LightGBM-SHAP landslide susceptibility by different factor screening methods

To study Qianjiang District of Chongqing Municipality, 23 evaluation factors are selected to construct a landslide disaster-causing factor database, optimal combination of factors is chosen utilizing two factor-screening methods: geodetector and Pearson-principal component analysis． Bayesian-LightGBM-SHAP hybrid model for landslide susceptibility evaluation was applied to verify model accuracy, and to analyze dominant factors affecting landslide occurrence in Qianjiang District． The initial model had an AUC value of 0.801, Pearson Correlation Coefficient-Bayesian-LightGBM model had an AUC value of 0.824, whereas GeoDetector-Bayesian-LightGBM model had an AUC of 0.835． Importance of factors was ranked． Average multi-year rainfall, elevation, POI kernel density and distance from rivers were found the most important factors for landslides to occur, while the sand transport index, hydrodynamic index and slope position had a weaker effect． The hybrid model combining factor screening method-Bayesian-LightGBM could improve accuracy of the model and provide a reference framework for constructing a rational factor database． Integrating analysis with factor significance verified that geo-probe could accurately detect contribution value of each factor to landslide occurrence． This highlights correlation between each combination of landslide conditioning factors, thus clarifies the relationship between each factor and landslides．