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dc.title Selection of Evolutionary Multicriteria Strategies: Application in Designing a Regional Water Restoration Management Plan en
dc.type Artículo es
dcterms.abstract Sustainability 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.extent 15 p. es
dcterms.issued 2012-07-01
dcterms.language Inglés es
dcterms.license Attribution-NonCommercial 4.0 International (BY-NC 4.0) es
dcterms.subject Soft Computing en
dcterms.subject Business Intelligence en
dcterms.subject Muticriteria Analysis en
dcterms.subject Optimization en
cic.version info:eu-repo/semantics/acceptedVersion es Udías, Ángel es Redchuk, Andrés es Cano, Javier es Galbiati, Lorenzo es
cic.lugarDesarrollo Instituto de Investigaciones en Ingeniería Industrial es
dcterms.subject.materia Otras Ingenierías y Tecnologías es
dcterms.identifier.url Recurso Completo es
dcterms.identifier.other 10.1007/978-3-642-53737-0_21 es
dcterms.isPartOf.issue vol. 537 es
dcterms.isPartOf.series Soft Computing for Business Intelligence es
cic.isPeerReviewed true es
cic.isFulltext true es


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