MONTHLY RESERVOIR INFLOW FORECASTING IN THAILAND: A COMPARISON OF ANN-BASED AND HISTORICAL ANALOUGE-BASED METHODS

Abstract

 Accurate forecasting of reservoir inflow is essential for effective reservoir management. In this study, artificial neural network (ANN)-based models and forecasting methods based on historical inflow analogues were used to forecast the monthly reservoir inflows of Sirikit Dam in the Nan River Basin of Thailand. Incorporating sea surface temperatures and ocean indices in the ANN model significantly improved the forecasting result. The wavelet decomposition of inputs before they were fed into the ANN model also improved the forecasting result. The variation analogue forecast produced the best result among the forecasting methods investigated, based on historical analogues. It was also superior to other forecasting methods when forecasting extreme inflow values.