Abstract

Landslides represent one of the main geological hazards, especially in Chile. The main purpose of this study is to evaluate the application of machine learning algorithms (SVM, RF, XGBoost and logistic regression) and compare the results for the modeling of landslide susceptibility in the province of Chanaral, III region, Chile. A total of 86 sites are identified using various sources, in addition to 86 non-landslide sites. This spatial data management and analysis are conducted using QGIS software. The sites are randomly divided, and then a cross-validation process is applied to calculate the accuracy of the models. After that, from 22 conditioning factors, 12 are chosen based on the information gain ratio (IGR). Subsequently, five factors are excluded by the correlation criterion. After this analysis, two indices not previously utilized in the literature, the NDGI (normalized difference glacier index) and EVI (enhanced vegetation index), are employed for the final model. The performance of the models is evaluated through the area under the ROC (receiver operating characteristic) curve (AUC). To study the statistical behavior of the model, the Friedman nonparametric test is performed to compare the performance with the other algorithms and the Nemenyi test for pairwise comparison. Of the algorithms used, RF (AUC = 0.957) and XGBoost (AUC = 0.955) have the highest accuracy values measured in AUC compared to the other models and can be used for the same purpose in other geographic areas with similar characteristics. The findings of this investigation have the potential to assist in land use planning, landslide risk reduction, and informed decision making in the surrounding zones.