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

Viewed

Total article views: 1,845 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,061 686 98 1,845 141 144
  • HTML: 1,061
  • PDF: 686
  • XML: 98
  • Total: 1,845
  • BibTeX: 141
  • EndNote: 144
Views and downloads (calculated since 12 May 2016)
Cumulative views and downloads (calculated since 12 May 2016)

Cited

Latest update: 15 Nov 2024
Download
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.