A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping

Abstract

The aim of this study was to develop a hybrid model (Geo-RFE-RF) for Landslide Susceptibility Mapping (LSM) predicated on GeoDetector and Random Forest (RF) using the Recursive Feature Elimination (RFE-RF) method for eliminating redundant and noise factors. At the outset, for the sample of 1522 investigated landslides and 1522 non-landslides, twenty-two factors were chosen as the initial landslide-conditioning factors to construct a spatial database. Subsequently, the GeoDetector and RFE-RF methods were adopted to eliminate the least effective factors, respectively, with the Geo-RFE-RF model being formulated with the combination factors of these two methods. Finally, the performance of the Geo-RFE-RF and RF model with twenty-two initial factors (In-RF) were compared and assessed, and the higher accuracy model was employed to generate a LSM in a case study area, Fengjie County (China). The results indicate that, the Area Under Curve, Accuracy, Precision, and F1 Score of the test dataset is increased by 0.9%, 0.4%, 1.5%, and 0.3%, respectively, under the Geo-RFE-RF model, as compared to the In-RF model. The conditioning factors used to construct the model have been reduced from twenty-two to thirteen, but the predictive ability of the Geo-RFE-RF model performs better, proving the effectiveness of the hybrid model that combines the factors from GeoDetector and RFE-RF methods. This hybrid model not only considers the spatial pattern characteristics of spatial data for screening factors, but the selected factors are also in good agreement with the adopted machine learning method, offering potential use as a general framework for eliminating redundant and noise factors in LSM across the globe.