Study on Optimal Allocation of Water Resources Based on Surrogate Model of Groundwater Numerical Simulation
The characteristics of groundwater systems are highly complex. It will take substantial computational resources and running time to optimize a groundwater numerical simulation model. In this study, in order to realize the coupling of simulation and optimization models, the improved backpropagation (...
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| Veröffentlicht in: | Water (Basel) 2019-04, Vol.11 (4), p.831 |
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| creator | Wang, Yufeng Shao, Jingli Zhang, Qiulan |
| description | The characteristics of groundwater systems are highly complex. It will take substantial computational resources and running time to optimize a groundwater numerical simulation model. In this study, in order to realize the coupling of simulation and optimization models, the improved backpropagation (BP) neural network was used as a surrogate model of a groundwater numerical simulation; the improved BP neural network was trained with the groundwater level drawdown–pumping volume data output of the simulation model. The method was applied to the water resource optimal allocation in the near future of Wenshang County, Shandong Provence of China. The results show that the water level drawdown output of the improved BP neural network model fits the results of the simulation model well, showing that the improved BP neural network can effectively be the surrogate of a groundwater numerical simulation to be embedded in an optimization model. The improved simulation and optimization technique can make full use of water resources in the whole area. Under an assurance rate of 50%, both water shortage and water shortage rate reduced to zero in the whole area. Under an assurance rate of 75%, water shortage and water shortage rate reduced to about 10% of the conventional scheme, which dramatically improves the comprehensive benefit of the whole area. |
| doi_str_mv | 10.3390/w11040831 |
| format | Article |
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It will take substantial computational resources and running time to optimize a groundwater numerical simulation model. In this study, in order to realize the coupling of simulation and optimization models, the improved backpropagation (BP) neural network was used as a surrogate model of a groundwater numerical simulation; the improved BP neural network was trained with the groundwater level drawdown–pumping volume data output of the simulation model. The method was applied to the water resource optimal allocation in the near future of Wenshang County, Shandong Provence of China. The results show that the water level drawdown output of the improved BP neural network model fits the results of the simulation model well, showing that the improved BP neural network can effectively be the surrogate of a groundwater numerical simulation to be embedded in an optimization model. The improved simulation and optimization technique can make full use of water resources in the whole area. Under an assurance rate of 50%, both water shortage and water shortage rate reduced to zero in the whole area. Under an assurance rate of 75%, water shortage and water shortage rate reduced to about 10% of the conventional scheme, which dramatically improves the comprehensive benefit of the whole area.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w11040831</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Approximation ; Aquifers ; Artificial intelligence ; Assurance ; Back propagation networks ; China ; Computer applications ; Computer simulation ; Drawdown ; Environmental aspects ; Groundwater ; Groundwater levels ; Hydrology ; Mathematical models ; Mathematical optimization ; Mathematical programming ; Measurement ; Methods ; Neural networks ; Numerical analysis ; Optimization ; Optimization techniques ; Resource allocation ; Simulation ; Software ; Sustainable development ; Water levels ; Water resources ; Water shortages ; Water supply ; Water table ; Water use ; Water, Underground</subject><ispartof>Water (Basel), 2019-04, Vol.11 (4), p.831</ispartof><rights>COPYRIGHT 2019 MDPI AG</rights><rights>2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c331t-6c076ef6890d593e074e339ee2265e1d8b746eebc3541957940968260b7b235a3</citedby><cites>FETCH-LOGICAL-c331t-6c076ef6890d593e074e339ee2265e1d8b746eebc3541957940968260b7b235a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Wang, Yufeng</creatorcontrib><creatorcontrib>Shao, Jingli</creatorcontrib><creatorcontrib>Zhang, Qiulan</creatorcontrib><title>Study on Optimal Allocation of Water Resources Based on Surrogate Model of Groundwater Numerical Simulation</title><title>Water (Basel)</title><description>The characteristics of groundwater systems are highly complex. It will take substantial computational resources and running time to optimize a groundwater numerical simulation model. In this study, in order to realize the coupling of simulation and optimization models, the improved backpropagation (BP) neural network was used as a surrogate model of a groundwater numerical simulation; the improved BP neural network was trained with the groundwater level drawdown–pumping volume data output of the simulation model. The method was applied to the water resource optimal allocation in the near future of Wenshang County, Shandong Provence of China. The results show that the water level drawdown output of the improved BP neural network model fits the results of the simulation model well, showing that the improved BP neural network can effectively be the surrogate of a groundwater numerical simulation to be embedded in an optimization model. The improved simulation and optimization technique can make full use of water resources in the whole area. Under an assurance rate of 50%, both water shortage and water shortage rate reduced to zero in the whole area. Under an assurance rate of 75%, water shortage and water shortage rate reduced to about 10% of the conventional scheme, which dramatically improves the comprehensive benefit of the whole area.</description><subject>Approximation</subject><subject>Aquifers</subject><subject>Artificial intelligence</subject><subject>Assurance</subject><subject>Back propagation networks</subject><subject>China</subject><subject>Computer applications</subject><subject>Computer simulation</subject><subject>Drawdown</subject><subject>Environmental aspects</subject><subject>Groundwater</subject><subject>Groundwater levels</subject><subject>Hydrology</subject><subject>Mathematical models</subject><subject>Mathematical optimization</subject><subject>Mathematical programming</subject><subject>Measurement</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Numerical analysis</subject><subject>Optimization</subject><subject>Optimization techniques</subject><subject>Resource allocation</subject><subject>Simulation</subject><subject>Software</subject><subject>Sustainable development</subject><subject>Water levels</subject><subject>Water resources</subject><subject>Water shortages</subject><subject>Water supply</subject><subject>Water table</subject><subject>Water use</subject><subject>Water, Underground</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUMtOwzAQtBBIVKUH_iASJw4t61fiHEsFBalQiYI4Ro6zqVKSuNiJqv49bosQ68OuxjNj7xByTWHCeQp3O0pBgOL0jAwYJHwshKDn_-ZLMvJ-A6FEqpSEAfladX2xj2wbLbdd1eg6mta1NbqrAmTL6FN36KI39LZ3Bn10rz0WB_qqd86uw230YgusD9y5s31b7I6K175BV5ngt6qavj76XZGLUtceR799SD4eH95nT-PFcv48my7GhnPajWMDSYxlrFIoZMoREoFhPUTGYom0UHkiYsTccCloKpNUQBorFkOe5IxLzYfk5uS7dfa7R99lm_D7NjyZMSnD6pIBDazJibXWNWZVW9rOaRNOgU1lbItlFfCpgjThUgELgtuTwDjrvcMy27qQmNtnFLJD_tlf_vwHtGh2Iw</recordid><startdate>20190401</startdate><enddate>20190401</enddate><creator>Wang, Yufeng</creator><creator>Shao, Jingli</creator><creator>Zhang, Qiulan</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20190401</creationdate><title>Study on Optimal Allocation of Water Resources Based on Surrogate Model of Groundwater Numerical Simulation</title><author>Wang, Yufeng ; Shao, Jingli ; Zhang, Qiulan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-6c076ef6890d593e074e339ee2265e1d8b746eebc3541957940968260b7b235a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Approximation</topic><topic>Aquifers</topic><topic>Artificial intelligence</topic><topic>Assurance</topic><topic>Back propagation networks</topic><topic>China</topic><topic>Computer applications</topic><topic>Computer simulation</topic><topic>Drawdown</topic><topic>Environmental aspects</topic><topic>Groundwater</topic><topic>Groundwater levels</topic><topic>Hydrology</topic><topic>Mathematical models</topic><topic>Mathematical optimization</topic><topic>Mathematical programming</topic><topic>Measurement</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Numerical analysis</topic><topic>Optimization</topic><topic>Optimization techniques</topic><topic>Resource allocation</topic><topic>Simulation</topic><topic>Software</topic><topic>Sustainable development</topic><topic>Water levels</topic><topic>Water resources</topic><topic>Water shortages</topic><topic>Water supply</topic><topic>Water table</topic><topic>Water use</topic><topic>Water, Underground</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yufeng</creatorcontrib><creatorcontrib>Shao, Jingli</creatorcontrib><creatorcontrib>Zhang, Qiulan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Yufeng</au><au>Shao, Jingli</au><au>Zhang, Qiulan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Study on Optimal Allocation of Water Resources Based on Surrogate Model of Groundwater Numerical Simulation</atitle><jtitle>Water (Basel)</jtitle><date>2019-04-01</date><risdate>2019</risdate><volume>11</volume><issue>4</issue><spage>831</spage><pages>831-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>The characteristics of groundwater systems are highly complex. It will take substantial computational resources and running time to optimize a groundwater numerical simulation model. In this study, in order to realize the coupling of simulation and optimization models, the improved backpropagation (BP) neural network was used as a surrogate model of a groundwater numerical simulation; the improved BP neural network was trained with the groundwater level drawdown–pumping volume data output of the simulation model. The method was applied to the water resource optimal allocation in the near future of Wenshang County, Shandong Provence of China. The results show that the water level drawdown output of the improved BP neural network model fits the results of the simulation model well, showing that the improved BP neural network can effectively be the surrogate of a groundwater numerical simulation to be embedded in an optimization model. The improved simulation and optimization technique can make full use of water resources in the whole area. Under an assurance rate of 50%, both water shortage and water shortage rate reduced to zero in the whole area. Under an assurance rate of 75%, water shortage and water shortage rate reduced to about 10% of the conventional scheme, which dramatically improves the comprehensive benefit of the whole area.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w11040831</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Approximation Aquifers Artificial intelligence Assurance Back propagation networks China Computer applications Computer simulation Drawdown Environmental aspects Groundwater Groundwater levels Hydrology Mathematical models Mathematical optimization Mathematical programming Measurement Methods Neural networks Numerical analysis Optimization Optimization techniques Resource allocation Simulation Software Sustainable development Water levels Water resources Water shortages Water supply Water table Water use Water, Underground |
| title | Study on Optimal Allocation of Water Resources Based on Surrogate Model of Groundwater Numerical Simulation |
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