Regional Landslide Susceptibility Analysis Based on INLA-SPDE Method
Landslide disasters pose significant threats to regional property and human safety.Therefore,conducting landslide susceptibility maps is crucial for effectively preventing and mitigating landslide risks.However,tradi-tional landslide susceptibility assessment methods are mostly based on raster or slope units and fail to fully con-sider modeling requirements based on specific landslide locations as the study units.Additionally,the selection of risk factors lacks objectivity.This study employed the SHAP algorithm to identify ten key risk factors:slope,pre-cipitation,aspect,distance to roads,distance to rivers,roughness,elevation,lithology,distance to settlements,and seismic intensity.Subsequently,the Integrated Nested Laplace Approximation-Stochastic Partial Differential Equa-tions(INLA-SPDE)method was utilized to construct a logistic regression model incorporating spatial random effects,effectively avoiding the impact of spatial autocorrelation on landslide susceptibility prediction.This model was used to predict the spatial distribution of landslides along the Duwen Highway in Sichuan Province.The re-sults show that the model's AUC value is 0.846,demonstrating good evaluation performance and providing a novel research approach for regional landslide susceptibility assessment.
万方数据
维普
