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F.LO.O.D.S.A.F.E: Flood Localization and Observation for Optimal Detection and Strategic Alert Forecasting Engine

Akhil Praveen, Amalraj P, Ashlin Babu, Neeraj R, Anison Abraham

Abstract


Floods are among the most devastating natural disasters, causing economic and environmental damage. This paper presents a machine-learning-based flood prediction and alerting system that integrates deep learning techniques with satellite and weather data. The system utilizes convolutional neural networks (CNNs) for image classification and U-Net architectures for precise flood mapping. Random forests and gradient boosting machines analyse real-time weather data to predict flood risks. Additionally, a dynamic evacuation route planning system provides optimized routes based on real-time conditions. The integration of cloud platforms ensures scalability and real-time response, making F.L.O.O.D.S.A.F.E a viable solution for flood-prone regions.


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References


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