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Artificial Neural Network and Differential Evolution methodologies used in single-and multi-objective formulations for the solution of subsurface water management problems

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dc.contributor.author Nikolos, IK en
dc.contributor.author Papadopoulou, MP en
dc.contributor.author Karatzas, GP en
dc.date.accessioned 2014-03-01T01:59:36Z
dc.date.available 2014-03-01T01:59:36Z
dc.date.issued 2010 en
dc.identifier.issn 17550386 en
dc.identifier.uri http://hdl.handle.net/123456789/29006
dc.subject ANNs en
dc.subject Artificial neural networks en
dc.subject DE en
dc.subject Differential evolution en
dc.subject Groundwater management en
dc.subject Multi-objective optimisation en
dc.subject Subsurface water management problems en
dc.title Artificial Neural Network and Differential Evolution methodologies used in single-and multi-objective formulations for the solution of subsurface water management problems en
heal.type journalArticle en
heal.identifier.primary 10.1504/IJAIP.2010.036401 en
heal.identifier.secondary http://dx.doi.org/10.1504/IJAIP.2010.036401 en
heal.publicationDate 2010 en
heal.abstract A single-objective Differential Evolution (DE) algorithm is combined with an Artificial Neural Network (ANN) to examine different operational strategies to cover the water demand in the Northern part of Rhodes Island, Greece. Successive calls to the simulator are used to provide the training data to the ANN, which is used as an approximation model to the simulator. Additionally, a multi-objective DE algorithm is combined with the pre-trained ANN to solve the same problem; the environmental constraints are realised through the definition of a second objective function, whereas the first objective function is the total pumping of the supply wells. © 2010 Inderscience Enterprises Ltd. en
heal.journalName International Journal of Advanced Intelligence Paradigms en
dc.identifier.doi 10.1504/IJAIP.2010.036401 en
dc.identifier.volume 2 en
dc.identifier.issue 4 en
dc.identifier.spage 365 en
dc.identifier.epage 377 en


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