Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping

• Published in: Catena (Giessen) 2020-08, Vol.191, p.104580, Article 104580
• Main Authors: Huang, Faming, Cao, Zhongshan, Guo, Jianfei, Jiang, Shui-Hua, Li, Shu, Guo, Zizheng
• Format: Article
• Language: English
• Subjects: Analytic hierarchy process; Back-propagation neural network; C5.0 decision tree; General statistical model; Landslide susceptibility prediction; Machine learning model; Support vector machine
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2020.104580

•Three types of data-based landslide susceptibility prediction (LSP) models are compared.•Machine learning has higher LSP accuracy than heuristic and general statistical models.•Accurate landslide susceptibility maps of Shicheng County are produced.•Contributions of different factors to susceptibility prediction are identified.•Distribution rules of landslide susceptibility indexes of each model are explored. Commonly used data-driven models for landslide susceptibility prediction (LSP) can be mainly classified as heuristic, general statistical or machine learning models. This study plans to compare the prediction performance of these data-driven models on the landslide susceptibility mapping, thus further to explore the inherently features of these data-driven models. As a result, a more accurate and reliable LSP can be realized through choosing an optimal data-based model. A heuristic model represented by the analytic hierarchy process (AHP), a general statistical model represented by the general linear model (GLM) and information value (IV) model, and machine learning models represented by binary logistic regression (BLR), Multilayer Perceptron (MLP), back-propagation neural network (BPNN), support vector machine (SVM) and C5.0 decision tree (C5.0 DT) are adopted in this study. Shicheng County in China is used as the study area. In total, 369 landslides identified through field investigation are classified as training (70%) and testing datasets (30%). Next, 13 landslide conditioning factors (elevation, slope, aspect, plan curvature, profile curvature, relief amplitude, total surface radiation, population density, Normalized difference vegetation index, distance to river, topographic wetness index and rock types) are acquired from data sources of the free remote sensing images, Digital Elevation Model, field investigation and government reports. The correlations between these conditioning factors and the landslide locations are determined by frequency ratio analysis. Then, the landslide susceptibility indexes (LSIs) calculated by the eight trained models are imported into GIS software to produce landslide susceptibility maps of Shicheng County. Finally, the area under receiver operating characteristic curve (AUC), the calculated LSIs are applied to assess the LSP performance of the present eight models. The testing results show that these eight models generate reasonable LSP results as a whole, further showing that the C5.0 DT is of the highest predictio