Landslide susceptibility prediction and identification of its main environmental factors based on machine learning models

Abstract

The modelling processes and uncertainties of various machine learning models for landslide susceptibility prediction (LSP) are different, and effectively identifying the main conditioning factors of landslide susceptibility is of great significance. Aiming at these problems, this study aims to discuss the LSP processes and the uncertainties of landslide susceptibility based on machine learning models, namely, support vector machine (SVM) and random forest (RF), and then to innovatively propose the "weighted mean method" for calculating more accurate landslide main control factors. First, the landslide inventories and 10 basic environmental factors of Yanchang County in Shaanxi Province are obtained, and the frequency ratios (FRs) of the environmental factors are taken as the input variables of the SVM and RF models.Then, the landslide and randomly selected nonlandslide samples are divided into model training and testing datasets. Furthermore, the trained RF and SVM models are used to predict the landslide susceptibility and draw the landslide susceptibility prediction (LSP) map.Finally, the uncertainties of LSP modelling are evaluated by the receiver operating characteristic (ROC) curve, mean value and standard deviation, and the main landslide control factors are calculated.The results show that ① Machine learning models can effectively predict the susceptibility of regional landslides. The accuracy of RF in LSP is higher, and its uncertainties are lower than those of SVM. As a whole, the landslide susceptibility distribution rules of the two models are similar.②The main control factors of landslide susceptibility in Yanchang County calculated by the weighted mean method are slope, elevation and lithology.③Case studies and literature reviews show that the RF model is a more reliable susceptibility model than other types of machine learning models.

Keywords

• landslide susceptibility prediction
• uncertainties analysis
• identification of the main control factors
• support vector machine
• random forest