Ensemble deep learning framework for landslide susceptibility mapping and road vulnerability index development in the Chittagong Hill Tracts, Bangladesh

Landslides pose a persistent threat to life, livelihoods, and critical infrastructure in the Chittagong Hill Tracts (CHT) of Bangladesh, a region marked by steep slopes, intense monsoonal rainfall, and increasing anthropogenic pressure. This study presents a comprehensive landslide susceptibility assessment for the monsoon-dominated Chittagong Hill Tracts (CHT), addressing data scarcity challenges. An ensemble model integrating Convolutional Neural Networks, Deep Neural Networks, and Long Short-Term Memory networks was developed using ten standardized 30-meter resolution geo-environmental variables. A balanced dataset of 5,082 samples was generated through spatially stratified random sampling. Spatial-block 10-fold cross-validation demonstrated strong model performance with an F1-score of 0.8923 and an AUC of 0.9558, outperforming individual models. Key drivers identified include annual rainfall and proximity to fault lines, highlighting…