Selection of Evolutionary Multicriteria Strategies: Application in Designing a Regional Water Restoration Management Plan

cic.isFulltexttruees
cic.isPeerReviewedtruees
cic.lugarDesarrolloInstituto de Investigaciones en Ingeniería Industrial es
cic.versioninfo:eu-repo/semantics/acceptedVersiones
dc.date.accessioned2017-04-07T14:05:22Z
dc.date.available2017-04-07T14:05:22Z
dc.identifier.urihttps://digital.cic.gba.gob.ar/handle/11746/5540
dc.titleSelection of Evolutionary Multicriteria Strategies: Application in Designing a Regional Water Restoration Management Planen
dc.typeArtículoes
dcterms.abstractSustainability of water resources has become a challenging problem worldwide, as the pollution levels of natural water resources (particularly of rivers) have increased drastically in the last decades. Nowadays, there are many Waste Water Treatment Plant (WWTP) technologies that provide different levels of efficiency in the removal of water pollutants, leading to a great number of combinations of different measures (PoM) or strategies. The management problem, then, involves finding which of these combinations are efficient, regarding the desired objectives (cost and quality). Therefore, decisions affecting water resources require the application of multi-objective optimization techniques which will lead to a set of tradeoff solutions, none of which is better or worse than the others, but, rather, the final decision must be one particular PoM including representative features of the whole set of solutions. Besides, there is not a universally accepted standard way to assess the water quality of a river. In order to consider simultaneously all these issues, we present in this work a hydroinformatics management tool, designed to help decision makers with the selection of a PoM that satisfies the WFD objectives. Our approach combines: 1) a Water Quality Model (WQM), devised to simulate the effects of each PoM used to reduce pollution pressures on the hydrologic network; 2) a Multi-Objective Evolutionary Algorithm (MOEA), used to identify efficient tradeoffs between PoMs’ costs and water quality; and 3) visualization of the Pareto optimal set, in order to extract knowledge from optimal decisions in a usable form. We have applied our methodology in a real scenario, the inner Catalan watersheds with promising results.en
dcterms.creator.authorUdías, Ángeles
dcterms.creator.authorRedchuk, Andréses
dcterms.creator.authorCano, Javieres
dcterms.creator.authorGalbiati, Lorenzoes
dcterms.extent15 p.es
dcterms.identifier.other10.1007/978-3-642-53737-0_21es
dcterms.identifier.urlRecurso Completoes
dcterms.isPartOf.issuevol. 537es
dcterms.isPartOf.seriesSoft Computing for Business Intelligencees
dcterms.issued2012-07-01
dcterms.languageIngléses
dcterms.licenseAttribution-NonCommercial 4.0 International (BY-NC 4.0)es
dcterms.subjectSoft Computingen
dcterms.subjectBusiness Intelligenceen
dcterms.subjectMuticriteria Analysisen
dcterms.subjectOptimizationen
dcterms.subject.materiaOtras Ingenierías y Tecnologíases

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