Reservoir Inflow Prediction using Machine Learning Techniques

Accurate forecasting of reservoir inflow is crucial for effective water management, especially in regions with limited water resources and high demand from various sectors, including irrigation, domestic, and industrial uses. For the effective planning and management of reservoir operations, flood control, hydroelectric power generation, and drought mitigation, predicting reservoir inflow plays a crucial role. With the rapid increase in population and industrialization, uncertainty in reservoir storage has increased, leading to a risk of water stress and compromised water security. Therefore, predicting reservoir inflow is crucial for reservoir operation and efficient water management. The inflow prediction is challenging due to the complex and dynamic nature of the rainfall-runoff process in a river basin. Hydrological models provide a simplified representation of real hydrological systems; despite this, due to the complexities and uncertainties in hydrological processes, it is challenging to achieve accurate predictions. In recent years, machine learning (ML) techniques have been widely used for simulating the streamflow due to their accuracy in capturing complex and non-stationary relationships between rainfall and streamflow. However, these ML models do not account for the physical characteristics of the watershed. Therefore, to increase the accuracy of prediction by gaining a better understanding of the hydrological patterns, physics-based, hybrid machine learning models have been developed in this study and applied in a river basin of Maharashtra, India. A physics-based HEC-HMC model was combined with ML models, such as long short-term memory (LSTM) and extreme gradient boosting (XGBoost), to develop a hybrid ML model using 2001 to 2021 hydro-meteorological data. The hybrid ML model was found to be capable of predicting the inflow (QIF) into the reservoir. The daily values of hydro-meteorological variables, viz., rainfall, temperature, relative humidity, wind speed, and reservoir inflow, were used to simulate the HEC-HMC model. The HEC-HMS simulated reservoir inflow (Qh), along with its lagged values (Qh-1, Qh-2), reservoir storage, rainfall, evaporation loss, and other factors, were used as inputs to the machine learning models. The preliminary results indicated that Qh, Qh-1 and lag-1 rainfall variables are essential inputs to machine learning models for the accurate prediction of the reservoir inflow.