NEURAL NETWORK APPLICATION IN RESERVOIR WATER LEVEL FORECASTING AND RELEASE DECISION

Reservoir dam is one of the defense mechanism for both flood and drought disasters. During flood, the opening of the dama€ s spillway gate must be adequate to ensure that the reservoir capacity will not over its limits and the discharges will not cause overflow downstream. While, during drought the reservoir needs to impound water and release adequately to fulfill its purposes. Modelling of the reservoir water release is vital to support the reservoir operator to make fast and accurate decision when dealing with both disasters. In this paper, intelligent decision support model based on neural network (NN) is proposed. The proposed model consists of situation assessment, forecasting and decision models. Situation assessment utilized temporal data mining technique to extract relevant data and attribute from the reservoir operation record. The forecasting model utilize NN to perform forecasting of the reservoir water level, while in the decision model, NN is applied to perform classification of the current and changes of reservoir water level. The simulations have shown that the performances of NN for both forecasting and decision models are acceptably good.