Comparison of Hybrid Models Based on the Infinite Slope Stability Analysis and Different Data-Driven Approaches for Regional Landslide Susceptibility Mapping

Landslide susceptibility mapping (LSM) predicts the possibility of future landslides and is critical for risk assessment, resource allocation, and land-use planning. To promote the prediction accuracy, generalization ability, practicability, and interpretability of LSM models, this paper makes the following novel contributions: (1) Two hybrid models based on the infinite slope stability model (ISSM) and different data-driven approaches for regional LSM are proposed. The adopted data-driven approaches are the logistic regression (LR) model and convolutional neural network (CNN); and (2) The LR model is compared with the LR-ISSM hybrid model to verify the important role of the physical module. The results reveal the necessity of considering the spatial correlation among grids/pixels for grid-based LSM models. While the CNN-ISSM hybrid model is slightly more accurate, the LR-ISSM hybrid model has better interpretability and produces promising prediction accuracy and generalization ability.