Evaluating the Efficiency of Probabilistic Weight of Evidence Model for Landslide Susceptibility Mapping (Case Study: Siyahbisheh Watershed, Mazandaran)

Abstract. The objective of this paper is to assess the impact of the datasets quality for the landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted in the Pays d'Auge plateau (Normandy, France) with a scale objective of 1/10000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, geomophological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlights that only high quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formations maps) can predict a satisfying proportion of landslides on the study area.

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Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region

CATENA ◽

10.1016/j.catena.2020.104851 ◽

2020 ◽

Vol 195 ◽

pp. 104851 ◽

Cited By ~ 3

Author(s):

Yaning Yi ◽

Zhijie Zhang ◽

Wanchang Zhang ◽

Huihui Jia ◽

Jianqiang Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Landslide Susceptibility ◽

Sampling Strategy ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Multiscale Sampling

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Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Symmetry ◽

10.3390/sym11060762 ◽

2019 ◽

Vol 11 (6) ◽

pp. 762 ◽

Cited By ~ 8

Author(s):

Renwei Li ◽

Nianqin Wang

Keyword(s):

Land Use ◽

Logistic Regression ◽

Statistical Models ◽

Landslide Susceptibility ◽

Slope Angle ◽

Susceptibility Mapping ◽

Aerial Photographs ◽

Weight Of Evidence ◽

Landslide Susceptibility Mapping ◽

Topographic Wetness Index

The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.

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GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia

Open Geosciences ◽

10.1515/geo-2019-0056 ◽

2019 ◽

Vol 11 (1) ◽

pp. 708-726

Author(s):

Zorgati Anis ◽

Gallala Wissem ◽

Vakhshoori Vali ◽

Habib Smida ◽

Gaied Mohamed Essghaier

Keyword(s):

Landslide Susceptibility ◽

Vegetation Index ◽

Satellite Image ◽

Susceptibility Mapping ◽

Weight Of Evidence ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Susceptibility Maps ◽

North Western ◽

Landslide Susceptibility Maps

AbstractThe Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area.

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