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

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 model...

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Bibliographic Details
Published inApplied sciences Vol. 9; no. 22; p. 4756
Main Authors Yu, Lanbing, Cao, Ying, Zhou, Chao, Wang, Yang, Huo, Zhitao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 07.11.2019
Subjects
Accuracy
Canyons
Case studies
Environmental regulations
Geology
Landslides
Landslides & mudslides
Learning algorithms
Learning theory
Machine learning
Mapping
Model accuracy
Modelling
Neural networks
Propagation
Rain
Reservoirs
Segments
Statistical analysis
Support vector machines
Susceptibility
susceptibility mapping
Technical services
three gorges reservoir area (tgra)
Water levels
China
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9224756