Regional dynamic hazard assessment of rainfall-induced landslide guided by geographic similarity

Landslides triggered by rainfall are complex phenomena influenced by a multitude of condition and trigger factors. A significant challenge in the field is the accurate and interpretable assessment of large-scale landslide hazards, particularly due to the lack of consideration for the synergistic effects of multiple triggers and spatial heterogeneity. This study introduces a novel regional hazard assessment method that leverages geographic similarity to address these challenges. Our approach consists of four key steps: (1) extraction of sample information from relevant data based on the historical distribution of landslides and their influencing factors, (2) application of a scale-space algorithm to manage spatial heterogeneity, with a partition scale determined by the q-value variation, (3) optimization of sample configuration and generation criteria under the guidance of geographic similarity for enhanced spatiotemporal modeling, and (4) utilization of machine learning models to refine inductive bias and capture nonlinear relationships, enabling a quantitative estimation of hazard probabilities for each slope unit within the prediction module. We applied our P-RF + method to Yunnan Province, China, incorporating 11 condition factors and 7 trigger factors across 624 historical rainfall-induced landslides and 1248 non-landslide cases. Comparative experiments reveal that the P-RF + model substantially outperforms existing methods in accuracy and interpretability. Furthermore, a case study during the rainy season illustrates the model's capability to provide timely warning instructions for rainfall-induced landslides. These findings underscore the potential of our proposed method to offer valuable insights for disaster prevention decision-making.