Flood Risk Prediction using Machine Learning: A Framework for Data-Driven Disaster Management

Floods are one of the most common and damaging natural disasters in the world. They kill a lot of people, damage infrastructure, hurt the economy, and harm the environment. Traditional hydrological and hydraulic models, while scientifically robust, frequently necessitate high-quality data, extensive computation, and localized calibration, thereby constraining their real-time applicability and transferability.This study introduces the Flood Risk Intelligence and Forecasting (FRIF) Framework, which combines machine learning (ML), geospatial analysis (GIS), and IoT-enabled real-time monitoring to predict and manage flood risk. The framework uses ensemble and deep learning methods to make accurate, understandable, and useful flood risk predictions by combining meteorological, hydrological, geospatial, and socio-economic datasets.The effectiveness of the framework is demonstrated by a case study of the Godavari River Basin in India, where Random Forest and XGBoost correctly predict over 85% of the data. Spatial flood risk maps are provided by GIS dashboards, and dynamic updates and ongoing monitoring are made possible by IoT integration. By providing disaster management authorities with actionable intelligence, the FRIF Framework enhances community resilience, resource allocation, and preparedness.