A Survey on Flood and Landslide Prediction using Machine Learning

Floods and landslides are highly destructive natural disasters, causing severe damage to lives, infrastructure, and economies. Their increasing frequency and intensity, driven by climate change, highlight the urgent need for advanced predictive systems to mitigate their impact. This survey examines the application of machine learning (ML) techniques for flood and landslide prediction, utilizing diverse data sources such as meteorological records, soil conditions, topography, remote sensing imagery, and historical incidents. Various ML models, including Random Forest (RF), Convo- lutional Neural Networks (CNNs), and Attention-UNet, are re- viewed for their effectiveness in risk assessment, spatial mapping, and prediction accuracy. Traditional models like RF provide robustness and simplicity, while advanced architectures like Attention-UNet excel in capturing complex spatial dependencies, making them ideal for high-resolution disaster mapping. Hybrid and ensemble models further enhance prediction reliability by overcoming the limitations of individual techniques. The integration of real-time sensor data and transfer learning improves model adaptability to dynamic and data-scarce envi- ronments. These systems offer actionable insights, empowering policymakers and emergency responders to optimize resource allocation, plan mitigation strategies, and enhance disaster pre- paredness. Moreover, ML applications in disaster management highlight the potential of interdisciplinary approaches, combining geospatial analysis, environmental science, and artificial intelli- gence. This survey underscores the transformative potential of ML in advancing flood and landslide prediction. By addressing challenges like data scarcity and computational complexity, it aims to support the development of more accurate, scalable, and efficient disaster management solutions essential for building resilient communities in an era of increasing environmental risks. Index Terms—Machine Learning, Disaster Prediction, Risk Assessment