Metal and physico-chemical variations at a hydroelectric reservoir analyzed by Multivariate Analyses and Artificial Neural Networks: Environmental management and policy/decision-making tools

dc.creatorCavalcante, YL
dc.creatorHauser-Davis, RA
dc.creatorSaraiva, ACF
dc.creatorBrandao, ILS
dc.creatorOliveira, TF
dc.creatorSilveira, AM
dc.date2013
dc.date36892
dc.date2014-07-30T14:01:27Z
dc.date2015-11-26T16:34:05Z
dc.date2014-07-30T14:01:27Z
dc.date2015-11-26T16:34:05Z
dc.date.accessioned2018-03-28T23:16:13Z
dc.date.available2018-03-28T23:16:13Z
dc.identifierScience Of The Total Environment. Elsevier Science Bv, v. 442, n. 509, n. 514, 2013.
dc.identifier0048-9697
dc.identifierWOS:000313918200057
dc.identifier10.1016/j.scitotenv.2012.10.059
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/56568
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/56568
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1271085
dc.descriptionThis paper compared and evaluated seasonal variations in physico-chemical parameters and metals at a hydroelectric power station reservoir by applying Multivariate Analyses and Artificial Neural Networks (ANN) statistical techniques. A Factor Analysis was used to reduce the number of variables: the first factor was composed of elements Ca, K, Mg and Na, and the second by Chemical Oxygen Demand. The ANN showed 100% correct classifications in training and validation samples. Physico-chemical analyses showed that water pH values were not statistically different between the dry and rainy seasons, while temperature, conductivity, alkalinity, ammonia and DO were higher in the dry period. TSS, hardness and COD, on the other hand, were higher during the rainy season. The statistical analyses showed that Ca, K, Mg and Na are directly connected to the Chemical Oxygen Demand, which indicates a possibility of their input into the reservoir system by domestic sewage and agricultural run-offs. These statistical applications, thus, are also relevant in cases of environmental management and policy decision-making processes, to identify which factors should be further studied and/or modified to recover degraded or contaminated water bodies. (C) 2012 Elsevier B.V. All rights reserved.
dc.description442
dc.description509
dc.description514
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationScience Of The Total Environment
dc.relationSci. Total Environ.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectHydroelectric reservoir
dc.subjectMetals
dc.subjectChemical Oxygen Demand
dc.subjectMultivariate Analyses
dc.subjectArtificial Neural Networks
dc.subjectEnvironmental management
dc.subjectWater-quality
dc.subjectRiver
dc.subjectClassification
dc.subjectNigeria
dc.subjectSystem
dc.titleMetal and physico-chemical variations at a hydroelectric reservoir analyzed by Multivariate Analyses and Artificial Neural Networks: Environmental management and policy/decision-making tools
dc.typeArtículos de revistas


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