A Hybrid Physics–Machine Learning Framework for Landslide Susceptibility Assessment with an Improved Non–Landslide Sampling Strategy

Rainfall–triggered clustered landslides pose severe risks to communities and infrastructure in mountainous regions. High–precision susceptibility assessment is essential for early warning and hazard mitigation. The traditional buffering method neglects physical slope stability mechanisms, leading to the misclassification of potentially unstable areas. To improve susceptibility model accuracy, we propose an improved non–landslide sampling strategy that integrates the physical–model TRIGRS (Transient Rainfall Infiltration and Grid–based Regional Slope–Stability Model) with 50 m buffering constraints. A hybrid physics–machine learning framework is used to evaluate the performance of landslide susceptibility assessment across four machine learning models, such as Multi–Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). Among the four models, the TRIGRS model integrated with MLP achieves the highest accuracy in susceptibility mapping. The improved non–landslide sampling strategy increased average Area Under the Curve (AUC) by 16.46% in random cross–validation and improved spatial generalization capability by 29% in spatial cross–validation, demonstrating its robustness in unseen areas. SHAP factor analysis further confirms rainfall, groundwater table, and human activity as the primary influencing factors, which aligns with physical mechanisms and improves model interpretability. Therefore, the proposed non–landslide sampling strategy coupled with the TRIGRS and MLP models outperforms traditional buffering method in evaluating regional landslide susceptibility, providing a more physically basis for geohazard risk assessment.