title: Investigation of the Interpretability and Dynamic Spatiotemporal Mapping of Landslide Susceptibility Assessment Models in the Three Gorges Reservoir Area, China
Creators: name: Chen, Cheng
Publisher: name: University of Liverpool Repository
Year: 2026

description: Landslides are a frequently occurring significant geological hazard. To effectively prevent and mitigate landslide hazards, landslide susceptibility mapping (LSM) has been widely adopted. The past decade has witnessed an increasing use of machine learning (ML) and deep learning (DL) models for producing LSM. However, challenges arise from the limited interpretability of those models and their inability to respond to constantly changing conditions. To address these issues, this research aims to develop novel DL-based models with improved interpretability and capabilities for dynamic spatiotemporal mapping, for landslide susceptibility assessments in the Three Gorges Reservoir Area in China. Specific objectives of this research include: (1) Identify optimal factor selection strategies for LSM models by evaluating and comparing different selection methods for ML and DL models; (2) Develop an interpretable DL model specifically tailored for explaining landslide occurrences in LSM tasks; (3) Evaluate model interpretability across a range of interpretable models and assess their capabilities in providing understandable and rational insights into landslide susceptibility; (4) Construct a dynamic LSM model that integrates multi-source dynamic data such as sequential rainfall measurements and ground surface displacements derived from Interferometric Synthetic Aperture Radar, in which graph convolutional networks are adopted to enable continuous assessments of landslide susceptibility in response to evolving environmental conditions. Through comprehensive experiments, this study reveals that factor selection methods can effectively enhance the predictive accuracy of mainstream ML classifiers including Support Vector Machine, Logistic Regression, Random Forest and Extreme Gradient Boosting by eliminating redundant or irrelevant factors. However, they do not improve the predictive accuracy of sophisticated DL models with inherent ability to optimise factor combination. The proposed Deep-Attention-LSF model, integrating an attribution branch network and an attention mechanism into a DL framework, not only increases the overall predictive accuracy of landslide susceptibility but also demonstrates improved model interpretability, especially the identification of the significance of each contributing factor to individual landslide occurrences. However, comparative assessment across diverse landslide susceptibility mapping models and interpretation methods reveals inconsistencies in factor attribution results, demonstrating the complexity of interpreting landslide causes. Individual interpretation methods cannot fully explain multifaceted landslide triggers. This research pioneers a spatiotemporal dynamic framework based on graph convolutional networks, incorporating multi-temporal data such as sequential rainfall measurements and remotely sensed ground surface displacements. This enables responsive and on-demand landslide forecasting adapted to evolving conditions.