Reservoir water balance simulation model utilizing machine learning algorithm

Abstract

Developing water losses and reservoir final storage forecast has become an increasingly important task for reservoir operation. Accurate forecasts would lead to better monitoring of water quality and more efficient reservoir operation. Therefore, the flash flood and water crisis problems in Malaysia can be reduced. Artificial neural networks (ANN) models with radial basis function (RBF) have been determined for high efficiency and accuracy, especially in the dynamics system. In this study, the proposed ANN Prediction Model is being developed by using inflow, the release of dam, initial and final storage of the reservoir as input, whereas the water losses from the reservoir as output. All the data collected over 11 years (1997–2007) at Klang Gate reservoir has been used to develop and test model output. The results indicated that the proposed model could provide monthly forecasting with maximum root mean square error of ± 20.07%. The advantages of this ANN model are to provide information for water losses, final storage, and variation of water level for better reservoir operation.