Predicting Dynamic Landslide Susceptibility Under Changing Land-Use Scenarios with Generalized Additive Model

Conventional landslide susceptibility assessment (LSA) generally relies on static land-use/land-cover (LULC) data, limiting its ability to capture the influence of future land-use evolution on landslide susceptibility. To address this limitation, this study proposes a dynamic LSA framework by integrating the Patch-generating Land Use Simulation (PLUS) model with a slope unit-based Generalized Additive Model (GAM). Using Wushan County in the Three Gorges Reservoir Area (TGRA), China, as a case study, historical LULC data were first analyzed, and future LULC scenarios for 2026 and 2030 were simulated using the PLUS model. Landslide susceptibility under different LULC scenarios was then evaluated using the interpretable GAM, while the statistical association between LULC categories and the spatial distribution of LSI was quantified using the GeoDetector model. The results show that the PLUS model accurately reproduced LULC evolution with a Kappa coefficient of 0.963. The GAM exhibited robust predictive performance, with 100 repeated spatial cross-validations (SCVs) yielding a mean AUC value exceeding 0.75. Although dynamic LULC had only a limited influence on the overall spatial pattern of landslide susceptibility, the proportion of very high-susceptibility areas gradually increased from 4.54% in 2022 to 4.62% in 2030. The interpretable model further revealed distinct nonlinear responses of environmental variables and demonstrated that different LULC categories contributed differently to the spatial distribution of landslide susceptibility. The proposed framework provides a practical approach for incorporating future land-use dynamics into landslide susceptibility assessment and offers valuable support for long-term landslide risk management and sustainable land-use planning.