Abstract
Reservoir inflow forecasting is crucial for appropriate reservoir management, especially in the flood season. Forecasting for this season must be sufficiently accurate and timely to allow dam managers to release water gradually for flood control in downstream areas. Recently, several models and methodologies have been developed and applied for inflow forecasting, with good results. Nevertheless, most were reported to have weaknesses in capturing the peak flow, especially rare extreme flows. In this study, an analogue-based forecasting method, designated the variation analogue method (VAM), was developed to overcome this weakness. This method, the wavelet artificial neural network (WANN) model, and the weighted mean analogue method (WMAM) were used to forecast the monthly reservoir inflow of the Sirikit Dam, located in the Nan River Basin, one of the eight sub-basins of the Chao Phraya River Basin in Thailand. It is one of four major dams in the Chao Phraya Basin, with a maximum storage of 10.64 km3, which supplies water to 22 provinces in this basin, covering an irrigation area of 1,513,465 hectares. Due to the huge extreme monthly inflow in August, with inflow of more than 3 km3 in 1985 and 2011, monthly or longer lead time inflow forecasting is needed for proper water and flood control management of this dam. The results of forecasting indicate that the WANN model provided good forecasting for whole-year forecasting including both low-flow and high-flow patterns, while the WMAM model provided only satisfactory results. The VAM showed the best forecasting performance and captured the extreme inflow of the Sirikit Dam well. For the high-flow period (July–September), the WANN model provided only satisfactory results, while those of the WMAM were markedly poorer than for the whole year. The VAM showed the best capture of flow in this period, especially for extreme flow conditions that the WANN and WMAM models could not capture.
Highlights
Reservoirs are manmade structures that are widely used in water resource management, and are recognized as some of the most efficient infrastructure components in integrated water resource management and development [1]
The forecasting using the wavelet artificial neural network (WANN) model in this study was begun by finding the input parameters of the artificial neural network (ANN) model that produced the best forecast
After obtaining the best forecast from the ANN model, all input parameters were decomposed into their detailed and approximated components
Summary
Reservoirs are manmade structures that are widely used in water resource management, and are recognized as some of the most efficient infrastructure components in integrated water resource management and development [1]. A similar study which indicated the successful integration of the ANN and wavelet analysis to predict water levels in the Nan River, Thailand, can be found in the work of Amnatsan et al [23] Another technique that has been widely used in forecasting is the analogue method (AM), which was first introduced by Lorenz in 1969 to predict the evolution of the states of a dynamic system [24]. The WMAM, which was found to show good predictive performance for a low-response watershed [31], may not be able to forecast the peak flow for the high-response catchment of the Sirikit Dam. Several previous studies have indicated that SSTs and ocean indices are associated with the seasonal and interannual climate of Thailand [36,37,38,39], and the variability of rainfall and reservoir inflows may be associated with SST anomalies. Their forecasting performance was compared using four indicators: the root mean square error (RMSE), the correlation (R), the Nash–Sutcliffe efficiency index (EI), and the coefficient of determination (CD)
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