Abstract

The main purpose of this article is to establish a susceptibility zonation map of the landslides in Phuoc Thanh commune, Phuoc Son district, Quang Nam province on a large scale using statistical methods and machine learning models. First, the five Landslide Susceptibility Index (LSI) maps were established from two statistical models (Logistic Regression - LR, Discriminant Analysis – DA) and three machine learning models (Bayesian Network – BN, Artificial Neural Network – ANN, Support Vector Machine – SVM)
were generated based on seven maps of landslide conditioning factors (slope, curvature, stream power index-SPI, topographic wetness index-TWI, sediment transportation index-STI, land use/land cover and weathering crust). Next, the five LSI maps will be evaluated for performance with the value of Area Under the Curve (AUC) according to the Receiver Operating Characteristic (ROC) curve. The results indicate that the integrated models have given outputs with good forecasting ability. They are also very useful in landuse planning as well as the prevention and mitigation of risks due to landslides and debris flows in the research area and other similar mountainous areas.