e-ISSN 2231-8542
ISSN 1511-3701
Shahram Karimi-Googhari, Huang Yuk Feng, Abdul Halim B Ghazali and Lee Teang Shui
Pertanika Journal of Tropical Agricultural Science, Volume 18, Issue 1, January 2010
Keywords: Reservoir inflow, neural network, forecasting, modelling
Published on:
Proper integrated management of a dam reservoir requires that all components of the water resource system be known. One of these components is the daily reservoir inflow which is the subject matter of this study, i.e. to establish predictions of what is coming in the next rainfall-runoff process over a catchment. The transformation of rainfall into runoff is an extremely complex, dynamic, and more of a non-linear process. The available six-year average daily rainfall data across the Sembrong dam catchment were computed using the well-known Theissen's polygon method. Daily reservoir inflow data were extracted by applying the water balance model to the Sembrong dam reservoir. Modelling of relationship between rainfall and reservoir inflow data was done using feed-forward back-propagation neural networks. The final selected model has one hidden layer with 11 neurons in the hidden layer. The selected model was applied for an independent data series testing. Results in relation to specific climatic and hydrologic properties of a small tropical catchment suggested that the model is suitable to be used in forecasting the next day's reservoir inflow. The efficiencies of the model Abtained indicated the validity of using the neural network for modelling reservoir inflow series.
ISSN 1511-3701
e-ISSN 2231-8542