Improving landslide susceptibility mapping in semi-arid regions using machine learning and geospatial techniques
Youssef Bammou, Brahim Benzougagh, Abdessalam Ouallali, Shuraik Kader, Mustapha Raougua, Brahim Igmoullan
Abstract
This research examines landslide susceptibility evaluation in the Tensift sub-catchment of Morocco. Despite the established nature of this research topic, this study employs machine learning (ML) models for landslide susceptibility assessment, acknowledging its distinctive traits and contributing elements. Landslide susceptibility was estimated using seven ML models (KNN, SVM, RF, XGBoost, ANN, LR, and DT), and their results were juxtaposed to identify the most suitable ML model for this application. The research combines quantitative and qualitative spatial data to map landslide susceptibility. Using different types of spatial data, such as slope, elevation, precipitation, and land use, in conjunction with ML models shows a comprehensive approach to the problem. A complete tolerance (TOL) and variance inflation factor (VIF) analysis was performed to select the conditioning factors to choose the most relevant features or factors that improve the accuracy of the landslide susceptibility model. The data was combined with a geographic database compiled from historical records of 1291 landslide areas in the region. The dataset was randomly split into a training set (70%) and a validation set (30%). The evaluation of models involved statistical indices and the ROC curve method. allowing a robust assessment. The XGBoost model was identified as the best-performing model with a high area under the curve (AUC) of 93.41%, closely followed by RF and KNN with AUC values of 91.09%. In addition, the root mean square error (RMSE) values were relatively low, ranging from a minimum of 0.257 for XGBoost to a maximum of 0.53 for ANN. The specific values obtained for AUC and RMSE offer significant insight into the efficacy of each model. The results of this study suggest that XGBoost is particularly effective for modeling landslide susceptibility in the Tensift sub-catchment, which may have implications for future research in similar semi-arid regions.