Advanced Computational Modeling for Predicting Landslide Susceptibility in Hilly Terrains

Abstract

Landslides pose significant risks in hilly terrains, necessitating accurate predictive models for effective risk management. This study explores advanced computational modeling techniques for predicting landslide susceptibility in such regions. Traditional models often fall short in capturing the complexity of landslide dynamics due to their reliance on simplistic approaches. To address this, we developed and validated several advanced models, including neural networks, support vector machines (SVM), and ensemble methods. Data was collected from diverse sources, including geospatial, topographic, soil, and meteorological datasets, and preprocessed for model training. Performance evaluation metrics such as accuracy, precision, recall, and the area under the curve (AUC) were employed. Results indicate that the ensemble method, combining predictions from multiple models, achieved the highest accuracy at 90%, surpassing individual models like neural networks and SVM. Case studies further validated the effectiveness of these models in identifying high-risk areas. This research demonstrates that advanced computational approaches significantly improve landslide susceptibility predictions compared to traditional methods. The findings suggest that integrating multiple data sources and modeling techniques can enhance landslide risk management and inform disaster preparedness strategies.