Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model
artificial neural network; dam; integrated approach; model; reservoir; simulation; stochasticity; water relations; water resource
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2023
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my.uniten.dspace-244282023-05-29T15:23:27Z Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model Fayaed S.S. Fiyadh S.S. Khai W.J. Ahmed A.N. Afan H.A. Ibrahim R.K. Fai C.M. Koting S. Mohd N.S. Binti Jaafar W.Z. Hin L.S. El-Shafie A. 54782522900 57197765961 57211320170 57214837520 56436626600 57188832586 57214146115 55839645200 57192892703 57208880526 57201523473 16068189400 artificial neural network; dam; integrated approach; model; reservoir; simulation; stochasticity; water relations; water resource The simulation elevation-surface area-storage interrelationship of a reservoir is a crucial task in developing ideal water release policies for reservoir and dam operations. In this study, an inclusive (stochastic dynamic programming-artificial neural network (SDP-ANN)) model was established and applied to obtain an ideal reservoir operation strategy for Sg. Langat reservoir in Malaysia. The problems associated with the management of water resources mostly relate to uncertainty and the stochastic nature of the reservoir inflow, and the SDP-ANN model is meant to consider uncertainty in the input parameters such as reservoir inflow and reservoir evaporation losses. The performance of the SDP-ANN model was compared to that of the stochastic dynamic programming-autoregression (AR) model. The primary aim of the model is to decrease the squared deviation from the desired water release, which we determined by comparing the SDP-AR and SDP-ANN model performances. The results indicate that the SDP-ANN model demonstrated greater resilience and reliability with a lower supply deficit. Consequently, the case study results confirm that the SDP-ANN model performs better than the SDP-AR model in obtaining the best parameters for the reservoir operation. Specifically, a comparison of the models shows that the proposed Model 2 increased the reliability and resilience of the system by 7.5% and 6.3%, respectively. � 2019 by the authors. Final 2023-05-29T07:23:27Z 2023-05-29T07:23:27Z 2019 Article 10.3390/su11195367 2-s2.0-85073418129 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073418129&doi=10.3390%2fsu11195367&partnerID=40&md5=1bf64d462ba374ebeef36ae31df23561 https://irepository.uniten.edu.my/handle/123456789/24428 11 19 5367 All Open Access, Gold, Green MDPI Scopus |
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| description |
artificial neural network; dam; integrated approach; model; reservoir; simulation; stochasticity; water relations; water resource |
| author2 |
54782522900 |
| author_facet |
54782522900 Fayaed S.S. Fiyadh S.S. Khai W.J. Ahmed A.N. Afan H.A. Ibrahim R.K. Fai C.M. Koting S. Mohd N.S. Binti Jaafar W.Z. Hin L.S. El-Shafie A. |
| format |
Article |
| author |
Fayaed S.S. Fiyadh S.S. Khai W.J. Ahmed A.N. Afan H.A. Ibrahim R.K. Fai C.M. Koting S. Mohd N.S. Binti Jaafar W.Z. Hin L.S. El-Shafie A. |
| spellingShingle |
Fayaed S.S. Fiyadh S.S. Khai W.J. Ahmed A.N. Afan H.A. Ibrahim R.K. Fai C.M. Koting S. Mohd N.S. Binti Jaafar W.Z. Hin L.S. El-Shafie A. Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model |
| author_sort |
Fayaed S.S. |
| title |
Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model |
| title_short |
Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model |
| title_full |
Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model |
| title_fullStr |
Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model |
| title_full_unstemmed |
Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model |
| title_sort |
improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model |
| publisher |
MDPI |
| publishDate |
2023 |
| _version_ |
1806426609842913280 |
| score |
13.214268 |