Simple Search:

Neural networks for forecasting daily reservoir inflows


Citation

Karimi-Googhari, Shahram and Huang, Yuk Feng and Ghazali, Abdul Halim and Lee, Teang Shui (2010) Neural networks for forecasting daily reservoir inflows. Pertanika Journal of Science & Technology, 18 (1). pp. 33-41. ISSN 0128-7680; ESSN: 2231-8526

Abstract / Synopsis

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.


Download File

[img]
Preview
PDF
neural.pdf

Download (2MB)

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
Publisher: Universiti Putra Malaysia Press
Keywords: Reservoir inflow; Neural network; Forecasting; Modelling
Depositing User: Anas Yahaya
Date Deposited: 21 Feb 2011 18:35
Last Modified: 05 Jan 2016 12:43
URI: http://psasir.upm.edu.my/id/eprint/9612
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item