Flood Monitoring Using Machine Learning
Bysani Rajdeep, Bobbala Anvitha · 2025
Floods are among the most destructive natural disasters, causing significant damage to infrastructure, loss of lives, and economic disruption. Effective flood monitoring and prediction are essential for mitigating these impacts. This study presents a comprehensive software-based approach to flood monitoring using machine learning techniques, integrating multi-source data such as satellite imagery, hydrological data, and weather patterns. The system employs the Random Forest algorithm for flood risk classification, leveraging its robust feature handling and high accuracy. By By evaluating both past and current data, the system makes accurate flood predictions and risk assessment. The proposed framework provides actionable insights for disaster preparedness and response while avoiding the need for hardware integration such as IoT devices. This research demonstrates the potential of combining geospatial data and advanced machine learning techniques to enhance flood risk assessment and environmental monitoring.