Towards Accurate Prediction of Runout Distance of Rainfall-Induced Shallow Landslides: An Integrated Remote Sensing and Explainable Machine Learning Framework in Southeast China

This study addresses the challenge of predicting runout distance of rainfall-induced shallow landslides by integrating deep learning and explainable machine learning. Using the June 2024 landslide disaster at the Fujian-Guangdong-Jiangxi border as a case study and remote sensing images as the data source, we developed an improved U-Shaped Convolutional Neural Network model (RAC-Unet) combining Deep Residual Structure, Atrous Spatial Pyramid Pooling, and Convolutional Block Attention Module modules. The model identified 34,376 shallow landslides and built a dynamic parameter database with 8875 samples, which was used for data-driven model training. After comparing models, Extreme Gradient Boosting was chosen as the best (R2 = 0.923), with its performance confirmed by Wilcoxon analysis and good generalization in external validation (R2 = 0.877). SHapley Additive Explanations analysis revealed how factors like the area of the sliding source zone (SA), length/width ratio of the sliding source zone (SLWR), and average slope of the source zone (SS) affect landslide runout, a simplified model using the three parameters SA, SLWR, and SS was constructed (R2 = 0.862). Compared to traditional models, this integrated framework solves the pre-disaster impact range estimation problem, deepens understanding of shallow landslide dynamics, and enables accurate pre- and post-disaster predictions. It provides comprehensive support for disaster risk assessment and emergency response in southeastern hilly areas.