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

By Chao Zhou, Kunlong Yin, Ying Cao, Bayes Ahmed, Yuanyao Li, Filippo Catani, Hamid Reza Pourghasemi. Computational Geosciences, 2018

351 citations recorded by the source corpus.

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.

Subfields: Management, Monitoring, Policy and Law, Mechanical Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/25b9a18efdaf3f35187d5e7d4163969be2214995

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Citations in the corpus

Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China (2019, result): “In this paper, the selected condition factors are elevation (X1), vegetation index (X2), slope (X3), average annual rainfall (X4), surface cutting density (X5) and overburden soil type (X6), according to expert opinions and the results from the studies (Bui et al. 2016; Fuheng 2004; Baocheng 2011; Z”
Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model (2020, methodology): “Meanwhile, many machine learning algorithms have been developed for landslide susceptibility mapping and landslide risk analysis (Huang et al. 2017a, b; Zhou et al. 2017).”
Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, methodology): “…neural network (Pradhan and Lee 2010; Dou et al. 2015; Polykretis et al. 2015), support vector machines (Hong et al. 2017a; Chen et al. 2018c; Zhou et al. 2018), naive Bayes (Tien Bui et al. 2012a; Tsangaratos and Ilia 2016; Pham et al. 2016a), and boosted regression trees (Youssef et al.…”
Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models (2020, methodology): “…predict because of the complex nature and development of the landslides, although several researchers have attempted it in the TGR (Bai et al. 2010; Bi et al. 2014; Peng et al. 2014; Chen et al. 2015; 2016; Xu et al. 2015; Zhang et al. 2016b; Wang et al. 2017; Zhang et al. 2017; Zhou et al. 2018).”