Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province, China

The rainfall can contribute significantly to landslide events, especially in hilly areas. The landslide susceptibility map (LSM) usually helps to mitigate disasters. However, how to accurately predict the susceptibility of landslides is still a difficult point in the field of disaster research. In this study, five advanced machine learning technologies (MLTs), including the Light Gradient Boosting Machine, extreme gradient boost, categorical boosting (CatBoost), support vector machine, and random forest, are utilized to landslide susceptibility modeling and their capabilities are compared through evaluation indicators. The northern part of Yanping, Fujian Province, China, is selected as the research object, because this area experienced mass landslide events due to extremely heavy rainfall in June 2010, resulting in many casualties and a large number of public facilities destroyed. The influencing factors for landslides, namely topographic, hydrological, geologic and human activities, are prepared from various data sources based on the availability. Through the analysis of the actual situation in the study area, 13 suitable landslide condition factors are considered and the availability of relevant factors is checked according to the multicollinearity test. The landslide inventory including 631 samples in this study area is obtained from historical information, satellite data in Google earth and performed field surveys. The landslide inventory is randomly divided into two datasets for model training and testing with a 7:3 ratio. The area under the curve of ROC, accuracy rate, Kappa index and F1 score are applied to compare the MLTs capabilities. In this paper, the results of factor importance analysis show that the first three important condition factors are the distance to faults, the distance to drainages and the slope. According to the LSMs, in the study area, the central and western regions are at high and very high landslide susceptibility levels, while almost all the eastern and northeastern regions are at medium and low landslide susceptibility levels. The CatBoost model is a very promising technology in landslide research according to the evaluation results, which means that for landslide susceptibility research, gradient boosting algorithms may get more accurate results and show better prospects in the future. Finally, the results of this paper will contribute to environmental protection to a certain extent.