Articles | Volume 373
https://doi.org/10.5194/piahs-373-209-2016
https://doi.org/10.5194/piahs-373-209-2016
12 May 2016
 | 12 May 2016

Inflow forecasting using Artificial Neural Networks for reservoir operation

Chuthamat Chiamsathit, Adebayo J. Adeloye, and Soundharajan Bankaru-Swamy

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Cited articles

Adeloye, A. J. and De Munari, A.: Artificial neural network based generalized storage-yield-reliability models using Levenberg-Marquardt algorithm, J. Hydrol., 362, 215–230, 2006.
Chiamsathit, C., Adeloye, A. J., and Soundharajan, B.: Assessing competing policies at Ubonratana reservoir, Thailand, Proceedings ICE (Water Management), 167(WM10), 551–560, 2014.
Edossa, D. C. and Babel, M. S.: Forecasting Hydrological Droughts Using Artificial Neural Network Modeling Technique, South Africa: University of Pretoria, Proceedings of 16th SANCIAHS National Hydrology Symposium, 1–3 October 2012, Pretoria, 2012.
EGAT: Improved Rule Curve, Procedure of the Ubonratana reservoir operation: Electricity Generating Authority of Thailand (EGAT) in the Ubonratana dam, EGAT, Khon Kaen, Thailand, 2002.
McMahon, T. A. and Adeloye, A. J.: Water resources yield, Water Resources Publications, LLC, Colorado, USA, 2005.
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Short summary
In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. This is necessary because without knowing the expected inflow, one would not know the amount of water to allocate at the start of each month. As expected, knowing the inflow through our forecasts significantly improved the performance of the Ubonratana reservoir, the test case. We expect the study to have utility for other systems.