Investigating machine learning and statistical approaches for landslide susceptibility mapping in Minfeng County, Xinjiang

Abstract Landslide susceptibility assessment in arid mountainous regions requires specialized modeling approaches. This study, combining the information value (IV) modeling and machine learning, develops a coupled model approach for Minfeng County, Xinjiang, that a complex arid zone with frequent landslides. From the ten influencing factors, seven key factors were identified through factor covariance and correlation studies, so as to construct the landslide susceptibility evaluation index system. On this basis, using 135 landslide samples and combining the output of the information value (IV) model with four machine learning algorithms–support vector machine (SVM), logistic regression (LR), random forest (RF), and artificial neural network (ANN)–we constructed four coupled models (IV-LR, IV-ANN, IV-SVM, and IV-RF) for landslide susceptibility evaluation. Critical results are as follows: (1) proximity to rivers/roads and vegetation density (NDVI) dominate landslide triggers and (2) all models showed high accuracy (area under curve [AUC] > 85%) with 7:3 training:testing validation and the IV-RF model achieved optimal high-susceptibility zone delineation (accuracy = 82.71%; AUC = 0.8945). This method provides a technical reference for landslide disaster prediction, prevention, and mitigation in arid mountainous areas of Xinjiang.