In The Science of the total environment
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 outflows in real-time. In this approach, the Explainable Machine Learning model is embedded with a conceptual reservoir module for forecasting streamflows using short-term weather forecasts. Long Short Term Memory (LSTM), a Machine Learning model, is used in this study to predict the streamflow, and the model's explainability is examined by Shapley additive explanations method (SHAP). Panchet reservoir catchment, which contains Tenughat and Konar reservoirs, is selected as a study area. The LSTM model performance is excellent in predicting the streamflows of Tenughat, Konar and 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 System data is used with the LSTM model to forecast the streamflows in three catchments. The inflows are forecasted well up to a 3-day lead in Tenughat and Konar reservoirs with NSE values above 0.88 and 0.87, respectively. The reservoir module performance in forecasting Tenughat and Konar reservoirs' 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 with reservoirs' outflows as additional inputs is excellent up to 5-day lead (NSE = 0.96-0.88). However, the forecasting error increased from 77 m3/s to 134 m3/s with the lead time. This approach could provide an efficient way to reduce flood risks in the reservoir-regulated basin.
Sushanth Kallem, Mishra Ashok, Mukhopadhyay Parthasarathi, Singh Rajendra
2022-Dec-05
Explainable machine learning, Forecasting, LSTM, Reservoir module
