Real-time streamflow forecasting in a reservoir-regulated river basin usingexplainable machine learning and conceptual reservoir module br

Real-time streamflow forecasting is essential to manage water resources effectively in a reservoir-regulated basin.However, forecasting becomes challenging without weather and upstream reservoir outflows forecasts in real-time.In this context, a novel hybrid approach is proposed in this study to forecast the streamflows and reservoir outflowsin real-time. In this approach, the Explainable Machine Learning model is embedded with a conceptual reservoir mod-ule for forecasting streamflows using short-term weather forecasts. Long Short Term Memory (LSTM), a MachineLearning model, is used in this study to predict the streamflow, and the model's explainability is examined by Shapleyadditive explanations method (SHAP). Panchet reservoir catchment, which contains Tenughat and Konar reservoirs, isselected as a study area. The LSTM model performance is excellent in predicting the streamflows of Tenughat, Konarand Panchet catchments with NSE values of 0.93, 0.87, and 0.96, respectively. The SHAP method identified the high-impact variables as streamflows and precipitation of 1-day lag. In forecasting, bias-corrected Global Forecast Systemdata is used with the LSTM model to forecast the streamflows in three catchments. The inflows are forecasted wellup to a 3-day lead in Tenughat and Konar reservoirs with NSE values above 0.88 and 0.87, respectively. The reservoirmoduleperformance in forecasting Tenughat and Konarreservoirs' outflows with the inflow forecasts is also promising up to a 3-day lead with NSE values above 0.88 for both reservoirs. The inflows forecasting to Panchet reservoir withreservoirs' outflows as additional inputs is excellent up to 5-day lead (NSE = 0.96-0.88). However, the forecastingerror increased from 77 m3/s to 134 m3/s with the lead time. This approach could provide an efficient way to reduceflood risks in the reservoir-regulated basin.