An interpretation study on the ml models for landslide susceptibility mapping

Natural hazards frequently threaten human life, ecosystem, and economy all around the world, while landslides are the most destructive ones. Landslide susceptibility mapping (LSM) is an effective strategy to determine the probability of future landslide events by involving a comprehensive analysis of various factors, including geological environmental, historical landslides and the landslide physical laws. As artificial intelligence techniques are becoming more popular in LSM, it is important to understand how decisions are made by these models. This study aims to use representative ML (ML) and deep learning (DL) models (include random forest (RF), support vector machine (SVM), residual neural networks (ResNet) and Densely connected convolutional networks (DenseNet)) to map the landslide susceptibility of the study area in Zigui, and then visualize the decision-making process of the model through an explainable artificial intelligence (XAI) technology, so as to provide more transparency and reliability about the occurrence of landslides.