FFM: Federated Learning Flood Forecasting Model

Abstract

Floods are one of the most common and severe natural catastrophes, inflicting death, infrastructural damage, agricultural disruption, and environmental degradation. Predicting flood risk accurately and quickly is critical for disaster planning, early warning, and resource management. Machine learning or statistical models that aggregate enormous amounts of hydrological and meteorological data from various locations into a single server are used in traditional flood prediction. Such centralized solutions create major data privacy, security, ownership, and scalability challenges, especially for geographically scattered datasets managed by diverse administrative agencies. This study presents a Federated Learning–based Deep Learning framework for flood-risk prediction utilizing Indian multi-state hydrological time-series data to overcome these constraints. Long Short-Term Memory (LSTM) neural networks are ideal for simulating sequential and temporal relationships in environmental data including rainfall, temperature, and multi-layer soil moisture levels. Each state acts as an independent client node that trains a local LSTM model using its own history data in the federated learning technique. Only learnt model parameters are shared with a central server, where the Federated Averaging (FedAvg) method aggregates them to enhance global prediction. This collaborative training maintains data security while using regional knowledge. The method divides flood conditions into three danger levels—Low, Medium, and High—based on streamflow quantile thresholds, turning a continuous prediction issue into a decision-making framework. After federated training, the global model can generate monthly flood-risk probabilities for a state and graphically illustrate streamflow patterns and risk distributions. Visual outputs aid authorities in seasonal flood behavior interpretation.Privacypreserving collaborative learning, scalability across geographically scattered regions, effective use of time-series environmental data, and increased predictive intelligence without centralized data storage are among the benefits of the suggested technology. The prototype implementation shows the promise of federated deep learning for environmental monitoring, early flood warning systems, and catastrophe risk reduction planning. Real-time sensor integration, sophisticated forecasting models, and implementation inside government disaster management frameworks might improve water resource management and community flood resilience.