<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/F94A8E13-059C-4A6C-9065-6F430B8C16B7"><efrbr-work:titleOfTheWork>Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: saltwater intrusion.</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/F94A8E13-059C-4A6C-9065-6F430B8C16B7"><efrbr-expression:titleOfTheExpression>Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: saltwater intrusion.</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Peer-Reviewed Journal Publication
            Δημοσίευση σε Περιοδικό με Κριτές
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2015-10-21</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">2010</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:summarizationOfContent>Artificial Neural Networks (ANNs) comprise a powerful tool to approximate the complicated behavior and response of physical systems allowing considerable reduction in computation time during time-consuming optimization runs. In this work, a Radial Basis Function Artificial Neural Network (RBFN) is combined with a Differential Evolution (DE) algorithm to solve a water resources management problem, using an optimization procedure. The objective of the optimization scheme is to cover the daily water demand on the coastal aquifer east of the city of Heraklion, Crete, without reducing the subsurface water quality due to seawater intrusion. The RBFN is utilized as an on-line surrogate model to approximate the behavior of the aquifer and to replace some of the costly evaluations of an accurate numerical simulation model which solves the subsurface water flow differential equations. The RBFN is used as a local approximation model in such a way as to maintain the robustness of the DE algorithm. The results of this procedure are compared to the corresponding results obtained by using the Simplex method and by using the DE procedure without the surrogate model. As it is demonstrated, the use of the surrogate model accelerates the convergence of the DE optimization procedure and additionally provides a better solution at the same number of exact evaluations, compared to the original DE algorithm.</efrbr-expression:summarizationOfContent><efrbr-expression:contextForTheExpression>Δημοσίευση σε επιστημονικό περιοδικό </efrbr-expression:contextForTheExpression><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="journal name">Water Science and Technology</efrbr-expression:note><efrbr-expression:note type="journal volume">7</efrbr-expression:note><efrbr-expression:note type="journal number">62</efrbr-expression:note><efrbr-expression:note type="page range">1479-1490</efrbr-expression:note></efrbr-expression:expression><efrbr-person:person identifier="F0B86E8A-104C-4561-909B-97C84A65A854"><efrbr-person:nameOfPerson vocabulary="">
            Papadopoulou MP
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="http://users.isc.tuc.gr/~inikolos"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Nikolos Ioannis
            Νικολος Ιωαννης
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="http://users.isc.tuc.gr/~gkaratzas"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Karatzas Giorgos
            Καρατζας Γιωργος
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-concept:concept identifier="21B054FC-8142-4B8B-BE15-4969F4A14E98"><efrbr-concept:termForTheConcept>
            Artificial Neural Networks
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="CECF1CCE-6BD5-4F43-84FC-3A78975E400C"><efrbr-concept:termForTheConcept>
             Radial Basis Function Artificial Neural Network 
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="BF25C704-C191-41A1-B84C-AE7983ACC0E6"><efrbr-concept:termForTheConcept>
            Crete
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="8DA4CD81-C8D7-4DD0-9405-49FA62DEE5EA"><efrbr-concept:termForTheConcept>
            DE algorithm
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