Neural Network Application in the Change of Reservoir Water Level Stage Forecasting

Artificial Neural Network is one of the computational algorithms that can be applied in developing a forecasting model for the change of reservoir water level stage. Forecasting of the change of reservoir water level stage is vital as the change of the reservoir water level can affect the reservoir operator’s decision. The decision of water release is very critical in both flood and drought seasons where the reservoir should maintain high volume of water during less rainfall and enough space for incoming heavy rainfall. The changes of reservoir water level which provides insights on the increase or decrease water level that affects water level stage. In this study, neural network model for forecasting the change of reservoir water level stage is studied. Six neural network models based on standard back propagation algorithm have been developed and tested. Sliding windows have been used to segment the data into various ranges. The finding shows that 2 days of delay have affected the change in stage of the reservoir water level. The finding was achieved through 4-17-1 neural network architecture.