Advance flood alert prediction using deep learning
Year: 2025
Type: article
Abstract: One of the most destructive natural disasters is flooding, which frequently causes a large number of fatalities, property damage, and infrastructure disruption. For disaster management and mitigation strategies to be effective, flood prognostications must be made accurately and on time. This study offers a robust flood alert prediction system that improves forecasting accuracy and response time by applying deep learning techniques. The proposed system integrates various environmental parameters, including rainfall intensity, river water levels, soil moisture, and satellite imagery, to train deep neural network models capable of recognizing complex patterns associated with flood events. According to evaluation results, the deep learning-based model provides a dependable tool for early warning systems by outperforming traditional statistical techniques in terms of prediction. By giving communities and authorities accurate, real-time alerts that facilitate proactive evacuation and resource allocation, this research confirms how deep learning has the potential to revolutionize flood risk management and lessen the impact of flood disasters.
Authors: Dr. Balaji Kannan, Syed Harun S
Institution: Vels University
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W6930687852
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