Assessment of landslide hazard under specific rainfall scenarios based on a hybrid CNN-TRIGRS and matrix approach: a case study of Meishan Town, Zhejiang Province, China.

As a frequent and devastating global geological hazard, landslides pose significant threats to communities, infrastructure, and the environment. The study area in Meishan Town, China, is particularly susceptible to rainfall-induced landslides due to frequent typhoons and rainstorms. This study proposes a novel hybrid framework that integrates a Convolutional Neural Network (CNN) with the TRIGRS physical model for landslide hazard assessment under four rainfall scenarios. The core innovation is a transparent and interpretable matrix-based coupling method that combines a CNN-derived landslide susceptibility index with the Factor of Safety (Fs) computed by TRIGRS, enabling a more comprehensive hazard zonation. To address the prevalent issue of imbalanced data in landslide inventories, we optimized the CNN training by comparing SMOTE-TL and RUS sampling strategies, finding SMOTE-TL significantly superior (AUC: 0.87 vs. 0.83). Our approach, which does not require extensive pre-failure rainfall data, produces hazard maps that are strongly consistent with the historical landslide inventory. This study provides an efficient and practical solution for landslide hazard assessment, particularly in data-scarce regions.