A physics-informed data-driven model for landslide susceptibility assessment in the Three Gorges Reservoir area

Abstract

Landslide susceptibility mapping is a crucial tool for analyzing geohazards in a region. Recent publications have popularized data-driven models, particularly machine learning-based methods, owing to their strong capability in dealing with complex nonlinear problems. However, a significant proportion of these models have neglected qualitative aspects during analysis, resulting in a lack of interpretability throughout the process and causing inaccuracies in the negative sample extraction. In this study, Scoops 3D was employed as a physics-informed tool to qualitatively assess slope stability in the study area (the Hubei Province section of the Three Gorges Reservoir Area). The non-landslide samples were extracted based on the calculated factor of safety (FS). Subsequently, the random forest algorithm was employed for data-driven landslide susceptibility analysis, with the area under the receiver operating characteristic curve (AUC) serving as the model evaluation index. Compared to the benchmark model (i.e., the standard method of utilizing the pure random forest algorithm), the proposed method's AUC value improved by 20.1%, validating the effectiveness of the dual-driven method (physics-informed data-driven).d.

Keywords

• Machine Learning
• Physics-informed
• Negative sample extraction
• Interpretability
• Dual-driven