Inflow forecasting using Artificial Neural Networks for reservoir operation
In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-05-01
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| Series: | Proceedings of the International Association of Hydrological Sciences |
| Online Access: | https://www.proc-iahs.net/373/209/2016/piahs-373-209-2016.pdf |
| _version_ | 1811326444388745216 |
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| author | C. Chiamsathit A. J. Adeloye S. Bankaru-Swamy |
| author_facet | C. Chiamsathit A. J. Adeloye S. Bankaru-Swamy |
| author_sort | C. Chiamsathit |
| collection | DOAJ |
| description | In this study, multi-layer perceptron (MLP) artificial neural networks have
been applied to forecast one-month-ahead inflow for the Ubonratana reservoir,
Thailand. To assess how well the forecast inflows have performed in the
operation of the reservoir, simulations were carried out guided by the
systems rule curves. As basis of comparison, four inflow situations were
considered: (1) inflow known and assumed to be the historic (Type A);
(2) inflow known and assumed to be the forecast (Type F); (3) inflow known
and assumed to be the historic mean for month (Type M); and (4) inflow is
unknown with release decision only conditioned on the starting reservoir
storage (Type N). Reservoir performance was summarised in terms of
reliability, resilience, vulnerability and sustainability. It was found that
Type F inflow situation produced the best performance while Type N was the
worst performing. This clearly demonstrates the importance of good inflow
information for effective reservoir operation. |
| first_indexed | 2024-04-13T14:50:26Z |
| format | Article |
| id | doaj.art-17e18a0237d54e8fba70e48a69b8e2d5 |
| institution | Directory Open Access Journal |
| issn | 2199-8981 2199-899X |
| language | English |
| last_indexed | 2024-04-13T14:50:26Z |
| publishDate | 2016-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Proceedings of the International Association of Hydrological Sciences |
| spelling | doaj.art-17e18a0237d54e8fba70e48a69b8e2d52022-12-22T02:42:37ZengCopernicus PublicationsProceedings of the International Association of Hydrological Sciences2199-89812199-899X2016-05-0137320921410.5194/piahs-373-209-2016Inflow forecasting using Artificial Neural Networks for reservoir operationC. Chiamsathit0A. J. Adeloye1S. Bankaru-Swamy2Institute for Infrastructure and Environment, Heriot-Watt University, Edinburgh, EH14 4AS, UKInstitute for Infrastructure and Environment, Heriot-Watt University, Edinburgh, EH14 4AS, UKInstitute for Infrastructure and Environment, Heriot-Watt University, Edinburgh, EH14 4AS, UKIn this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.https://www.proc-iahs.net/373/209/2016/piahs-373-209-2016.pdf |
| spellingShingle | C. Chiamsathit A. J. Adeloye S. Bankaru-Swamy Inflow forecasting using Artificial Neural Networks for reservoir operation Proceedings of the International Association of Hydrological Sciences |
| title | Inflow forecasting using Artificial Neural Networks for reservoir operation |
| title_full | Inflow forecasting using Artificial Neural Networks for reservoir operation |
| title_fullStr | Inflow forecasting using Artificial Neural Networks for reservoir operation |
| title_full_unstemmed | Inflow forecasting using Artificial Neural Networks for reservoir operation |
| title_short | Inflow forecasting using Artificial Neural Networks for reservoir operation |
| title_sort | inflow forecasting using artificial neural networks for reservoir operation |
| url | https://www.proc-iahs.net/373/209/2016/piahs-373-209-2016.pdf |
| work_keys_str_mv | AT cchiamsathit inflowforecastingusingartificialneuralnetworksforreservoiroperation AT ajadeloye inflowforecastingusingartificialneuralnetworksforreservoiroperation AT sbankaruswamy inflowforecastingusingartificialneuralnetworksforreservoiroperation |