Assessing landslide susceptibility using Bayesian probability-based weight of evidence model

Sujatha, Evangelin Ramani; Kumaravel, P.; Rajamanickam, G. V.

doi:10.1007/s10064-013-0537-9

Cited by 43 publications

(20 citation statements)

References 29 publications

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“…Para la evaluación de la probabilidad espacial, se utilizó el método estadístico bivariado Peso de la Evidencia (Chung y Fabbri, 1999;Van Westen et al, 2003), basado en la teoría de la probabilidad Bayesiana, donde se analiza la relación entre las áreas afectadas por los movimientos en masa y la distribución espacial de los factores condicionantes del terreno (Sujatha et al, 2014;Regmi et al, 2010). Este método arroja los pesos de las clases que conforman las variables condicionantes (Van Westen et al, 2003), indicando la presencia e influencia de la clase como parámetro en la ocurrencia del movimiento en masa (Regmi et al, 2010;Oh y Lee, 2011).…”

Section: Estimación De La Probabilidad Espacialunclassified

Evaluación de la amenaza por movimientos en masa detonados por lluvias para una región de los Andes colombianos estimando la probabilidad espacial, temporal, y magnitud

Aristizábal

López

Sánchez

et al. 2019

revbol

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Los movimientos en masa detonados por lluvia son una de las amenazas que cobra el mayor número de víctimas anuales en terrenos montañosos y ambientes tropicales, como los Andes colombianos. En el presente estudio, se evaluó la amenaza por movimientos en masa detonados por lluvias en el Valle de Aburrá, localizado en el norte de los Andes colombianos, donde se asienta un número importante de viviendas y familias en áreas altamente susceptibles. Por esta razón, se presenta un método cuantitativo que permite evaluar de forma holística la amenaza combinando la probabilidad espacial, temporal y magnitud. Para la probabilidad espacial de ocurrencia, se utiliza el método estadístico bivariado denominado Peso de la Evidencia. Para la probabilidad temporal, se identificaron los umbrales de lluvia detonante y lluvia antecedente requerida para detonar un movimiento en masa y su probabilidad temporal diaria, y finalmente, para la probabilidad de magnitud, se utilizó la curva magnitud-frecuencia de acuerdo con el inventario multitemporal de movimientos en masa elaborado. Los resultados obtenidos señalan que la categoría de amenaza alta representa el 75% de los movimientos en masa del inventario elaborado y abarca el 37% del área de estudio, la categoría media representa 28% de los movimientos en masa del inventario y el 56% del área de estudio, y finalmente la categoría de amenaza baja representa tan solo el 25% de los movimientos en masa del inventario y el 7% del área de estudio.

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Section: Estimación De La Probabilidad Espacialunclassified

Evaluación de la amenaza por movimientos en masa detonados por lluvias para una región de los Andes colombianos estimando la probabilidad espacial, temporal, y magnitud

Aristizábal

López

Sánchez

et al. 2019

revbol

View full text Add to dashboard Cite

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“…There are four possible combinations for every factor, the frequency of which is expressed as a number of pixels and can be calculated in GIS. Thorough expositions of WoE modelling can be found in Lee and Choi (2004and Sujatha et al 2014.…”

Section: Weights Of Evidence (Woe)mentioning

confidence: 99%

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Tehrany

Shabani

Jebur

et al. 2017

Geomatics, Natural Hazards and Risk

198

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The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.

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“…The major difference in bivariate approaches is that the influence of each variable is evaluated independently before they are combined to form a unique LSI. The so-called 'influence' in bivariate approaches can be quantified by several ways, such as the probabilistic frequency ratio (PFR) (Lee and Pradhan 2006), weight of evidence (WOE) (Sujatha et al 2014) and IFV (Cevik and Topal 2003). All of these methods were based on the assumption that the predictive (causative) factors are conditionally independent of each other and possible conditional or posterior probabilities exist with regard to landslide occurrence (Schicker and Moon 2012).…”

Section: Identification Of Inventory-based Statistical Modelsmentioning

confidence: 99%

Identification of inventory-based susceptibility models for assessing landslide probability: a case study of the Gaoping River Basin, Taiwan

Harrison

Chang

Liu

2017

Geomatics, Natural Hazards and Risk

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This study describes the use of inventory-based landslide susceptibility index (LSI) models based on the selection of causative factors, functional relationships between factors and integration to a hazard-warning model. We first merged landslide inventory data of five typhoon-training events and obtained sets of data representing environmental conditions where landslides are likely to occur. These well-defined data sets are used to select five representative causative factors, i.e. the slope angle, rock strength, drainage, curvature and soil type. Four bivariate statistical model combinations were tested: the linear combination, geometric mean, and two other mixed combinations. As a result, the modulation effects between (i) rock strength and slope and (ii) drainage and curvature were intensified in mixed model 2 (MX2) through factor multiplication. The MX2 LSI was integrated with three multivariate landslide hazard-warning models and tested with four triggering factor rainfall parameter sets. Results lead to the conclusion that threshold models with terraininfluenced rainfall can better identify hazard-warning locations. Modulating factor combinations for the hazard warning can also mirror true environmental conditions, yielding more representative model results. This study improves the method for identifying LSI models for the application to rainfall-triggered landslide hazard models.

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Assessing landslide susceptibility using Bayesian probability-based weight of evidence model

Cited by 43 publications

References 29 publications

Evaluación de la amenaza por movimientos en masa detonados por lluvias para una región de los Andes colombianos estimando la probabilidad espacial, temporal, y magnitud

Evaluación de la amenaza por movimientos en masa detonados por lluvias para una región de los Andes colombianos estimando la probabilidad espacial, temporal, y magnitud

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Identification of inventory-based susceptibility models for assessing landslide probability: a case study of the Gaoping River Basin, Taiwan

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