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Dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation
The dataset contains reservoir characteristic parameters, streamflow series of reservoirs in the upper Yangtze River, the standard operating rules (SORs) and the seasonal top of buffer pools (seasonal TBPs) for these reservoirs, which were provided by the Yangtze River Commission. Moreover, annual h...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736769/ https://www.ncbi.nlm.nih.gov/pubmed/31516958 http://dx.doi.org/10.1016/j.dib.2019.104440 |
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author | He, Shaokun Guo, Shenglian Chen, Kebing Deng, Lele Liao, Zhen Xiong, Feng Yin, Jiabo |
author_facet | He, Shaokun Guo, Shenglian Chen, Kebing Deng, Lele Liao, Zhen Xiong, Feng Yin, Jiabo |
author_sort | He, Shaokun |
collection | PubMed |
description | The dataset contains reservoir characteristic parameters, streamflow series of reservoirs in the upper Yangtze River, the standard operating rules (SORs) and the seasonal top of buffer pools (seasonal TBPs) for these reservoirs, which were provided by the Yangtze River Commission. Moreover, annual hydropower of these reservoirs is tested to evaluate operation performance. These research materials are related to the research article in Advances in Water Resources, entitled ‘Optimal impoundment operation for cascade reservoirs coupling parallel dynamic programming with importance sampling and successive approximation’ (He et al., 2019). The dataset could be used to derive optimal operating rules to explore the potential benefits of water resources via our proposed algorithm (importance sampling – parallel dynamic programming, IS-PDP) in different runoff scenarios. It can also be further applied for water resources management and other potential users. |
format | Online Article Text |
id | pubmed-6736769 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-67367692019-09-12 Dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation He, Shaokun Guo, Shenglian Chen, Kebing Deng, Lele Liao, Zhen Xiong, Feng Yin, Jiabo Data Brief Environmental Science The dataset contains reservoir characteristic parameters, streamflow series of reservoirs in the upper Yangtze River, the standard operating rules (SORs) and the seasonal top of buffer pools (seasonal TBPs) for these reservoirs, which were provided by the Yangtze River Commission. Moreover, annual hydropower of these reservoirs is tested to evaluate operation performance. These research materials are related to the research article in Advances in Water Resources, entitled ‘Optimal impoundment operation for cascade reservoirs coupling parallel dynamic programming with importance sampling and successive approximation’ (He et al., 2019). The dataset could be used to derive optimal operating rules to explore the potential benefits of water resources via our proposed algorithm (importance sampling – parallel dynamic programming, IS-PDP) in different runoff scenarios. It can also be further applied for water resources management and other potential users. Elsevier 2019-08-28 /pmc/articles/PMC6736769/ /pubmed/31516958 http://dx.doi.org/10.1016/j.dib.2019.104440 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Environmental Science He, Shaokun Guo, Shenglian Chen, Kebing Deng, Lele Liao, Zhen Xiong, Feng Yin, Jiabo Dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation |
title | Dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation |
title_full | Dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation |
title_fullStr | Dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation |
title_full_unstemmed | Dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation |
title_short | Dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation |
title_sort | dataset for reservoir impoundment operation coupling parallel dynamic programming with importance sampling and successive approximation |
topic | Environmental Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736769/ https://www.ncbi.nlm.nih.gov/pubmed/31516958 http://dx.doi.org/10.1016/j.dib.2019.104440 |
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