-basins. DHSVM was used to simulate landslide volume, frequency, and sediment loads for each of the land cover applications under various future climate scenarios. We comment on the suitability of various harvesting techniques for different parts of the forest to minimize landslide-induced sediment loading to streams in an altered climate. This approach can be developed as a decision making tool that can be used by forest managers to make long-term planning decisions.

Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy)

KAUST Repository

Lombardo, Luigi

2016-06-29

This study evaluates the performances of the presence-only approach, Maximum Entropy, in assessing landslide triggering-thickness susceptibility within the Mili catchment (Sicily, Italy). This catchment underwent several meteorological stresses, resulting in hundreds of shallow rapid mass movements between 2007 and 2011. In particular, the area has become known for two disasters, which occurred in 2009 and 2010; the first weather system did not pass directly over the catchment; however, peak rainfall was registered over the basin during the second meteorological event. Field data were collected to associate the depth from the slope surface that material was mobilised at the triggering zone to each mass movement within the catchment. This information has been used to model the landslide susceptibility for two classes of processes, divided into shallow failures for maximum depths of 1 m and deep ones in case of values equal or greater than 1 m. Topographic attributes from a 2-m DEM were used as predictors, together with medium resolution vegetation indexes derived from ASTER scenes and geological, land use and tectonic maps. The presence-only approach discriminated between the two depth classes at the landslide trigger zone, producing excellent prediction skills associated with relatively low variances across a set of 50 randomly generated replicates. The role of each predictor was assessed to ascertain the significance to the final model output. This work uses simple field measurements to produce triggering-thickness susceptibility, which is a novel approach and may perform better as a proxy for landslide hazard assessments with respect to more common susceptibility practises. © 2016, Springer Science+Business Media Dordrecht.

Shallow landslide stability computation using a distributed transient response model for susceptibility assessment and validation. A case study from Ribeira Quente valley (S. Miguel island, Azores)

Science.gov (United States)

Amaral, P.; Marques, R.; Zêzere, J. L.; Marques, F.; Queiroz, G.

2009-04-01

based system that will publish the FS values to a WebGIS platform, based on near real time ground-based rainfall monitoring. This application will allow the evaluation of scenarios considering the variation of the pressure head response, related to transient rainfall regime. The resultant computational platform combined with regional empirical rainfall triggered landslides threshold (Marques et al. 2008) can be incorporated in a common server with the Regional Civil Protection for emergency planning purposes. This work is part of the project VOLCSOILRISK (Volcanic Soils Geotechnical Characterization for Landslide Risk Mitigation), supported by Direcção Regional da Ciência e Tecnologia do Governo Regional dos Açores. References: IVERSON, R.M. (2000) - Landslide triggering by rain infiltration. Water Resources Research 36, 1897-1910. MARQUES, R., ZÊZERE, J.L., TRIGO, R., GASPAR, J.L., TRIGO, I. (2008) - Rainfall patterns and critical values associated with landslides in Povoação County (São Miguel Island, Azores): relationships with the North Atlantic Oscillation. Hydrol. Process. 22, 478-494. DOI: 10.1002/hyp.6879.

Spatial agreement of predicted patterns in landslide susceptibility maps

Czech Academy of Sciences Publication Activity Database

Sterlacchini, S.; Ballabio, C.; Blahůt, Jan; Masetti, J.; Sorichetta, A.

2011-01-01

Roč. 125, č. 1 (2011), s. 51-61 ISSN 0169-555X Institutional research plan: CEZ:AV0Z30460519 Keywords : landslide susceptibility * weights of evidence * success rate Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 2.520, year: 2011 http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V93-511TN6P-3&_user=1407143&_coverDate=01%2F01%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000052620&_version=1&_urlVersion=0&_userid=1407143&md5=d4e3ac0e1295373f2203f99a1aa8e905&searchtype=a

Assessing Landslide Characteristics and Developing a Landslide Potential Hazard Map in Rwanda and Uganda Using NASA Earth Observations

Science.gov (United States)

Sinclair, L.; Conner, P.; le Roux, J.; Finley, T.

2015-12-01

The International Emergency Disasters Database indicates that a total of 482 people have been killed and another 27,530 have been affected by landslides in Rwanda and Uganda, although the actual numbers are thought to be much higher. Data for individual countries are poorly tracked, but hotspots for devastating landslides occur throughout Rwanda and Uganda due to the local topography and soil type, intense rainfall events, and deforestation. In spite of this, there has been little research in this region that utilizes satellite imagery to estimate areas susceptible to landslides. This project utilized Landsat 8 Operational Land Imager (OLI) data and Google Earth to identify landslides that occurred within the study area. These landslides were then added to SERVIR's Global Landslide Catalog (GLC). Next, Landsat 8 OLI, the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM), and Shuttle Radar Topography Mission Version 2 (SRTM V2) data were used to create a Landslide Susceptibility Map. This was combined with population data from the Socioeconomic Data and Applications Center (SEDAC) to create a Landslide Hazard map. A preliminary assessment of the relative performance of GPM and TRMM in identifying landslide conditions was also performed. The additions to the GLC, the Landslide Susceptibility Map, the Landslide Hazard Map, and the preliminary assessment of satellite rainfall performance will be used by SERVIR and the Regional Centre for Mapping of Resources for Development (RCMRD) for disaster risk management, land use planning, and determining landslide conditions and moisture thresholds.

Integrating the effects of forest cover on slope stability in a deterministic landslide susceptibility model (TRIGRS 2.0)

Science.gov (United States)

Zieher, T.; Rutzinger, M.; Bremer, M.; Meissl, G.; Geitner, C.

2014-12-01

The potentially stabilizing effects of forest cover in respect of slope stability have been the subject of many studies in the recent past. Hence, the effects of trees are also considered in many deterministic landslide susceptibility models. TRIGRS 2.0 (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability; USGS) is a dynamic, physically-based model designed to estimate shallow landslide susceptibility in space and time. In the original version the effects of forest cover are not considered. As for further studies in Vorarlberg (Austria) TRIGRS 2.0 is intended to be applied in selected catchments that are densely forested, the effects of trees on slope stability were implemented in the model. Besides hydrological impacts such as interception or transpiration by tree canopies and stems, root cohesion directly influences the stability of slopes especially in case of shallow landslides while the additional weight superimposed by trees is of minor relevance. Detailed data on tree positions and further attributes such as tree height and diameter at breast height were derived throughout the study area (52 km²) from high-resolution airborne laser scanning data. Different scenarios were computed for spruce (Picea abies) in the study area. Root cohesion was estimated area-wide based on published correlations between root reinforcement and distance to tree stems depending on the stem diameter at breast height. In order to account for decreasing root cohesion with depth an exponential distribution was assumed and implemented in the model. Preliminary modelling results show that forest cover can have positive effects on slope stability yet strongly depending on tree age and stand structure. This work has been conducted within C3S-ISLS, which is funded by the Austrian Climate and Energy Fund, 5th ACRP Program.

926 School of Water Resour

African Journals Online (AJOL)

USER

2015-11-09

Nov 9, 2015 ... Soil Loss Equation (USLE) coupled with GIS was used to identify areas that are susceptible for soil erosion and ... and the soil loss due to landslide alone in the past 20 ..... Remote sensing based NDVI data and. GIS based ...

Directions of the US Geological Survey Landslide Hazards Reduction Program

Science.gov (United States)

Wieczorek, G.F.

1993-01-01

The US Geological Survey (USGS) Landslide Hazards Reduction Program includes studies of landslide process and prediction, landslide susceptibility and risk mapping, landslide recurrence and slope evolution, and research application and technology transfer. Studies of landslide processes have been recently conducted in Virginia, Utah, California, Alaska, and Hawaii, Landslide susceptibility maps provide a very important tool for landslide hazard reduction. The effects of engineering-geologic characteristics of rocks, seismic activity, short and long-term climatic change on landslide recurrence are under study. Detailed measurement of movement and deformation has begun on some active landslides. -from Author

Research on the evolution model and deformation mechanisms of Baishuihe landslide based on analyzing geologic process of slope

Science.gov (United States)

Zhang, S.; Tang, H.; Cai, Y.; Tan, Q.

2016-12-01

The landslide is a result of both inner and exterior geologic agents, and inner ones always have significant influences on the susceptibility of geologic bodies to the exterior ones. However, current researches focus more on impacts of exterior factors, such as precipitation and reservoir water, than that of geologic process. Baishuihe landslide, located on the south bank of Yangtze River and 56km upstream from the Three Gorges Project, was taken as the study subject with the in-situ investigation and exploration carried out for the first step. After the spatial analysis using the 3D model of topography built by ArcGIS (Fig.1), geologic characteristics of the slope that lies in a certain range near the Baishuihe landslide on the same bank were investigated for further insights into geologic process of the slope, with help of the geological map and structure outline map. Baishuihe landslide developed on the north limb of Baifuping anticline, a dip slope on the southwest margin of Zigui basin. The eastern and western boundaries are both ridges and in the middle a distinct slide depression is in process of deforming. Evolutionary process of Baishuihe landslide includes three steps below. 1) Emergence of Baifuping anticline leaded to interbedded dislocation, tension cracks and joint fractures in bedrocks. 2) Weathering continuously weakened strength of soft interlayers in the Shazhenxi Formation (T3s). 3) Rock slide caused by neotectonics happened on a large scale along the weak layers and joint planes, forming initial Baishuihe landslide. Although the landslide has undergone reconstruction for a long time, it could still be divided clearly into two parts, namely a) the rock landslide at the back half (south) and b) the debris landslide at the front half (north). a) The deformation mechanism for the rock landslide is believed to be deterioration in strength of weak bedding planes due to precipitation and free face caused by human activities or river incision. b

A satellite-based global landslide model

Directory of Open Access Journals (Sweden)

A. Farahmand

2013-05-01

Full Text Available Landslides are devastating phenomena that cause huge damage around the world. This paper presents a quasi-global landslide model derived using satellite precipitation data, land-use land cover maps, and 250 m topography information. This suggested landslide model is based on the Support Vector Machines (SVM, a machine learning algorithm. The National Aeronautics and Space Administration (NASA Goddard Space Flight Center (GSFC landslide inventory data is used as observations and reference data. In all, 70% of the data are used for model development and training, whereas 30% are used for validation and verification. The results of 100 random subsamples of available landslide observations revealed that the suggested landslide model can predict historical landslides reliably. The average error of 100 iterations of landslide prediction is estimated to be approximately 7%, while approximately 2% false landslide events are observed.

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

Science.gov (United States)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

Collaborative GIS for flood susceptibility mapping: An example from Mekong river basin of Viet Nam

Science.gov (United States)

Thanh, B.

2016-12-01

Flooding is one of the most dangerous natural disasters in Vietnam. Floods have caused serious damages to people and made adverse impact on social economic development across the country, especially in lower river basin where there is high risk of flooding as consequences of the climate change and social activities. This paper presents a collaborative platform of a combination of an interactive web-GIS framework and a multi-criteria evaluation (MCE) tool. MCE is carried out in server side through web interface, in which parameters used for evaluation are groups into three major categories, including (1) climatic factor: precipitation, typhoon frequency, temperature, humidity (2) physiographic data: DEM, topographic wetness index, NDVI, stream power index, soil texture, distance to river (3) social factor: NDBI, land use pattern. Web-based GIS is based on open-source technology that includes an information page, a page for MCE tool that users can interactively alter parameters in flood susceptible mapping, and a discussion page. The system is designed for local participation in prediction of the flood risk magnitude under impacts of natural processes and human intervention. The proposed flood susceptibility assessment prototype was implemented in the Mekong river basin, Viet Nam. Index images were calculated using Landsat data, and other were collected from authorized agencies. This study shows the potential to combine web-GIS and spatial analysis tool to flood hazard risk assessment. The combination can be a supportive solution that potentially assists the interaction between stakeholders in information exchange and in disaster management, thus provides for better analysis, control and decision-making.

Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

Science.gov (United States)

Melchiorre, C.; Castellanos Abella, E. A.; van Westen, C. J.; Matteucci, M.

2011-04-01

This paper describes a procedure for landslide susceptibility assessment based on artificial neural networks, and focuses on the estimation of the prediction capability, robustness, and sensitivity of susceptibility models. The study is carried out in the Guantanamo Province of Cuba, where 186 landslides were mapped using photo-interpretation. Twelve conditioning factors were mapped including geomorphology, geology, soils, landuse, slope angle, slope direction, internal relief, drainage density, distance from roads and faults, rainfall intensity, and ground peak acceleration. A methodology was used that subdivided the database in 3 subsets. A training set was used for updating the weights. A validation set was used to stop the training procedure when the network started losing generalization capability, and a test set was used to calculate the performance of the network. A 10-fold cross-validation was performed in order to show that the results are repeatable. The prediction capability, the robustness analysis, and the sensitivity analysis were tested on 10 mutually exclusive datasets. The results show that by means of artificial neural networks it is possible to obtain models with high prediction capability and high robustness, and that an exploration of the effect of the individual variables is possible, even if they are considered as a black-box model.

A combined field/remote sensing approach for characterizing landslide risk in coastal areas

Science.gov (United States)

Francioni, Mirko; Coggan, John; Eyre, Matthew; Stead, Doug

2018-05-01

Understanding the key factors controlling slope failure mechanisms in coastal areas is the first and most important step for analyzing, reconstructing and predicting the scale, location and extent of future instability in rocky coastlines. Different failure mechanisms may be possible depending on the influence of the engineering properties of the rock mass (including the fracture network), the persistence and type of discontinuity and the relative aspect or orientation of the coastline. Using a section of the North Coast of Cornwall, UK, as an example we present a multi-disciplinary approach for characterizing landslide risk associated with coastal instabilities in a blocky rock mass. Remotely captured terrestrial and aerial LiDAR and photogrammetric data were interrogated using Geographic Information System (GIS) techniques to provide a framework for subsequent analysis, interpretation and validation. The remote sensing mapping data was used to define the rock mass discontinuity network of the area and to differentiate between major and minor geological structures controlling the evolution of the North Coast of Cornwall. Kinematic instability maps generated from aerial LiDAR data using GIS techniques and results from structural and engineering geological surveys are presented. With this method, it was possible to highlight the types of kinematic failure mechanism that may generate coastal landslides and highlight areas that are more susceptible to instability or increased risk of future instability. Multi-temporal aerial LiDAR data and orthophotos were also studied using GIS techniques to locate recent landslide failures, validate the results obtained from the kinematic instability maps through site observations and provide improved understanding of the factors controlling the coastal geomorphology. The approach adopted is not only useful for academic research, but also for local authorities and consultancy's when assessing the likely risks of coastal instability.

DETECTION OF LOCAL SITE CONDITIONS INFLUENCING EARTHQUAKE SHOCK AND SECONDARY EFFECTS IN THE VALPARAISO AREA IN CENTRAL-CHILE USING REMOTE SENSING AND GIS METHODS

Directory of Open Access Journals (Sweden)

Barbara Theilen-Willige

2011-01-01

Full Text Available The potential contribution of remote sensing and GIS techniques to earthquake hazard analysis was investigated in Valparaiso in Chile in order to improve the systematic, standardized inventory of those areas that are more susceptible to earthquake ground motions or to earthquake related secondary effects such as landslides, liquefaction, soil amplifications, compaction or even tsunami-waves. Geophysical, topographical, geological data and satellite images were collected, processed, and integrated into a spatial database using Geoinformation Systems (GIS and image processing techniques. The GIS integrated evaluation of satellite imageries, of digital topographic data and of various open-source geodata can contribute to the acquisition of those specific tectonic, geomorphologic/ topographic settings influencing local site conditions in Valparaiso, Chile. Using the weighted overlay techniques in GIS, susceptibility maps were produced indicating areas, where causal factors influencing near- surface earthquake shock occur aggregated. Causal factors (such as unconsolidated sedimentary layers within a basin’s topography, higher groundwater tables, etc. summarizing and interfering each other, rise the susceptibility of soil amplification and of earthquake related secondary effects. This approach was used as well to create a tsunami flooding susceptibility map. LANDSAT Thermal Band 6-imageries were analysed to get information of surface water currents in this area.

Hazard, Vulnerability and Capacity Mapping for Landslides Risk Analysis using Geographic Information System (GIS)

Science.gov (United States)

Sari, D. A. P.; Innaqa, S.; Safrilah

2017-06-01

This research analyzed the levels of disaster risk in the Citeureup sub-District, Bogor Regency, West Java, based on its potential hazard, vulnerability and capacity, using map to represent the results, then Miles and Huberman analytical techniques was used to analyze the qualitative interviews. The analysis conducted in this study is based on the concept of disaster risk by Wisner. The result shows that the Citeureup sub-District has medium-low risk of landslides. Of the 14 villages, three villages have a moderate risk level, namely Hambalang, Tajur, and Tangkil, or 49.58% of the total land area. Eleven villages have a low level of risk, namely Pasir Mukti, Sanja, Tarikolot, Gunung Sari, Puspasari, East Karang Asem, Citeureup, Leuwinutug, Sukahati, West Karang Asem West and Puspanegara, or 48.68% of the total land area, for high-risk areas only around 1.74%, which is part of Hambalang village. The analysis using Geographic Information System (GIS) prove that areas with a high risk potential does not necessarily have a high level of risk. The capacity of the community plays an important role to minimize the risk of a region. Disaster risk reduction strategy is done by creating a safe condition, which intensified the movement of disaster risk reduction.

Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy)

KAUST Repository

Lombardo, Luigi; Fubelli, G.; Amato, G.; Bonasera, M.

2016-01-01

This study evaluates the performances of the presence-only approach, Maximum Entropy, in assessing landslide triggering-thickness susceptibility within the Mili catchment (Sicily, Italy). This catchment underwent several meteorological stresses

Investigating landslides caused by earthquakes - A historical review

Science.gov (United States)

Keefer, D.K.

2002-01-01

Post-earthquake field investigations of landslide occurrence have provided a basis for understanding, evaluating, and mapping the hazard and risk associated with earthquake-induced landslides. This paper traces the historical development of knowledge derived from these investigations. Before 1783, historical accounts of the occurrence of landslides in earthquake are typically so incomplete and vague that conclusions based on these accounts are of limited usefulness. For example, the number of landslides triggered by a given event is almost always greatly underestimated. The first formal, scientific post-earthquake investigation that included systematic documentation of the landslides was undertaken in the Calabria region of Italy after the 1783 earthquake swarm. From then until the mid-twentieth century, the best information on earthquake-induced landslides came from a succession of post-earthquake investigations largely carried out by formal commissions that undertook extensive ground-based field studies. Beginning in the mid-twentieth century, when the use of aerial photography became widespread, comprehensive inventories of landslide occurrence have been made for several earthquakes in the United States, Peru, Guatemala, Italy, El Salvador, Japan, and Taiwan. Techniques have also been developed for performing "retrospective" analyses years or decades after an earthquake that attempt to reconstruct the distribution of landslides triggered by the event. The additional use of Geographic Information System (GIS) processing and digital mapping since about 1989 has greatly facilitated the level of analysis that can applied to mapped distributions of landslides. Beginning in 1984, synthesis of worldwide and national data on earthquake-induced landslides have defined their general characteristics and relations between their occurrence and various geologic and seismic parameters. However, the number of comprehensive post-earthquake studies of landslides is still

Investigating Landslides Caused by Earthquakes A Historical Review

Science.gov (United States)

Keefer, David K.

Post-earthquake field investigations of landslide occurrence have provided a basis for understanding, evaluating, and mapping the hazard and risk associated withearthquake-induced landslides. This paper traces thehistorical development of knowledge derived from these investigations. Before 1783, historical accounts of the occurrence of landslides in earthquakes are typically so incomplete and vague that conclusions based on these accounts are of limited usefulness. For example, the number of landslides triggered by a given event is almost always greatly underestimated. The first formal, scientific post-earthquake investigation that included systematic documentation of the landslides was undertaken in the Calabria region of Italy after the 1783 earthquake swarm. From then until the mid-twentieth century, the best information on earthquake-induced landslides came from a succession ofpost-earthquake investigations largely carried out by formal commissions that undertook extensive ground-based field studies. Beginning in the mid-twentieth century, when the use of aerial photography became widespread, comprehensive inventories of landslide occurrence have been made for several earthquakes in the United States, Peru, Guatemala, Italy, El Salvador, Japan, and Taiwan. Techniques have also been developed for performing ``retrospective'' analyses years or decades after an earthquake that attempt to reconstruct the distribution of landslides triggered by the event. The additional use of Geographic Information System (GIS) processing and digital mapping since about 1989 has greatly facilitated the level of analysis that can applied to mapped distributions of landslides. Beginning in 1984, syntheses of worldwide and national data on earthquake-induced landslides have defined their general characteristics and relations between their occurrence and various geologic and seismic parameters. However, the number of comprehensive post-earthquake studies of landslides is still

Comparison between monitored and modeled pore water pressure and safety factor in a slope susceptible to shallow landslides

Science.gov (United States)

Bordoni, Massimiliano; Meisina, Claudia; Zizioli, Davide; Valentino, Roberto; Bittelli, Marco; Chersich, Silvia

2014-05-01

Shallow landslides can be defined as slope movements affecting superficial deposits of small thicknesses which are usually triggered due to extreme rainfall events, also very concentrated in time. Shallow landslides are hazardous phenomena: in particular, if they happen close to urbanized areas they could cause significant damages to cultivations, structures, infrastructures and, sometimes, human losses. The triggering mechanism of rainfall-induced shallow landslides is strictly linked with the hydrological and mechanical responses of usually unsaturated soils to rainfall events. For this reason, it is fundamental knowing the intrinsic hydro-mechanical properties of the soils in order to assess both susceptibility and hazard of shallow landslide and to develop early-warning systems at large scale. The hydrological data collected by a 20 months monitoring on a slope susceptible to shallow landslides in an area of the North -Eastern Oltrepo Pavese (Northern Apennines, Italy) were used to identify the hydrological behaviors of the investigated soils towards rainfall events. Field conditions under different rainfall trends have also been modeled by using both hydrological and physically-based stability models for the evaluation of the slope safety factor . The main objectives of this research are: (a) to compare the field measured pore water pressures at different depths with results of hydrological models, in order to evaluate the efficiency of the tested models and to determine how precipitations affect pore pressure development; (b) to compare the time trends of the safety factor that have been obtained by applying different stability models; (c) to evaluate, through a sensitivity analysis, the effects of soil hydrological properties on modeling pore water pressure and safety factor. The test site slope where field measurements were acquired is representative of other sites in Northern Apennines affected by shallow landslides and is characterized by medium

Forecast model of landslides in a short time

International Nuclear Information System (INIS)

Sanchez Lopez, Reinaldo

2006-01-01

The IDEAM in development of their functions as member of the national technical committee for the prevention and disasters attention (SNPAD) accomplishes the follow-up, monitoring and forecast in real time of the environmental dynamics that in extreme situations constitute threats and natural risks. One of the frequent dynamics and of greater impact is related to landslides, those that affect persistently the life of the persons, the infrastructure, the socioeconomic activities and the balance of the environment. The landslide in Colombia and in the world are caused mainly by effects of the rain, due to that, IDEAM has come developing forecast model, as an instrument for risk management in a short time. This article presents aspects related to their structure, operation, temporary space resolution, products, results, achievements and projections of the model. Conceptually, the model is support by the principle of the dynamic temporary - space, of the processes that consolidate natural hazards, particularly in areas where the man has come building the risk. Structurally, the model is composed by two sub-models; the general susceptibility of the earthly model and the critical rain model as a denotative factor, that consolidate the hazard process. In real time, the model, works as a GIS, permitting the automatic zoning of the landslides hazard for issue public advisory warming to help makers decisions on the risk that cause frequently these events, in the country

Investigation of relationship between sediment yield and landslide in Iran

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Samad Shadfar

2012-07-01

Full Text Available Landslides have been made irreversible damage to urban areas and economic in Iran. In this research, at first, for Investigation of relationship between landslide and sediment yield was recognized some of effective factors on Landslide. These Factors were processed with use of ILWIS and Arc GIS software’s. Landslide hazard zonation was done using Density Area and Index Overlay methods in GIS and evaluated them using Quality Sum index. In after phase, were determined sediment yield in each of them. Finally, occurrence rate landslide investigated in sediment yield zones. The results indicated that, slope, lithology and distance from the hydrographic network have the greatest impact on landslides. Most of the landslides have occurred in the 15-40% slope class, units of conglomerate and marl, and within one km of drainage network. On the other hand, the relationship between landslide frequency and distance of the fault was not a linear relationship and Almost 60 %of landslides have occurred distance of one km of the faults. Evaluation using Quality Sum index showed that the density Area has a more logical answer and as Appropriate method will be introduced in the basin. Investigation of deposition potential in sub-basins showed that Javaherdeh sub basin with 92.74 deposition potential is the first priority. Nedasht and latmohalleh sub basins, each with a deposition potential of 20.08 are the next priorities. Relationship between landslide area and deposition potential were identified as 8/91% of the landslides in the area of low And about 79 percent of landslides are located in high and very high deposition potentials.

Potentiality of SENTINEL-1 for landslide detection: first results in the Molise Region (Italy)

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Barra, Anna; Monserrat, Oriol; Mazzanti, Paolo; Esposito, Carlo; Crosetto, Michele; Scarascia Mugnozza, Gabriele

2016-04-01

A detailed inventory map, including information on landslide activity, is one of the most important input to landslide susceptibility and hazard analyses. The contribution of satellite SAR Interferometry in landslide risk mitigation is well-known within the scientific community. In fact, many encouraging results have been obtained, principally, in areas characterized by high coherence of the images (e.g. due to rock lithology or urban environment setting). In terms of coherence, the expected increased capabilities of Sentinel-1 for landslide mapping and monitoring are connected to both wavelength (55.5 mm) and short temporal baseline (12 days). The latter one is expected to be a key feature for increasing coherence and for defining monitoring and updating plans. With the aim of assessing these potentialities, we processed a set of 14 Sentinel-1 SLC images, acquired during a temporal span of 7 months, over the Molise region (Southern Italy), a critical area geologically susceptible to landslides. Even though Molise is mostly covered by crops and forested areas (63% and 35% respectively), that means a non-optimal coherence condition for SAR interferometry, promising results have been obtained. This has been achieved by integrating differential interferometric SAR techniques (12-days interferograms and time series) with GIS multilayer analysis (optical, geological, geomorphological, etc.). Specifically, analyzing a single burst of a Sentinel-1 frame (approximately 1875 km2), 62 landslides have been detected, thus allowing to improve the pre-existing inventory maps both in terms of landslide boundaries and state of activity. The results of our ongoing research show that Sentinel-1 can give a significant improvement in terms of exploitation of SAR data for landslide mapping and monitoring. As a matter of fact, by analyzing longer periods, it is expected to achieve a better understanding of landslide behavior and its relationship with triggering factors. This will be key

Developing a scientific procedure for community based hazard mapping and risk mitigation

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Verrier, M.

2011-12-01

As an international exchange student from the Geological Sciences Department at San Diego State University (SDSU), I joined the KKN-PPM program at Universitas Gadjah Mada (UGM), Yogyakarta, Indonesia, in July 2011 for 12 days (July 4th to July 16th) of its two month duration (July 4th to August 25th). The KKN-PPM group I was attached was designated 154 and was focused in Plosorejo Village, Karanganyar, Kerjo, Central Java, Indonesia. The mission of KKN-PPM 154 was to survey Plosorejo village for existing landslides, to generate a simple hazard susceptibility map that can be understood by local villagers, and then to begin dissemination of that map into the community. To generate our susceptibility map we first conducted a geological survey of the existing landslides in the field study area, with a focus on determining landslide triggers and gauging areas for susceptibility for future landslides. The methods for gauging susceptibility included lithological observation, the presence of linear cracking, visible loss of structural integrity in structures such as villager homes, as well as collaboration with local residents and with the local rescue and response team. There were three color distinctions used in representing susceptibility which were green, where there is no immediate danger of landslide damage; orange, where transportation routes are at risk of being disrupted by landslides; and red, where imminent landslide potential puts a home in direct danger. The landslide inventory and susceptibility data was compiled into digital mediums such as CorelDraw, ArcGIS and Google Earth. Once a technical map was generated, we presented it to the village leadership for confirmation and modification based on their experience. Finally, we began to use the technical susceptibility map to draft evacuation routes and meeting points in the event of landslides, as well as simple susceptibility maps that can be understood and utilized by local villagers. Landslide mitigation

OVERVIEW OF MODERN RESEARCH OF LANDSLIDES ACCORDING TO AERIAL AND SATELLITE IMAGERY

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K. M. Lyapishev

2015-01-01

Full Text Available This article is an overview of researches of landslides using remote sensing methods such as aerial photography, satellite images, radar interferometry, and their combination with the use of GIS technology. Modern methods of investigation of landslides are very diverse. The authors propose different approaches to the identification, classification and monitoring of landslides. Data analysis techniques can help in creating more sophisticated approach to the analysis of landslides.

The Pliocene Horcón Formation, Central Chile: a case study of earthquake-induced landslide susceptibility

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Valdivia, D.; Elgueta, S.; Hodgkin, A.; Marquardt, C.; del Valle, F.; Yáñez Morroni, G.

2017-12-01

Stability slope analysis is typically focused on modeling using cohesion and friction angle parameters but in earthquake-induced landslides, susceptibility is correlated more to lithological and stratigraphic parameters. In sedimentary deposits whose cohesion and diagenesis are very low, the risk of landslides increases. The Horcón Formation, which crops out continuously along cliffs in Central Chile between 32.5° and 33°S, is a Miocene-Pliocene well preserved, horizontally stratified unit composed of marine strata which overlies Paleozoic-Mesozoic igneous basement. During the Quaternary, the sequence was tectonically uplifted 80 meters and covered by unconsolidated eolian deposits. Given that Seismotectonic and Barrier-Asperity models suggest the occurrence of a forthcoming megathrust earthquake in a segment which includes this area, the Horcón Formation constitutes a good case study to characterize the susceptibility of this type of sediment for mass movements triggered by earthquakes. Field mapping, stratigraphic and sedimentological studies, including petrographic analyses to determine lithological composition and paragenesis of diagenetic events, have been carried out along with limited gravimetric profiling and CPTU drill tests. High resolution digital elevation modeling has also been applied. This work has led to the recognition of a shallow marine lithofacies association composed of weakly lithified fossiliferous and bioturbated medium to fine grained litharenite, mudstone, and fine conglomerate. The low grade of diagenesis in the sedimentary deposits was in response to a short period of burial and a subsequent accelerated uplift evidenced along the coast of Chile during the Quaternary. We have generated a predictive model of landslide susceptibility for the Horcón Formation and for the overlying Quaternary eolian deposits incorporating variables such as composition and diagenesis of lithofacies, slope, structures, weathering and landcover. The model

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam

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Dieu Tien Bui

2015-04-01

Full Text Available The main objective of this study is to investigate potential application of an integrated evidential belief function (EBF-based fuzzy logic model for spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam. First, a landslide inventory map was constructed from various sources. Then the landslide inventory map was randomly partitioned as a ratio of 70/30 for training and validation of the models, respectively. Second, six landslide conditioning factors (slope angle, slope aspect, lithology, distance to faults, soil type, land use were prepared and fuzzy membership values for these factors classes were estimated using the EBF. Subsequently, fuzzy operators were used to generate landslide susceptibility maps. Finally, the susceptibility maps were validated and compared using the validation dataset. The results show that the lowest prediction capability is the fuzzy SUM (76.6%. The prediction capability is almost the same for the fuzzy PRODUCT and fuzzy GAMMA models (79.6%. Compared to the frequency-ratio based fuzzy logic models, the EBF-based fuzzy logic models showed better result in both the success rate and prediction rate. The results from this study may be useful for local planner in areas prone to landslides. The modelling approach can be applied for other areas.

The role of multicollinearity in landslide susceptibility assessment by means of Binary Logistic Regression: comparison between VIF and AIC stepwise selection

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Cama, Mariaelena; Cristi Nicu, Ionut; Conoscenti, Christian; Quénéhervé, Geraldine; Maerker, Michael

2016-04-01

Landslide susceptibility can be defined as the likelihood of a landslide occurring in a given area on the basis of local terrain conditions. In the last decades many research focused on its evaluation by means of stochastic approaches under the assumption that 'the past is the key to the future' which means that if a model is able to reproduce a known landslide spatial distribution, it will be able to predict the future locations of new (i.e. unknown) slope failures. Among the various stochastic approaches, Binary Logistic Regression (BLR) is one of the most used because it calculates the susceptibility in probabilistic terms and its results are easily interpretable from a geomorphological point of view. However, very often not much importance is given to multicollinearity assessment whose effect is that the coefficient estimates are unstable, with opposite sign and therefore difficult to interpret. Therefore, it should be evaluated every time in order to make a model whose results are geomorphologically correct. In this study the effects of multicollinearity in the predictive performance and robustness of landslide susceptibility models are analyzed. In particular, the multicollinearity is estimated by means of Variation Inflation Index (VIF) which is also used as selection criterion for the independent variables (VIF Stepwise Selection) and compared to the more commonly used AIC Stepwise Selection. The robustness of the results is evaluated through 100 replicates of the dataset. The study area selected to perform this analysis is the Moldavian Plateau where landslides are among the most frequent geomorphological processes. This area has an increasing trend of urbanization and a very high potential regarding the cultural heritage, being the place of discovery of the largest settlement belonging to the Cucuteni Culture from Eastern Europe (that led to the development of the great complex Cucuteni-Tripyllia). Therefore, identifying the areas susceptible to

Landslide Susceptibility Assessment in the Central Part of Republic of Moldova

Science.gov (United States)

Ercanoglu, Murat; Boboc, Nicolae; Sirodoev, Igor; Ahmet Temiz, F.; Sirodoev, Ghenadi

2010-05-01

factors such as unreasoned road and civil construction, agricultural activity, failure of water pipe systems. Fragmentation of the agricultural lands and appearance of hundreds of thousands of small farmers have destroyed previous land protection system as well as landslide control system. Lack of financial resources played the main role in failing of these systems. In order to help the decision makers and to prevent human life, we initiated a collaboration as being the two landslide suffering countries, Turkey and Moldova, under the Science For Peace Project supported by NATO. It is believed that integration of the two teams' experience and knowledge under landslide topic will provide useful and beneficial information and economic benefits for the future works such as urban development and planning, engineering applications, land-use potential planning etc. in Moldova, based on the ideas of application of scientific principles to reach a better and peaceful world. Research results will be helpful in developing new regulations on territory protection, existing and currently designed buildings, infrastructures, and facilities as well as land management in the Republic of Moldova.

Object-based Landslide Mapping: Examples, Challenges and Opportunities

Science.gov (United States)

Hölbling, Daniel; Eisank, Clemens; Friedl, Barbara; Chang, Kang-Tsung; Tsai, Tsai-Tsung; Birkefeldt Møller Pedersen, Gro; Betts, Harley; Cigna, Francesca; Chiang, Shou-Hao; Aubrey Robson, Benjamin; Bianchini, Silvia; Füreder, Petra; Albrecht, Florian; Spiekermann, Raphael; Weinke, Elisabeth; Blaschke, Thomas; Phillips, Chris

2016-04-01

Over the last decade, object-based image analysis (OBIA) has been increasingly used for mapping landslides that occur after triggering events such as heavy rainfall. The increasing availability and quality of Earth Observation (EO) data in terms of temporal, spatial and spectral resolution allows for comprehensive mapping of landslides at multiple scales. Most often very high resolution (VHR) or high resolution (HR) optical satellite images are used in combination with a digital elevation model (DEM) and its products such as slope and curvature. Semi-automated object-based mapping makes use of various characteristics of image objects that are derived through segmentation. OBIA enables numerous spectral, spatial, contextual and textural image object properties to be applied during an analysis. This is especially useful when mapping complex natural features such as landslides and constitutes an advantage over pixel-based image analysis. However, several drawbacks in the process of object-based landslide mapping have not been overcome yet. The developed classification routines are often rather complex and limited regarding their transferability across areas and sensors. There is still more research needed to further improve present approaches and to fully exploit the capabilities of OBIA for landslide mapping. In this study several examples of object-based landslide mapping from various geographical regions with different characteristics are presented. Examples from the Austrian and Italian Alps are shown, whereby one challenge lies in the detection of small-scale landslides on steep slopes while preventing the classification of false positives with similar spectral properties (construction areas, utilized land, etc.). Further examples feature landslides mapped in Iceland, where the differentiation of landslides from other landscape-altering processes in a highly dynamic volcanic landscape poses a very distinct challenge, and in Norway, which is exposed to multiple

Assessing internal biophysical vulnerability to landslide hazards - a nested catchment approach: Xiangxi Watershed / Three Gorges Reservoir

Science.gov (United States)

Wiegand, Matthias; Seeber, Christoph; Hartmann, Heike; Xiang, Wei; King, Lorenz

2010-05-01

. Dwellings and road infrastructure, chosen as high priorities, are captured based on various data like: high resolution satellite imagery, topographic information and field investigation. Currently demographic data is available only at administrative county level - therefore buildings will serve as spatial proxy for population density. Elements at risk will be classified into categories and susceptibility factors will be identified for sampled groups. The envisaged model defines the susceptibility of a certain element at risk not only by the element itself - it assumes that the specific susceptibility is also strongly influenced by the particular surroundings. The susceptibility of a certain building, as for instance, will be defined by the structure type and condition, and in addition or as proxy, specific site characteristics like: slope angle and aspect, soil type and erodibility, lithology, proximity to streams, proximity to the Three Gorges reservoir, depth to groundwater, land use change and dissect intensity, if feasible. Each factor with potential influence on susceptibility will go through a GIS based factor weighting procedure as part of the quantitative vulnerability model. Holistic, "cross scale integrated" vulnerability assessment models need to integrate environmental, social/ cultural and economic aspects. Therefore the proposed vulnerability assessment model must be seen as a starting point for a conceptual framework, and might serve as stimulus to local disaster- and resources management systems. Furthermore the GIS based model enables the opportunity to be linked and refined within the local spatial data infrastructure initiatives.

A landslide susceptibility assessment in urban areas based on existing data: an example from the Iguaná Valley, Medellín City, Colombia

Czech Academy of Sciences Publication Activity Database

Klimeš, Jan; Rios Escobar, V.

2010-01-01

Roč. 10, č. 10 (2010), s. 2067-2079 ISSN 1561-8633 R&D Projects: GA ČR GP205/09/P383 Institutional research plan: CEZ:AV0Z30460519 Keywords : landslide susceptibility assessment * Colombia * Medellín Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 1.792, year: 2010 http://www.nat-hazards-earth-syst-sci.net/10/2067/2010/nhess-10-2067-2010.html

Modeling landslide recurrence in Seattle, Washington, USA

Science.gov (United States)

Salciarini, Diana; Godt, Jonathan W.; Savage, William Z.; Baum, Rex L.; Conversini, Pietro

2008-01-01

To manage the hazard associated with shallow landslides, decision makers need an understanding of where and when landslides may occur. A variety of approaches have been used to estimate the hazard from shallow, rainfall-triggered landslides, such as empirical rainfall threshold methods or probabilistic methods based on historical records. The wide availability of Geographic Information Systems (GIS) and digital topographic data has led to the development of analytic methods for landslide hazard estimation that couple steady-state hydrological models with slope stability calculations. Because these methods typically neglect the transient effects of infiltration on slope stability, results cannot be linked with historical or forecasted rainfall sequences. Estimates of the frequency of conditions likely to cause landslides are critical for quantitative risk and hazard assessments. We present results to demonstrate how a transient infiltration model coupled with an infinite slope stability calculation may be used to assess shallow landslide frequency in the City of Seattle, Washington, USA. A module called CRF (Critical RainFall) for estimating deterministic rainfall thresholds has been integrated in the TRIGRS (Transient Rainfall Infiltration and Grid-based Slope-Stability) model that combines a transient, one-dimensional analytic solution for pore-pressure response to rainfall infiltration with an infinite slope stability calculation. Input data for the extended model include topographic slope, colluvial thickness, initial water-table depth, material properties, and rainfall durations. This approach is combined with a statistical treatment of rainfall using a GEV (General Extreme Value) probabilistic distribution to produce maps showing the shallow landslide recurrence induced, on a spatially distributed basis, as a function of rainfall duration and hillslope characteristics.

The impact of expert knowledge on natural hazard susceptibility assessment using spatial multi-criteria analysis

Science.gov (United States)

Karlsson, Caroline; Kalantari, Zahra; Mörtberg, Ulla; Olofsson, Bo; Lyon, Steve

2016-04-01

Road and railway networks are one of the key factors to a country's economic growth. Inadequate infrastructural networks could be detrimental to a society if the transport between locations are hindered or delayed. Logistical hindrances can often be avoided whereas natural hindrances are more difficult to control. One natural hindrance that can have a severe adverse effect on both infrastructure and society is flooding. Intense and heavy rainfall events can trigger other natural hazards such as landslides and debris flow. Disruptions caused by landslides are similar to that of floods and increase the maintenance cost considerably. The effect on society by natural disasters is likely to increase due to a changed climate with increasing precipitation. Therefore, there is a need for risk prevention and mitigation of natural hazards. Determining susceptible areas and incorporating them in the decision process may reduce the infrastructural harm. Spatial multi-criteria analysis (SMCA) is a part of decision analysis, which provides a set of procedures for analysing complex decision problems through a Geographic Information System (GIS). The objective and aim of this study was to evaluate the usefulness of expert judgements for inundation, landslide and debris flow susceptibility assessments through a SMCA approach using hydrological, geological and land use factors. The sensitivity of the SMCA model was tested in relation to each perspective and impact on the resulting susceptibility. A least cost path function was used to compare new alternative road lines with the existing ones. This comparison was undertaken to identify the resulting differences in the susceptibility assessments using expert judgements as well as historic incidences of flooding and landslides in order to discuss the usefulness of the model in road planning.

LOCAL SITE CONDITIONS INFLUENCING EARTHQUAKE INTENSITIES AND SECONDARY COLLATERAL IMPACTS IN THE SEA OF MARMARA REGION - Application of Standardized Remote Sensing and GIS-Methods in Detecting Potentially Vulnerable Areas to Earthquakes, Tsunamis and Other Hazards.

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George Pararas-Carayannis

2011-01-01

Full Text Available The destructive earthquake that struck near the Gulf of Izmit along the North Anatolian fault in Northwest Turkey on August 17, 1999, not only generated a local tsunami that was destructive at Golcuk and other coastal cities in the eastern portion of the enclosed Sea of Marmara, but was also responsible for extensive damage from collateral hazards such as subsidence, landslides, ground liquefaction, soil amplifications, compaction and underwater slumping of unconsolidated sediments. This disaster brought attention in the need to identify in this highly populated region, local conditions that enhance earthquake intensities, tsunami run-up and other collateral disaster impacts. The focus of the present study is to illustrate briefly how standardized remote sensing techniques and GIS-methods can help detect areas that are potentially vulnerable, so that disaster mitigation strategies can be implemented more effectively. Apparently, local site conditions exacerbate earthquake intensities and collateral disaster destruction in the Marmara Sea region. However, using remote sensing data, the causal factors can be determined systematically. With proper evaluation of satellite imageries and digital topographic data, specific geomorphologic/topographic settings that enhance disaster impacts can be identified. With a systematic GIS approach - based on Digital Elevation Model (DEM data - geomorphometric parameters that influence the local site conditions can be determined. Digital elevation data, such as SRTM (Shuttle Radar Topography Mission, with 90m spatial resolution and ASTER-data with 30m resolution, interpolated up to 15 m is readily available. Areas with the steepest slopes can be identified from slope gradient maps. Areas with highest curvatures susceptible to landslides can be identified from curvature maps. Coastal areas below the 10 m elevation susceptible to tsunami inundation can be clearly delineated. Height level maps can also help locate

Sistemi WebGIS e Inventario IFFI per la Prevenzione nel Rischio Frane

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Italo Di Giovanni

2013-02-01

Full Text Available Frane ed alluvioni sono molto frequenti sul territorio italiano: ciò è dovuto soprattutto alle caratteristiche geologico geomorfologiche (75% del territorio italiano è montano-collinare e quelle climatiche, che determinano un alto rischio idrogeologico dell’Italia con conseguente impatto a livello socio-economico, sia per il numero di vittime sia per i danni prodotti ad abitazioni, industrie, infrastrutture, beni culturali e ambientali, ed agricoltura.WebGIS system and IFFI Inventory for Prevention in Landslide RiskGeological, geomorphological and climatic characteristics determine a high landslide risk in Italy, with a consequent impacton the socio-economic level. As part of the IFFI Project, a WebGIS archive was created toprovide a framework of the distribution of landslides. Final result: nearby 485.000 landslides have been recorded in Italy. Moreover in Italy the remediation of the damages, due to landslides and floods, cost more than secure the lands. Today, in the framework of prevention, landslide monitoring cannot be ignored by the authorities allowing control conditions that can become extremely dangerous, with damages to property and/or people.

Investigating Earthquake-induced Landslides­a Historical Review

Science.gov (United States)

Keefer, D. K.; Geological Survey, Us; Park, Menlo; Usa, Ca

, extensive to relatively complete inventories landslides have been prepared for a relatively small number of earthquakes. Through the 1960's and 1970's the best landslide inventories typically were complete only for a central affected area, although the first virtually complete inventory of a large earthquake was prepared for the M 7.6 Guatemala earthquake in 1976. Beginning in 1980, virtu- ally complete landslide inventories have prepared for several additional earthquakes in California, El Salvador, Japan, Italy, and Taiwan. Most of these used aerial pho- tography in combination with ground field studies, although the studies of the most recent of these events, in Taiwan, have also used satellite imagery, and three of the others (including the two smallest) were compiled largely from ground-based field 1 studies without aerial photography. Since 1989, digital mapping and GIS techniques have come into common use for mapping earthquake-induced landslides, and the use of these techniques has greatly enhanced the level of analysis that can be applied to earthquake-induced landslide occurrence. The first synthesis of data on earthquake- induced landslides, completed in 1984, defined the general characteristics of these landslides, derived relations between landslide occurrence on the one hand and geo- logic and seismic parameters on the other hand, and identified the types of hazards as- sociated with them. Since then, additional synthesis of worldwide data (1999) and na- tional data from New Zealand (1997), Greece (2000), and Italy (2000) have provided additional data on landslide characteristics and hazards and have extended, revised, and refined these relations. Recently completed studies have also identified areas with anomalous landslide distributions, have provided data for correlating the occurrence of landslides with a measure of local ground motion, have verified the occasional delayed triggering of landslides as a consequence of seismic shaking, and have identi- fied

Combining heuristic and statistical techniques in landslide hazard assessments

Science.gov (United States)

Cepeda, Jose; Schwendtner, Barbara; Quan, Byron; Nadim, Farrokh; Diaz, Manuel; Molina, Giovanni

2014-05-01

As a contribution to the Global Assessment Report 2013 - GAR2013, coordinated by the United Nations International Strategy for Disaster Reduction - UNISDR, a drill-down exercise for landslide hazard assessment was carried out by entering the results of both heuristic and statistical techniques into a new but simple combination rule. The data available for this evaluation included landslide inventories, both historical and event-based. In addition to the application of a heuristic method used in the previous editions of GAR, the availability of inventories motivated the use of statistical methods. The heuristic technique is largely based on the Mora & Vahrson method, which estimates hazard as the product of susceptibility and triggering factors, where classes are weighted based on expert judgment and experience. Two statistical methods were also applied: the landslide index method, which estimates weights of the classes for the susceptibility and triggering factors based on the evidence provided by the density of landslides in each class of the factors; and the weights of evidence method, which extends the previous technique to include both positive and negative evidence of landslide occurrence in the estimation of weights for the classes. One key aspect during the hazard evaluation was the decision on the methodology to be chosen for the final assessment. Instead of opting for a single methodology, it was decided to combine the results of the three implemented techniques using a combination rule based on a normalization of the results of each method. The hazard evaluation was performed for both earthquake- and rainfall-induced landslides. The country chosen for the drill-down exercise was El Salvador. The results indicate that highest hazard levels are concentrated along the central volcanic chain and at the centre of the northern mountains.

Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria

Science.gov (United States)

Thomas, Matthew A.; Mirus, Benjamin B.; Collins, Brian D.; Lu, Ning; Godt, Jonathan W.

2018-01-01

Rainfall-induced shallow landsliding is a persistent hazard to human life and property. Despite the observed connection between infiltration through the unsaturated zone and shallow landslide initiation, there is considerable uncertainty in how estimates of unsaturated soil-water retention properties affect slope stability assessment. This source of uncertainty is critical to evaluating the utility of physics-based hydrologic modeling as a tool for landslide early warning. We employ a numerical model of variably saturated groundwater flow parameterized with an ensemble of texture-, laboratory-, and field-based estimates of soil-water retention properties for an extensively monitored landslide-prone site in the San Francisco Bay Area, CA, USA. Simulations of soil-water content, pore-water pressure, and the resultant factor of safety show considerable variability across and within these different parameter estimation techniques. In particular, we demonstrate that with the same permeability structure imposed across all simulations, the variability in soil-water retention properties strongly influences predictions of positive pore-water pressure coincident with widespread shallow landsliding. We also find that the ensemble of soil-water retention properties imposes an order-of-magnitude and nearly two-fold variability in seasonal and event-scale landslide susceptibility, respectively. Despite the reduced factor of safety uncertainty during wet conditions, parameters that control the dry end of the soil-water retention function markedly impact the ability of a hydrologic model to capture soil-water content dynamics observed in the field. These results suggest that variability in soil-water retention properties should be considered for objective physics-based simulation of landslide early warning criteria.

GIS and RS-based modelling of potential natural hazard areas in Pehchevo municipality, Republic of Macedonia

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Milevski Ivica

2013-01-01

Full Text Available In this paper, one approach of Geographic Information System (GIS and Remote Sensing (RS assessment of potential natural hazard areas (excess erosion, landslides, flash floods and fires is presented. For that purpose Pehchevo Municipality in the easternmost part of the Republic of Macedonia is selected as a case study area because of high local impact of natural hazards on the environment, social-demographic situation and local economy. First of all, most relevant static factors for each type of natural hazard are selected (topography, land cover, anthropogenic objects and infrastructure. With GIS and satellite imagery, multi-layer calculation is performed based on available traditional equations, clustering or discreditation procedures. In such way suitable relatively “static” natural hazard maps (models are produced. Then, dynamic (mostly climate related factors are included in previous models resulting in appropriate scenarios correlated with different amounts of precipitation, temperature, wind direction etc. Finally, GIS based scenarios are evaluated and tested with field check or very fine resolution Google Earth imagery showing good accuracy. Further development of such GIS models in connection with automatic remote meteorological stations and dynamic satellite imagery (like MODIS will provide on-time warning for coming natural hazard avoiding potential damages or even causalities.

From Physical Process to Economic Cost - Integrated Approaches of Landslide Risk Assessment

Science.gov (United States)

Klose, M.; Damm, B.

2014-12-01

The nature of landslides is complex in many respects, with landslide hazard and impact being dependent on a variety of factors. This obviously requires an integrated assessment for fundamental understanding of landslide risk. Integrated risk assessment, according to the approach presented in this contribution, implies combining prediction of future landslide occurrence with analysis of landslide impact in the past. A critical step for assessing landslide risk in integrated perspective is to analyze what types of landslide damage affected people and property in which way and how people contributed and responded to these damage types. In integrated risk assessment, the focus is on systematic identification and monetization of landslide damage, and analytical tools that allow deriving economic costs from physical landslide processes are at the heart of this approach. The broad spectrum of landslide types and process mechanisms as well as nonlinearity between landslide magnitude, damage intensity, and direct costs are some main factors explaining recent challenges in risk assessment. The two prevailing approaches for assessing the impact of landslides in economic terms are cost survey (ex-post) and risk analysis (ex-ante). Both approaches are able to complement each other, but yet a combination of them has not been realized so far. It is common practice today to derive landslide risk without considering landslide process-based cause-effect relationships, since integrated concepts or new modeling tools expanding conventional methods are still widely missing. The approach introduced in this contribution is based on a systematic framework that combines cost survey and GIS-based tools for hazard or cost modeling with methods to assess interactions between land use practices and landslides in historical perspective. Fundamental understanding of landslide risk also requires knowledge about the economic and fiscal relevance of landslide losses, wherefore analysis of their

Integrating spatial, temporal, and size probabilities for the annual landslide hazard maps in the Shihmen watershed, Taiwan

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C. Y. Wu

2013-09-01

Full Text Available Landslide spatial, temporal, and size probabilities were used to perform a landslide hazard assessment in this study. Eleven intrinsic geomorphological, and two extrinsic rainfall factors were evaluated as landslide susceptibility related factors as they related to the success rate curves, landslide ratio plots, frequency distributions of landslide and non-landslide groups, as well as probability–probability plots. Data on landslides caused by Typhoon Aere in the Shihmen watershed were selected to train the susceptibility model. The landslide area probability, based on the power law relationship between the landslide area and a noncumulative number, was analyzed using the Pearson type 5 probability density function. The exceedance probabilities of rainfall with various recurrence intervals, including 2, 5, 10, 20, 50, 100 and 200 yr, were used to determine the temporal probabilities of the events. The study was conducted in the Shihmen watershed, which has an area of 760 km2 and is one of the main water sources for northern Taiwan. The validation result of Typhoon Krosa demonstrated that this landslide hazard model could be used to predict the landslide probabilities. The results suggested that integration of spatial, area, and exceedance probabilities to estimate the annual probability of each slope unit is feasible. The advantage of this annual landslide probability model lies in its ability to estimate the annual landslide risk, instead of a scenario-based risk.

QVAST: a new Quantum GIS plugin for estimating volcanic susceptibility

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Bartolini, S.; Cappello, A.; Martí, J.; Del Negro, C.

2013-11-01

One of the most important tasks of modern volcanology is the construction of hazard maps simulating different eruptive scenarios that can be used in risk-based decision making in land-use planning and emergency management. The first step in the quantitative assessment of volcanic hazards is the development of susceptibility maps (i.e., the spatial probability of a future vent opening given the past eruptive activity of a volcano). This challenging issue is generally tackled using probabilistic methods that use the calculation of a kernel function at each data location to estimate probability density functions (PDFs). The smoothness and the modeling ability of the kernel function are controlled by the smoothing parameter, also known as the bandwidth. Here we present a new tool, QVAST, part of the open-source geographic information system Quantum GIS, which is designed to create user-friendly quantitative assessments of volcanic susceptibility. QVAST allows the selection of an appropriate method for evaluating the bandwidth for the kernel function on the basis of the input parameters and the shapefile geometry, and can also evaluate the PDF with the Gaussian kernel. When different input data sets are available for the area, the total susceptibility map is obtained by assigning different weights to each of the PDFs, which are then combined via a weighted summation and modeled in a non-homogeneous Poisson process. The potential of QVAST, developed in a free and user-friendly environment, is here shown through its application in the volcanic fields of Lanzarote (Canary Islands) and La Garrotxa (NE Spain).

Landslide potential zonation in Baleghlu watershed (NW Iran) using AHP Fuzzy method

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Jananeh, Keristineh; Roostai, Shahram

2017-04-01

Landslides and slope instabilities are among the important natural hazards, which cause human and financial casualties and loss of economic resources every year. These hazards mostly occur in natural slopes or those manipulated by human. Zonation of areas with regard to landslide potential is one of the means to identify areas prone to produce landslide and so, to conduct plannings and management based on the prepared zonation maps in order to reduce the casualties. This contribution investigates on the landslide potential zonation within the Baleghlu watershed. This watershed is located in the southeast of Sabalan volcano (NW Iran) within the longitudes of 47° 48` and 48° 12` E and northern latitudes of 37° 51` and 38° 16` N. Its main river is Baleghlu, which is later connected to the Arax river through the Qarasu and Dareh Roud rivers, and is finally terminated to the Caspian sea. The method of investigation is Fuzzy AHP in the GIS environment. First, the main factors including the slope and its direction, geology, soil, climate, distance from the road and river and land usage were investigated and then, after preparing data layers based on the above-mentioned parameters and giving weights to them in the GIS environment, the landslide potential map was prepared by Fuzzy AHP method. It was revealed that the slope factor with the value of 0.3882 has the highest weight, while the land usage factor with the value of 0.0287 has the lowest weight. According to the final zonation map of the landslide potential, the watershed was divided into 5 classes, ranging from very high potential class to the very low potential. The obtained results showed that the largest part of the watershed (32.21%) has low landslide potential, while about 13.5% of it has very high potential. Areas with very high and high landslide potential (327.39 km2 area) are mainly located in the northwest of the watershed, with some small areas distributed in the south and east, while areas with very

Morphometric analysis of landslide in the Mountain Region of the State of Rio de Janeiro in Brazi: the case study of D'anta's watershed

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Carvalho Araújo, João Paulo; da Silva, Lúcia Maria; Avear, Marcello; Dourado, Francisco; Ferreira Fernandes, Nelson

2013-04-01

Mass movements are recurrent phenomena in the whole Mountain Region of the State of Rio de Janeiro in Brazil. These events actively participate in the relief evolution and are also responsible for many damages and loss of human lives. The triggering of these events depends on the natural environment and the preparatory and immediate action of the physical, biotic and human agents responsible for these processes. This work is based on the hypothesis in which the topographical conditions have a major effect on the spatial distribution of translational landslides caused by decreased of the internal resistance of the material mobilized. Therefore, the purpose of this study is to identify the topographical conditions favorable to landslide triggering based on morphometric analysis in a pilot watershed - D'antás watershed - located in the mountainous region of the State of Rio de Janeiro. The indices include the topographic wetness index (TWI), contributing area, slope angle and elevation and were derived from 5-m grid digital terrain model, computed on a Geographic Information System (GIS). The maps produced allowed the analysis of topographic influence on the landslides distribution from the indices of frequency classes (F), concentration of scars (CC) and potential of landslide (PL). The landscape sectors that are more likely to be affected by landslides were the ones where the elevation ranges from 1070m - 1187m, slope angle between 40.95° and 47.77°, contributing area between (log10) 1.32 m² - 1.95 m² and topographic wetness index between 7.11 to 9.59. This work provides important information which may help in the decision-making process, using fewer data and indices of easy application. Finally, the results obtained will subsidize of a landslide susceptibility map through the implementation of the conditional probability method aimed at predicting and mitigating of the damage caused by landslides.

Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province

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Brenning, A.; Schwinn, M.; Ruiz-Páez, A. P.; Muenchow, J.

2015-01-01

Mountain roads in developing countries are known to increase landslide occurrence due to often inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading. This study empirically investigates landslide initiation frequency along two paved interurban highways in the tropical Andes of southern Ecuador across different climatic regimes. Generalized additive models (GAM) and generalized linear models (GLM) were used to analyze the relationship between mapped landslide initiation points and distance to highway while accounting for topographic, climatic, and geological predictors as possible confounders. A spatial block bootstrap was used to obtain nonparametric confidence intervals for the odds ratio of landslide occurrence near the highways (25 m distance) compared to a 200 m distance. The estimated odds ratio was 18-21, with lower 95% confidence bounds >13 in all analyses. Spatial bootstrap estimation using the GAM supports the higher odds ratio estimate of 21.2 (95% confidence interval: 15.5-25.3). The highway-related effects were observed to fade at about 150 m distance. Road effects appear to be enhanced in geological units characterized by Holocene gravels and Laramide andesite/basalt. Overall, landslide susceptibility was found to be more than 1 order of magnitude higher in close proximity to paved interurban highways in the Andes of southern Ecuador.

Landslide susceptibility near highways is increased by one order of magnitude in the Andes of southern Ecuador, Loja province

Science.gov (United States)

Brenning, A.; Schwinn, M.; Ruiz-Páez, A. P.; Muenchow, J.

2014-03-01

Mountain roads in developing countries are known to increase landslide occurrence due to often inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading. This study empirically investigates landslide initiation frequency along two paved interurban highways in the tropical Andes of southern Ecuador across different climatic regimes. Generalized additive models (GAM) and generalized linear models (GLM) were used to analyze the relationship between mapped landslide initiation points and distance to highway while accounting for topographic, climatic and geological predictors as possible confounders. A spatial block bootstrap was used to obtain non-parametric confidence intervals for the odds ratio of landslide occurrence near the highways (25 m distance) compared to a 200 m distance. The estimated odds ratio was 18-21 with lower 95% confidence bounds > 13 in all analyses. Spatial bootstrap estimation using the GAM supports the higher odds ratio estimate of 21.2 (95% confidence interval: 15.5-25.3). The highway-related effects were observed to fade at about 150 m distance. Road effects appear to be enhanced in geological units characterized by Holocene gravels and Laramide andesite/basalt. Overall, landslide susceptibility was found to be more than one order of magnitude higher in close proximity to paved interurban highways in the Andes of southern Ecuador.

Spatio-Temporal Distribution of Landslides in Java and the Triggering Factors

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Danang Sri Hadmoko

2017-07-01

Full Text Available Java Island, the most populated island of Indonesia, is prone to landslide disasters. Their occurrence and impact have increased mainly as the result of natural factors, aggravated by human imprint. This paper is intended to analyse: (1 the spatio-temporal variation of landslides in Java during short term and long-term periods, and (2 their causative factors such as rainfall, topography, geology, earthquakes, and land-use. The evaluation spatially and temporally of historical landslides and consequences were based on the landslide database covering the period of 1981 – 2007 in the GIS environment. Database showed that landslides distributed unevenly between West Java (67 %, Central Java (29 % and East Java (4 %. Slope failures were most abundant on the very intensively weathered zone of old volcanic materials on slope angles of 30O – 40O. Rainfall threshold analysis showed that shallow landslides and deep-seated landslides were triggered by rainfall events of 300 – 600 mm and > 600 mm respectively of antecedent rainfall during 30 consecutive days, and many cases showed that the landslides were not always initiated by intense rainfall during the landslide day. Human interference plays an important role in landslide occurrence through land conversion from natural forest to dryland agriculture which was the host of most of landslides in Java. These results and methods can be used as valuable information on the spatio-temporal characteristics of landslides in Java and their relationship with causative factors, thereby providing a sound basis for landslide investigation in more detail.

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

KAUST Repository

Lombardo, Luigi

2018-02-13

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this latent effect provides valuable imaging support

Landslides Are Common In The Amazon Rainforests Of SE Peru

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Khanal, S. P.; Muttiah, R. S.; Janovec, J. P.

2005-12-01

The recent landslides in La Conchita, California, Mumbai, India, Ratnapura, Sri Lanka and Sugozu village, Turkey have dramatically illustrated prolonged rainfall on water induced change in soil shear stress. In these examples, the human footprint may have also erased or altered the natural river drainage from small to large scales. By studying patterns of landslides in natural ecosystems, government officials, policy makers, engineers, geologists and others may be better informed about likely success of prevention or amelioration programs in risk prone areas. Our study area in the Los Amigos basin in Amazon rainforests of Southeastern Peru, has recorded several hundred landslides. The area has no large human settlements. The basin is characterized by heavy rainfall, dense vegetation, river meander and uniform soils. Our objectives were: 1). Determine the spatial pattern of landslides using GIS and Remotely sensed data, 2). Model the statistical relationship between environmental variables and, 3). Evaluate influence of drainage on landscape and soil loss. GIS layers consisted of: 50cm aerial imagery, DEMs, digitized streams, soils, geology, rainfall from the TRMM satellite, and vegetation cover from the LANDSAT and MODIS sensors.

Application of a weighted spatial probability model in GIS to analyse landslides in Penang Island, Malaysia

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Samy Ismail Elmahdy

2016-01-01

Full Text Available In the current study, Penang Island, which is one of the several mountainous areas in Malaysia that is often subjected to landslide hazard, was chosen for further investigation. A multi-criteria Evaluation and the spatial probability weighted approach and model builder was applied to map and analyse landslides in Penang Island. A set of automated algorithms was used to construct new essential geological and morphometric thematic maps from remote sensing data. The maps were ranked using the weighted probability spatial model based on their contribution to the landslide hazard. Results obtained showed that sites at an elevation of 100–300 m, with steep slopes of 10°–37° and slope direction (aspect in the E and SE directions were areas of very high and high probability for the landslide occurrence; the total areas were 21.393 km2 (11.84% and 58.690 km2 (32.48%, respectively. The obtained map was verified by comparing variogram models of the mapped and the occurred landslide locations and showed a strong correlation with the locations of occurred landslides, indicating that the proposed method can successfully predict the unpredictable landslide hazard. The method is time and cost effective and can be used as a reference for geological and geotechnical engineers.

Object-based landslide detection in different geographic regions

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Friedl, Barbara; Hölbling, Daniel; Eisank, Clemens; Blaschke, Thomas

2015-04-01

Landslides occur in almost all mountainous regions of the world and rank among the most severe natural hazards. In the last decade - according to the world disaster report 2014 published by the International Federation of Red Cross and Red Crescent Societies (IRFC) - more than 9.000 people were killed by mass movements, more than 3.2 million people were affected and the total amount of disaster estimated damage accounts to more than 1.700 million US dollars. The application of remote sensing data for mapping landslides can contribute to post-disaster reconstruction or hazard mitigation, either by providing rapid information about the spatial distribution and location of landslides in the aftermath of triggering events or by creating and updating landslide inventories. This is especially valid for remote and inaccessible areas, where information on landslides is often lacking. However, reliable methods are needed for extracting timely and relevant information about landslides from remote sensing data. In recent years, novel methods such as object-based image analysis (OBIA) have been successfully employed for semi-automated landslide mapping. Several studies revealed that OBIA frequently outperforms pixel-based approaches, as a range of image object properties (spectral, spatial, morphometric, contextual) can be exploited during the analysis. However, object-based methods are often tailored to specific study areas, and thus, the transferability to regions with different geological settings, is often limited. The present case study evaluates the transferability and applicability of an OBIA approach for landslide detection in two distinct regions, i.e. the island of Taiwan and Austria. In Taiwan, sub-areas in the Baichi catchment in the North and in the Huaguoshan catchment in the southern-central part of the island are selected; in Austria, landslide-affected sites in the Upper Salzach catchment in the federal state of Salzburg are investigated. For both regions

Earthquake induced landslide hazard: a multidisciplinary field observatory in the Marmara SUPERSITE

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Bigarré, Pascal

2014-05-01

, that shows an important slump mass facing the Istanbul coastline. A multidisciplinary research program based on pre-existing studies has been designed with objectives and tasks linked to constrain and tackle progressively some challenging issues related to data integration, modeling, monitoring and mapping technologies. Concerning the on-shore area, this program includes the refined analysis of the seismic site response, the permanent multi-parameter ground monitoring of a representative unstable slope as well as the in-depth slope stability analysis based on the stress-strain dynamic numerical modelling approach. Hyperspectral and Dinsar imagery technologies are also deployed to complete inventory and observational information. The development of a dynamic GIS tool featuring capabilities to integrate and process very different types of data, and up-date susceptibility maps based on near to real-time rainfall-seismic shaking input, is currently undertaken. Moreover, the research is gaining high profit of a vast drilling program undertaken by the Istanbul Metropolitan Area, aiming to yield a detailed geological and geotechnical characterization of the slopes. Also included in the objectives is to test a landslide early warning system. As regards the selected off-shore area, high resolution geophysical marine surveys are being conducted to complete its geomorphological description to help in mapping possible incipient mass movements. This is especially expected to provide better-constrained input for both laboratory testing and numerical modeling of tsunami scenarios thank to a unique lab-scale tsunami channel.

Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

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Oliveira, Sérgio C.; Zêzere, José L.; Lajas, Sara; Melo, Raquel

2017-07-01

Approaches used to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. The former are based on the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the counteracting forces that are resistant to motion. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodologically distinct, the statistical and deterministic methods generate similar shallow slide susceptibility results regarding the model's predictive capacity and spatial agreement; and (ii) the combination of shallow slide susceptibility maps obtained with statistical and physically based methods, for the same study area, generate a more reliable susceptibility model for shallow slide occurrence. These hypotheses were tested at a small test site (13.9 km2) located north of Lisbon (Portugal), using a statistical method (the information value method, IV) and a physically based method (the infinite slope method, IS). The landslide susceptibility maps produced with the statistical and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slide susceptibility map, which combines the independent results obtained with statistical and physically based models. Moreover, the combination of the two models allowed the identification of areas where the results of the information value and the infinite slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.

Landslides in Nicaragua - Mapping, Inventory, Hazard Assessment, Vulnerability Reduction, and Forecasting Attempts

Science.gov (United States)

Dévoli, G.; Strauch, W.; Álvarez, A.; Muñoz, A.; Kjekstad, O.

2009-04-01

access, manage, update and distribute in a short time to all sectors and users; and finally, the need of a comprehensive understanding of landslide processes. Many efforts have been made in the last 10 years to gain a more comprehensive and predictive understanding of landslide processes in Nicaragua. Since 1998, landslide inventory GIS based maps have been produced in different areas of the country, as part of international and multidisciplinary development projects. Landslide susceptibility and hazard maps are available now at INETEŔs Website for all municipalities of the country. The insights on landslide hazard have been transmitted to governmental agencies, local authorities, NGÓs, international agencies to be used in measures for risk reduction. A massive application example was the integration of hazard assessment studies in a large house building program in Nicaragua. Hazards of landslides, and other dangerous phenomena, were evaluated in more than 90 house building projects, each with 50 - 200 houses to be build, sited mainly in rural areas of the country. For more than 7000 families, this program could finally assure that their new houses were build in safe areas. Attempts have been made to develop a strategy for early warning of landslides in Nicaragua. First approaches relied on precipitation gauges with satellite based telemetry which were installed in some Nicaraguan volcanoes where lahars occur frequently. The occurrence of lahars in certain gullies could be detected by seismic stations. A software system gave acoustic alarm at INETEŔs Monitoring Centre when certain trigger levels of the accumulated precipitation were reached. The monitoring and early warning for all areas under risk would have required many rain gauges. A new concept is tested which uses near real time precipitation estimates from NOAA meteorological satellite data. A software system sends out alarm messages if strong or long lasting rains are observed over certain landslide "hot spots

GIS based landslide hazard evaluation and zonation – A case from Jeldu District, Central Ethiopia

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Tilahun Hamza

2017-04-01

The results revealed that 12% (5.64 km2 of the study area falls under no hazard, 27% (12.69 km2 as low hazard, 32% (15.04 km2 as moderate hazard, 21% (9.87 km2 as high hazard and the rest 8% (3.76 km2 as very high hazard. The validation of LHZ map shows that, 92% of past landslides fall in high or very high hazard zones, while 6% fall in medium and only 2% in low landslide hazard zones. The validation of LHZ map thus, reasonably showed that the adopted methodology produced satisfactory results and the delineated hazard zones may practically be applied for the regional planning and development of infrastructures in the area.

Assessing the Agreement Between Eo-Based Semi-Automated Landslide Maps with Fuzzy Manual Landslide Delineation

Science.gov (United States)

Albrecht, F.; Hölbling, D.; Friedl, B.

2017-09-01

Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA) provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR) satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.

Major risk from rapid, large-volume landslides in Europe (EU Project RUNOUT)

Science.gov (United States)

Kilburn, Christopher R. J.; Pasuto, Alessandro

2003-08-01

Project RUNOUT has investigated methods for reducing the risk from large-volume landslides in Europe, especially those involving rapid rates of emplacement. Using field data from five test sites (Bad Goisern and Köfels in Austria, Tessina and Vajont in Italy, and the Barranco de Tirajana in Gran Canaria, Spain), the studies have developed (1) techniques for applying geomorphological investigations and optical remote sensing to map landslides and their evolution; (2) analytical, numerical, and cellular automata models for the emplacement of sturzstroms and debris flows; (3) a brittle-failure model for forecasting catastrophic slope failure; (4) new strategies for integrating large-area Global Positioning System (GPS) arrays with local geodetic monitoring networks; (5) methods for raising public awareness of landslide hazards; and (6) Geographic Information System (GIS)-based databases for the test areas. The results highlight the importance of multidisciplinary studies of landslide hazards, combining subjects as diverse as geology and geomorphology, remote sensing, geodesy, fluid dynamics, and social profiling. They have also identified key goals for an improved understanding of the physical processes that govern landslide collapse and runout, as well as for designing strategies for raising public awareness of landslide hazards and for implementing appropriate land management policies for reducing landslide risk.

Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria Region of central Italy

Science.gov (United States)

Salciarini, D.; Godt, J.W.; Savage, W.Z.; Conversini, P.; Baum, R.L.; Michael, J.A.

2006-01-01

We model the rainfall-induced initiation of shallow landslides over a broad region using a deterministic approach, the Transient Rainfall Infiltration and Grid-based Slope-stability (TRIGRS) model that couples an infinite-slope stability analysis with a one-dimensional analytical solution for transient pore pressure response to rainfall infiltration. This model permits the evaluation of regional shallow landslide susceptibility in a Geographic Information System framework, and we use it to analyze susceptibility to shallow landslides in an area in the eastern Umbria Region of central Italy. As shown on a landslide inventory map produced by the Italian National Research Council, the area has been affected in the past by shallow landslides, many of which have transformed into debris flows. Input data for the TRIGRS model include time-varying rainfall, topographic slope, colluvial thickness, initial water table depth, and material strength and hydraulic properties. Because of a paucity of input data, we focus on parametric analyses to calibrate and test the model and show the effect of variation in material properties and initial water table conditions on the distribution of simulated instability in the study area in response to realistic rainfall. Comparing the results with the shallow landslide inventory map, we find more than 80% agreement between predicted shallow landslide susceptibility and the inventory, despite the paucity of input data.

COMPARISON of FUZZY-BASED MODELS in LANDSLIDE HAZARD MAPPING

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N. Mijani

2017-09-01

Full Text Available Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR and Quality Sum (QS. The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzy-gamma method with a minor difference is in the second order.

Monitoring and Warning of Landslides Based On Rainfall

Science.gov (United States)

Yudhbir, Y.

Management issues of landslide hazards assume much greater significance in poorest segments of society living in landslide risk prone hilly areas in developing countries. Analysis of the temporal recurrence of landslides shows that disastrous events occur with a frequency higher than the social and economic capacity of these societies to recover from previous events. In the context of landslide hazard management in In- dian Himalayan states this problem assumes much greater significance. Majority of the population lives on hill slopes which experience repeated landsliding activity es- pecially during the summer monsoon rains. Considering the high cost of structural control measures and the lack of necessary spatial database in respect of Quaternary geology, detailed topography and geohydrology etc., there is an acute need to develop a monitoring and warning system which is economical, easy to operate and does not require high technological inputs. Since most of the landslides in these areas are triggered by high incidence of rain, it appears attractive to explore development of a monitoring and warning network based on critical rainfall intensity thresholds. Such an option for management of landslide hazards would also provide useful meteorological data required for assessment of wa- ter resources, soil loss due to erosion, agricultural practices and flood incidence. In this paper, available approaches to the prediction and warning of landslide based on rainfall data will be critically reviewed. Various criteria recommended in litera- ture for threshold rainfall values in rain induced ground movements/failures would be compared and these relationships will be contrasted with the limited data available for the Indian Himalayan landslides. A plan for a network of automatic rain gauges and a suitable warning system will be discussed.

Landslide inventory for the Little North Santiam River Basin, Oregon

Science.gov (United States)

Sobieszczyk, Steven

2010-01-01

This geodatabase is an inventory of existing landslides in the Little North Santiam River Basin, Oregon (2009). Each landslide feature shown has been classified according to a number of specific characteristics identified at the time recorded in the GIS database. The classification scheme was developed by the Oregon Department of Geology and Mineral Industries (Burns and Madin, 2009). Several significant landslide characteristics recorded in the database are portrayed with symbology on this map. The specific characteristics shown for each landslide are the activity of landsliding, landslide features, deep or shallow failure, type of landslide movement, and confidence of landslide interpretation. These landslide characteristics are determined primarily on the basis of geomorphic features, or landforms, observed for each landslide. This work was completed as part of the Master's thesis "Turbidity Monitoring and LiDAR Imagery Indicate Landslides are Primary Source of Suspended-Sediment Load in the Little North Santiam River Basin, Oregon, Winter 2009-2010" by Steven Sobieszczyk, Portland State University and U.S. Geological Survey. Data layers in this geodatabase include: landslide deposit boundaries (Deposits); field-verfied location imagery (Photos); head scarp or scarp flanks (Scarp_Flanks); and secondary scarp features (Scarps).The geodatabase template was developed by the Oregon Department of Geology and Mineral Industries (Burns and Madin, 2009).

ASSESSING THE AGREEMENT BETWEEN EO-BASED SEMI-AUTOMATED LANDSLIDE MAPS WITH FUZZY MANUAL LANDSLIDE DELINEATION

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