Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/10692
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dc.contributor.authorAyvaz, Mustafa Tamer-
dc.contributor.authorElçi, A.-
dc.date.accessioned2019-08-16T13:32:27Z
dc.date.available2019-08-16T13:32:27Z
dc.date.issued2018-
dc.identifier.issn0022-1694-
dc.identifier.urihttps://hdl.handle.net/11499/10692-
dc.identifier.urihttps://doi.org/10.1016/j.jhydrol.2018.06.006-
dc.description.abstractManagement of groundwater requires a sufficient coverage of accurate groundwater quality data. These data are usually collected from monitoring wells which are spatially distributed in the river basin or the groundwater body that is studied. A minimum number of monitoring wells with an optimum spatial distribution is desired to ensure a cost-effective observation of the groundwater body. Therefore, the configuration of groundwater monitoring networks and the number of required wells becomes an important engineering optimization problem. The goal of this study is to find an optimum monitoring network with the fewest wells that provides sufficient spatial coverage on groundwater quality. With the presented method redundant wells in an already existing network are identified. Here, a genetic algorithm (GA) based optimization approach is used in which each monitoring well in the watershed is represented with a binary GA bit to evaluate if the corresponding monitoring well will be selected for the network. The proposed approach can solve the problem by simultaneously optimizing two conflicting objectives. The first objective is the maximization of the match between the interpolated groundwater quality concentration distributions obtained using data from all available monitoring wells and the wells from the newly generated network. The match is primarily evaluated using the Nash-Sutcliffe (NS) model efficiency. Groundwater quality is represented by the water quality index WQI that aggregates several quality parameters. The second objective deals with the minimization of the number of monitoring wells in the newly generated network by considering cost-related constraints. These two objectives are integrated in a single objective function where different combinations of both objectives are investigated by considering two cases. The applicability of the proposed approach is evaluated for the groundwater monitoring network of the Gediz River Basin (GRB) which is one of the most important river basins in Turkey. Findings indicate that the proposed approach significantly reduces the number of monitoring wells with a relatively small deviation of the spatial distribution of the WQI values. Also, the monitoring network is optimized such that sampling points are removed from less polluted areas and selected in areas with higher pollutant concentrations. © 2018 Elsevier B.V.en_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofJournal of Hydrologyen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGenetic algorithmen_US
dc.subjectGroundwater qualityen_US
dc.subjectMonitoring networken_US
dc.subjectOptimizationen_US
dc.subjectCost effectivenessen_US
dc.subjectGenetic algorithmsen_US
dc.subjectGroundwateren_US
dc.subjectGroundwater resourcesen_US
dc.subjectMonitoringen_US
dc.subjectRiversen_US
dc.subjectSpatial distributionen_US
dc.subjectWater qualityen_US
dc.subjectWatershedsen_US
dc.subjectConcentration distributionsen_US
dc.subjectConflicting objectivesen_US
dc.subjectEngineering optimization problemsen_US
dc.subjectGroundwater monitoring networksen_US
dc.subjectGroundwater quality monitoringen_US
dc.subjectOptimization approachen_US
dc.subjectPollutant concentrationen_US
dc.subjectWellsen_US
dc.subjectair samplingen_US
dc.subjectconcentration (composition)en_US
dc.subjectgenetic algorithmen_US
dc.subjectgroundwateren_US
dc.subjectmonitoring systemen_US
dc.subjectoptimizationen_US
dc.subjectriver basinen_US
dc.subjectspatial distributionen_US
dc.subjectwater managementen_US
dc.subjectwater qualityen_US
dc.subjectGediz Basinen_US
dc.subjectTurkeyen_US
dc.titleIdentification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approachen_US
dc.typeArticleen_US
dc.identifier.volume563en_US
dc.identifier.startpage1078
dc.identifier.startpage1078en_US
dc.identifier.endpage1091en_US
dc.identifier.doi10.1016/j.jhydrol.2018.06.006-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopus2-s2.0-85049434741en_US
dc.identifier.wosWOS:000441492700087en_US
dc.identifier.scopusqualityQ1-
dc.ownerPamukkale University-
item.languageiso639-1en-
item.openairetypeArticle-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept10.02. Civil Engineering-
crisitem.author.dept14.01. Surgical Medicine-
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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