Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. (March 2018)

• Record Type: Journal Article
• Authors: Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Li, Yuanyao; Catani, Filippo; Pourghasemi, Hamid Reza
• Is Part Of: Computers & geosciences. Volume 112(2018)
• Page Start: 23
• Page End: 37
• Publication Date: 2018-03
• Subjects: Landslide susceptibility modeling -- Machine learning -- Support vector machine (SVM) -- Artificial neural network (ANN) -- Logistic regression (LR); Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2017.11.019
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: 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. Highlights: The development laws of the colluvial landslide and rockfall were analyzed. The model performance was improved by eliminating less important factors. The separated modeling of each landslide type has significantly increased the prediction accuracy. The performance of three models was compared and the SVM model performed the best.