Essential insights into decision mechanism of landslide susceptibility mapping based on different machine learning models

Abstract

This work aims to discuss and compare the inherent essence of different machine learning algorithms for landslide susceptibility models (LSMs), which is of great significance for accurate prevention and detection of landslides. A geospatial database was established in GIS based on various factors of topography, geological conditions, environmental conditions and human activities, including 22 conditioning factors and 866 historical landslides. As for model algorithms, ANN is an operation model composed of a large number of interconnected nodes, and RF refers to an ensemble method of separately trained binary decision trees. Two algorithms were adopted in this paper for landslide susceptibility models. Meantime, an interpretable algorithm SHAP was used to gain insight into essential decision mechanism of LSMs. The result showed that RF model exhibits better stability and robustness. The global interpretation shows that the same landslide moderators play different roles in different models. The local interpretation shows that for the same evaluation unit different models give different decision mechanisms, and the local interpretation can be combined with the field survey, which can provide a comprehensive framework for assessing the assigned landslide.