Flood Forecasting through Spatiotemporal Rainfall in Hilly Watersheds

Flood prediction in hilly regions, characterized by rapid flow rates and high destructive potential, remains a significant challenge. This study addresses this problem by introducing a novel machine learning-based approach to enhance flood forecast accuracy and lead time in small watersheds within hilly terrain. The study area encompasses small watersheds of approximately 600 km2. The proposed method analyzes spatiotemporal characteristics in rainfall dynamics to identify historical rainfall-flood events that closely resemble current patterns, effectively "learning from the past to predict the present". The approach demonstrates notable precision, with an average error of 8.33% for peak flow prediction, 14.27% for total volume prediction, and a lead time error of just 1 h for peak occurrence. These results meet the stringent accuracy requirements for flood forecasting, offering a targeted and effective solution for flood forecasting in challenging hilly terrains. This innovative methodology deviates from conventional techniques by adopting a holistic view of rainfall trends, representing a significant advancement in addressing the complexities of flood prediction in these regions.