Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan

The Himalayan region, a rugged mountain zone is among the most susceptible zones to the landslide hazard due to its terrain, geography, and active tectonics. Machine learning (ML) techniques are most advanced and precise methods to develop landslide susceptibility model (LSM). The current study was designed to analyze and assess the landslide susceptibility using ML approaches for District Hattian Bala, NW Himalayas, Pakistan. The historical satellite imageries are used to generate spatiotemporal landslide inventories of year 2005, 2007 and 2012. A spatial database was created pertaining to topographic, environmental, geologic, and anthropogenic factors including slope, aspect, elevation, curvature, plane curvature, profile curvature, topographic wetness index (TWI), lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI) and land use/ land cover (LULC). These LCFs were selected to analyze periodic landslide susceptibility in the region. The experimental design utilized 349, 393, and 735 landslide inventory of 2005, 2007, and 2012 respectively. Two ML models, i.e., Random Forest (RF) and Logistic Regression (LR) were applied to assess landslide susceptibility determine by thirteen landslide causative factors (LCFs). The spatiotemporal landslide inventory was partitioned into training (70%) and testing (30%) landslides for respective years to check the prediction accuracies of selected ML models. Comparative analysis of different LSMs was performed by the Receiver Operator Curves - Area Under Curves (ROC-AUC). The resultant accuracy, MAE, RMSE, Kappa, Precision, Recall, F1 indicated that RF outperformed the LR model. The study aims to minimize losses to lives and potential economic damage linked with recurrent slope instabilities in the region. It is anticipated that use of ML algorithms would support concerned authorities and organizations to effectively plan and manage landslide hazard in the region.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).