A comparative study of intelligent prediction models for landslide susceptibility: random forest and support vector machine

Colluvial landslides widely developed in mountainous and hilly areas have the characteristics of mass occurrence and sudden occurrence. How to reveal the spatial distribution rules of potential landslides quickly and accurately is of great significance for landslide warning and prevention in the study area. Landslide susceptibility prediction (LSP) modeling provides an effective way to reveal the spatial distribution of regional landslides, however, it is difficult to accurately divide slope units and select prediction models in the processes of LSP modeling. To solve these problems, this paper takes the widely developed colluvial landslides in Dingnan County, Jiangxi Province, China as the research object. Firstly, the multi-scale segmentation (MSS) algorithm is used to divide Dingnan County into 100,000 slope units, to improve the efficiency and accuracy of slope unit division. Secondly, 18 environmental factors with abundant types and clear meanings, including topography, lithology and hydrological environment factors, were selected as input variables of LSP models. Then, a widely representative Support Vector Machine (SVM) and Random Forest (RF) models were selected to explore the difference characteristics of various machine learning models in predicting landslide susceptibility. Finally, the comprehensive evaluation method is proposed to compare the accuracy of various slope unit-based machine learning methods for LSP. The results show that the MSS algorithm can divide slope units in Dingnan County efficiently and accurately. The RF model (AUC = 0.896) has a higher LSP accuracy than that of the SVM model (AUC = 0.871), and the landslide susceptibility indexes (LSI) predicted by the RF model have a smaller mean value and a larger standard deviation than those of the SVM model. Conclusively, the overall performance of RF model in predicting landslide susceptibility is higher than that of SVM model.