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Modeling water quality in an urban river using hydrological factors – Data driven approaches

dc.rights.licenseopenen_US
dc.contributor.authorCHANG, Fi-John
dc.contributor.authorTSAI, Yu-Hsuan
dc.contributor.authorCHEN, Pin-An
hal.structure.identifierEnvironnements et Paléoenvironnements OCéaniques [EPOC]
dc.contributor.authorCOYNEL, Alexandra
dc.contributor.authorVACHAUD, Georges
dc.date.accessioned2024-05-14T13:02:34Z
dc.date.available2024-05-14T13:02:34Z
dc.date.issued2015-03-01
dc.identifier.issn0301-4797en_US
dc.identifier.urihttps://www.researchgate.net/publication/270007604_Modeling_water_quality_in_an_urban_river_using_hydrological_factors_-_Data_driven_approaches
dc.identifier.urioai:crossref.org:10.1016/j.jenvman.2014.12.014
dc.identifier.urioai:researchgate.net:270007604
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/199824
dc.description.abstractEnContrasting seasonal variations occur in river flow and water quality as a result of short duration, severe intensity storms and typhoons in Taiwan. Sudden changes in river flow caused by impending extreme events may impose serious degradation on river water quality and fateful impacts on ecosystems. Water quality is measured in a monthly/quarterly scale, and therefore an estimation of water quality in a daily scale would be of good help for timely river pollution management. This study proposes a systematic analysis scheme (SAS) to assess the spatio-temporal interrelation of water quality in an urban river and construct water quality estimation models using two static and one dynamic artificial neural networks (ANNs) coupled with the Gamma test (GT) based on water quality, hydrological and economic data. The Dahan River basin in Taiwan is the study area. Ammonia nitrogen (NH3–N) is considered as the representative parameter, a correlative indicator in judging the contamination level over the study. Key factors the most closely related to the representative parameter (NH3–N) are extracted by the Gamma test for modeling NH3–N concentration, and as a result, four hydrological factors (discharge, days w/o discharge, water temperature and rainfall) are identified as model inputs. The modeling results demonstrate that the nonlinear autoregressive with exogenous input (NARX) network furnished with recurrent connections can accurately estimate NH3–N concentration with a very high coefficient of efficiency value (0.926) and a low RMSE value (0.386 mg/l). Besides, the NARX network can suitably catch peak values that mainly occur in dry periods (September–April in the study area), which is particularly important to water pollution treatment. The proposed SAS suggests a promising approach to reliably modeling the spatio-temporal NH3–N concentration based solely on hydrological data, without using water quality sampling data. It is worth noticing that such estimation can be made in a much shorter time interval of interest (span from a monthly scale to a daily scale) because hydrological data are long-term collected in a daily scale. The proposed SAS favorably makes NH3–N concentration estimation much easier (with only hydrological field sampling) and more efficient (in shorter time intervals), which can substantially help river managers interpret and estimate water quality responses to natural and/or manmade pollution in a more effective and timely way for river pollution management.
dc.language.isoENen_US
dc.sourcecrossref
dc.sourceresearchgate
dc.title.enModeling water quality in an urban river using hydrological factors – Data driven approaches
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.jenvman.2014.12.014en_US
dc.subject.halSciences de l'environnementen_US
bordeaux.journalJournal of Environmental Managementen_US
bordeaux.page87-96en_US
bordeaux.volume151en_US
bordeaux.hal.laboratoriesEPOC : Environnements et Paléoenvironnements Océaniques et Continentaux - UMR 5805en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.teamTGMen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcedissemin
hal.identifierhal-04574861
hal.version1
hal.date.transferred2024-05-14T13:02:36Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
workflow.import.sourcedissemin
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Environmental%20Management&rft.date=2015-03-01&rft.volume=151&rft.spage=87-96&rft.epage=87-96&rft.eissn=0301-4797&rft.issn=0301-4797&rft.au=CHANG,%20Fi-John&TSAI,%20Yu-Hsuan&CHEN,%20Pin-An&COYNEL,%20Alexandra&VACHAUD,%20Georges&rft.genre=article


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