Enhanced Flood Forecasting: Revolutionizing Prediction with Federated Learning

This paper addresses the critical issue of flood prediction, a common and devastating natural disaster. Predicting floods has long been a challenging task, and this article introduces an innovative solution using federated learning, an advanced machine learning technique. Federated learning ensures data privacy, availability, and security, while also tackling network latency problems. Unlike traditional methods, it promotes onsite training of local data models, eliminating the need to transfer large datasets to a central server for aggregation. The new model combines smaller models from 18 stations to predict upcoming floods. It issues flood alerts with a five-day lead time to specific clients, enhancing preparedness and response. At each client station, a local (FFNN) model is trained to predict water levels based on regional parameters. This approach promises to revolutionize flood forecasting, improving our ability to mitigate flood-related damage and protect lives and property. Furthermore, to enhance accuracy we have used advanced CNN2D algorithm for flood forecasting.