Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines: A Case Study from Wushan Segment in the Three Gorges Reservoir Area, China

Abstract

Landslides are destructive geological hazards that occur all over the world. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA. The Wushan segment of TGRA was selected as a case study. At first, 165 landslides were identified and a total of 14 landslide causal factors were constructed from different data sources. Multicollinearity analysis and information gain ratio (IGR) model were applied to select landslide causal factors. Subsequently, the landslide susceptibility mapping using the calculated results of four models, namely, support vector machines (SVM), artificial neural networks (ANN), classification and regression tree (CART), and logistic regression (LR). The accuracy of these four maps were evaluated using the receive operating characteristic (ROC) and the accuracy statistic. Results revealed that eliminating the inconsequential factors can perhaps improve the accuracy of landslide susceptibility modelling, and the SVM model had the best performance in this study, providing strong technical support for landslide susceptibility modelling in TGRA.

Integrating InSAR Observables and Multiple Geological Factors for Landslide Susceptibility Assessment

“…The index calculations of these factors are described below. It should be mentioned that these spatial factors were first selected based on suggestions reported in the relevant studies in the literature [16][17][18][19][20]. A significance test was then performed to identify the most influential factors that have the high correlation with the landslides in the study areas.…”

“…Machine learning algorithms enrich the quality and accuracy of generated susceptibility maps. Researchers use and compare various machine learning models on the basis of different data [16][17][18][19], integrate different machine learning models to improve accuracy [20][21][22][23], or develop new algorithms that are based on traditional machine learning models to strengthen landslide prediction results [24][25][26]. These techniques perform better than do classical methods.…”

Special Issue on “Mapping and Monitoring of Geohazards”

“…The published papers cover three types of geohazards across eight different countries: earthquake [4][5][6], landslide [7][8][9][10][11][12][13][14], and volcanic hazards [15]. As of 21st May 2020, almost 6 months after the deadline for submissions, the 12 papers have been viewed 6096 times, downloaded 4868 times, and cited 16 times overall.…”

“…In similarity to [10], the paper by Yu et al [11] also focusses on LSM, but in the Three Gorges Reservoir area (Chongqing municipality, China). It employs an information gain ratio (IGR) model to select landslide causal factors, before subsequently exploring the performance of machine learning models, including support vector machines (SVM), for LSM.…”

Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)’s main route (Karakorum Highway)

“…Further, to combine different models to identify potential landslide areas through Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)'s main route (Karakorum Highway) [19] developing LSM contributed to manage risk in a sustainable way [7,8]. More recently Artificial Intelligence techniques have been used to identify potential landslides [9][10][11][12][13]. Similarly, combining different approaches that include interferometric synthetic aperture radar (InSAR) images for monitoring and assessment of landslides have led to high accuracy in identification of potential landslides [14][15][16][17][18].…”