An Integrated Deep Learning Framework for Multi-Hazard Risk Assessment: Flood Prediction and Landslide Detection

Abstract

Floods and landslides are among the most destructive natural disasters, causing severe environmental and socio-economic damage across vulnerable regions worldwide. Accurate and real-time prediction of such disasters is essential for effective disaster management, risk mitigation, and early warning systems. This paper proposes a multi-modal prediction framework that integrates environmental data and satellite imagery using machine learning and deep learning techniques to improve the accuracy and reliability of disaster prediction. The proposed system consists of two parallel prediction models: an environmental data-based model using XGBoost and Random Forest algorithms, and a satellite image-based model using Convolutional Neural Networks (EfficientNet). The environmental model processes critical features such as rainfall, humidity, soil moisture, temperature, elevation, and slope to identify disaster-prone conditions. Simultaneously, the image-based model analyzes satellite imagery using the Normalized Difference Water Index (NDWI) to detect water bodies and assess flood-related patterns. A fusion layer integrates the predictions generated by all models to produce a unified multi-hazard risk score categorized into four risk levels: Low, Moderate, High, and Critical. Experimental evaluation demonstrates that the proposed multi-modal fusion model significantly improves prediction accuracy and overall performance when compared to individual prediction models. The framework offers a scalable, robust, and interpretable solution for real-time disaster monitoring, risk assessment, and intelligent early warning systems, thereby supporting proactive disaster preparedness and emergency response management.