Privacy-Aware Federated Deep Learning Framework for Intelligent Flood Forecasting and Water Level Prediction

Abstract

Floods are among the most destructive natural disasters, causing severe damage to human lives, infrastructure, agriculture, and the economy. Accurate and timely flood forecasting is essential for effective disaster preparedness and mitigation. This paper presents a PrivacyAware Federated Deep Learning Framework for Intelligent Flood Forecasting and Water Level Prediction, which combines Federated Learning (FL) with a Feedforward Neural Network (FFNN) to deliver secure and accurate predictions without sharing raw data among participating stations. The proposed framework enables multiple regional nodes to train local models independently while transmitting only model parameters to a central server for global aggregation, thereby preserving data privacy and reducing communication overhead. The aggregated model identifies flood-prone regions and predicts future water levels using hydrological and meteorological parameters. Experimental evaluation demonstrates that the proposed framework achieves high prediction accuracy with low prediction error while ensuring secure, decentralized learning. The proposed system provides a reliable and scalable solution for intelligent flood forecasting and early warning applications.