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 km 3 , 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 km 3 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. operation. In reservoir operation, care is required, especially for multipurpose reservoirs where there may be a number of potentially conflicting objectives. For water supply, operations should keep reservoirs as full as possible, whereas flood control requires reservoirs to be kept as empty as possible to allow the capture of flood water [3]. Reservoirs should be neither partially empty at the end of the rainy season nor full at the time of a series of peak floods that lead to heavy releases, causing floods in downstream areas [4]. Due to its complexity, reservoir operation is a challenging problem for water resource planners and managers. To optimize operating rules, many optimization and simulation models have been developed and applied over the past several decades [5][6][7][8][9]. However, these operating rules are not easy to implement, as appropriate reservoir operations depend on the accuracy of inflow forecasting and the operating time horizon [10]. Accurate inflow prediction is not only an important non-engineering measure to ensure flood-control safety and increase water resou...

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.