Runoff Forecasting and Flood Risk Classification in Sunamganj: A Comparative Analysis of Statistical, Machine Learning, and Deep Learning Models

This study has developed an near real-time runoff forecasting and flood risk classification framework for Sunamganj. Statistical, Machine Learning and Deep Learning models such as Persistence, Linear Regression, Random Forest, XGBoost, LSTM and GRU have been used to predict 1–24 hours of runoff and compare models. Hydrological patterns have been analyzed using time, lag and rainfall-runoff related features. In addition, three flood risk classes—Normal, Moderate and High—have been created based on runoff and the classification performance has been evaluated. This study provides an effective flood early warning approach for flood-prone areas of Bangladesh.