Reservoir inflow forecasting using time series and neural network models

In practice, the reservoir net inflow is computed based upon the application of the water balance equation to the reservoir system since it is difficult to obtain direct and reliable measurements of this variable. The net inflow process has been thus found to possess a random behaviour because it is related to the stochastic nature of various physical processes involved in the water balance computation (e.g., precipitation, evaporation, etc.). Therefore, the aim of this research is to propose a forecasting method that can accurately and efficiently predict the random reservoir inflow series. The proposed forecasting methods considered were the linear regression, the exponential smoothing technique, the periodic autoregressive moving average (PARMA) method, and the neural network procedure. An illustrative application was carried out using 25 years (1970--1994) of monthly rainfall and inflow data from the Pedu-Muda reservoirs in Kedah, Malaysia. The first 18 years (1970--1987) were used for calibration while the remaining 7 years (1988--1994) were used for verification of the proposed models. (Abstract shortened by UMI.