Five Days Lead Time Water Level Forecasting in the Ganges–Padma River Using Satellite and Reanalysis Data with Machine Learning

Bangladesh is disaster-prone due to its location, heavy monsoon rainfall, and frequent cyclones, with floods causing major loss of life and property. Accurate and timely flood forecasting and warning systems are essential to reduce flood-related damage and human suffering. Currently, the national flood forecasting system provides reasonably accurate predictions only for short lead times of up to three days (FFWC, 2021). Improving medium to long range flood forecasting with lead times of 5–10 days is therefore critical for enhanced flood preparedness.

This study investigates artificial intelligence-based approaches for medium-range river flow forecasting with a five-day lead time at Hardinge Bridge station in the Ganges basin. Multiple input variables, including precipitation, precipitable water, soil moisture storage, and satellite-derived river water levels, were used. Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM) algorithms were applied to simulate river water level as an alternative to traditional hydrologic model-based forecasting. Predictions were evaluated against the Bangladesh Water Development Board’s Flood Forecasting and Warning Centre (FFWC).

For each algorithm, 70% of data were used for training, 15% for testing, and 15% for independent validation. Various input combinations, or model scenarios, were examined. The scenario including all variables performed best. Among algorithms, Random Forest showed superior performance, with RMSE of 0.28 m, a coefficient of determination (R²) of 0.99, and a Nash Sutcliffe Efficiency (NSE) of 0.99. Upon evaluating the R² value by comparison in a percentage scale, it was observed that best RF model of scenario-01 demonstrated an improvement of approximately 38% over FFWC's Prediction of water level.

This research establishes that the machine learning algorithms, particularly RF, offers a promising alternative to traditional flood forecasting methods, with significant accuracy in predicting water level at Hardinge bridge station in the Ganges basin.  Its capacity to use satellite-derived data improves flood forecasting and leads to more reliable predictions, potentially improving flood preparedness and risk management in Bangladesh.