FEDERATED DEEP LEARNING FRAMEWORK FOR REAL-TIME FLOOD PREDICTION AND EARLY WARNING SYSTEMS

Abstract

Flood forecasting is essential for protecting lives, infrastructure, and enabling timely disaster response. However, conventional centralized machine learning models struggle with data privacy, communication overhead, and limited access to distributed hydrological datasets. To overcome these challenges, this research proposes a Federated Deep Learning Framework for Real-Time Flood Prediction and Early Warning Systems, integrating advanced hydrological modeling with privacy-preserving federated learning techniques.Machine learning and deep learning methods have already demonstrated high potential for flood forecasting and detection [1], [7], [10], [19]. Time-series models such as LSTM provide strong capabilities for hydrological prediction [6], [11], while deep architectures like CNNs and ResNet enhance spatial feature extraction from remote sensing data [8], [18]. Federated learning enables decentralized model training without sharing raw data, ensuring privacy and security [3], [5], [12], [13], [20]. Further, optimization techniques like Adam and communication-efficient distributed learning approaches significantly improve model performance and scalability [15], [17], [23].The proposed framework combines rainfall data, river discharge, remote sensing imagery, and IoT-based flood monitoring inputs [21] to provide accurate and real-time flood prediction. Privacy-preserving federated aggregation prevents data leakage risks [16], while enabling collaborative learning across multiple geographically distributed nodes. Experimental evaluations show that federated deep learning improves accuracy, robustness, and scalability compared to centralized approaches, advancing toward a secure and intelligent flood forecasting ecosystem suitable for climate-sensitive regions.