Landslide Susceptibility Prediction Using Deep Learning

Abstract

Landslides are catastrophic natural disasters, especially in mountainous regions, endangering lives, infrastructure, and wildlife. Moxi Town in China's Sichuan Province frequently suffers from severe landslides, often driven by geological, meteorological, and human factors. To mitigate such risks, accurate prediction models are critical. This chapter introduces a landslide prediction model using deep learning, specifically Convolutional Neural Networks (CNNs) and U-Net architectures, trained with Moxi Town's data. By integrating meteorological, geological, and environmental data, the model detects and assesses landslide risks with high accuracy. Additionally, advanced image processing via satellite imagery enhances disaster management. The framework, using CNNs and explainable AI, offers interpretability, aiding early-warning systems and real-time risk assessment. This comprehensive approach underscores the importance of advanced tech in improving resilience in landslide-prone regions, enhancing disaster preparedness and reducing risks in Moxi and similar areas.