FFM: Flood Forecasting Model Using Federated Learning

Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is advanced machine (ML) guarantees data privacy, ensures availability, promises security, handles network latency trials inherent in by prohibiting be transferred over training. urges onsite training local models, focuses on transmission these models instead sending set towards central server aggregation global at server. proposed integrates locally trained eighteen clients, investigates which station flooding about happen generates alert specific client with five days lead A feed forward neural (FFNN) where expected. module FFNN predicts expected water level taking multiple regional parameters as input. The dataset different rivers barrages collected from 2015 2021 considering four aspects including snow melting, rainfall-runoff, flow routing hydrodynamics. successfully predicted previous happened selected zone during 2010 84 % accuracy.