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Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: Saltwater intrusion

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dc.contributor.author Papadopoulou, MP en
dc.contributor.author Nikolos, IK en
dc.contributor.author Karatzas, GP en
dc.date.accessioned 2014-03-01T01:33:02Z
dc.date.available 2014-03-01T01:33:02Z
dc.date.issued 2010 en
dc.identifier.issn 0273-1223 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/20286
dc.subject Artificial intelligence en
dc.subject Computational cost en
dc.subject Design en
dc.subject Differential evolution en
dc.subject Groundwater resources management en
dc.subject Neural networks en
dc.subject Saltwater intrusion en
dc.subject Surrogate model en
dc.subject.classification Engineering, Environmental en
dc.subject.classification Environmental Sciences en
dc.subject.classification Water Resources en
dc.subject.other Artificial intelligent en
dc.subject.other Artificial Neural Network en
dc.subject.other Artificial neural networks en
dc.subject.other Coastal aquifers en
dc.subject.other Computation time en
dc.subject.other Computational costs en
dc.subject.other Daily water demand en
dc.subject.other DE algorithms en
dc.subject.other De-optimization en
dc.subject.other Differential Evolution en
dc.subject.other Differential evolution algorithms en
dc.subject.other Environmental design en
dc.subject.other Groundwater resources management en
dc.subject.other Local approximation en
dc.subject.other Numerical simulation models en
dc.subject.other Optimization procedures en
dc.subject.other Optimization scheme en
dc.subject.other Physical systems en
dc.subject.other Radial basis functions en
dc.subject.other Seawater intrusion en
dc.subject.other Simplex methods en
dc.subject.other Subsurface waters en
dc.subject.other Surrogate model en
dc.subject.other Water resources management en
dc.subject.other Approximation algorithms en
dc.subject.other Aquifers en
dc.subject.other Biology en
dc.subject.other Computer simulation en
dc.subject.other Convergence of numerical methods en
dc.subject.other Design en
dc.subject.other Differential equations en
dc.subject.other Differentiation (calculus) en
dc.subject.other Evolutionary algorithms en
dc.subject.other Groundwater geochemistry en
dc.subject.other Groundwater resources en
dc.subject.other Network management en
dc.subject.other Optimization en
dc.subject.other Radial basis function networks en
dc.subject.other Salt water intrusion en
dc.subject.other Seawater en
dc.subject.other Security of data en
dc.subject.other Water pollution en
dc.subject.other Water quality en
dc.subject.other Neural networks en
dc.subject.other sea water en
dc.subject.other surface water en
dc.subject.other artificial intelligence en
dc.subject.other artificial neural network en
dc.subject.other groundwater resource en
dc.subject.other optimization en
dc.subject.other saline intrusion en
dc.subject.other subsurface flow en
dc.subject.other surrogate method en
dc.subject.other water demand en
dc.subject.other water flow en
dc.subject.other water quality en
dc.subject.other algorithm en
dc.subject.other aquifer en
dc.subject.other article en
dc.subject.other artificial intelligence en
dc.subject.other artificial neural network en
dc.subject.other environmental monitoring en
dc.subject.other finite element analysis en
dc.subject.other methodology en
dc.subject.other process model en
dc.subject.other radial basis function artificial neural network en
dc.subject.other seashore en
dc.subject.other simulation en
dc.subject.other water management en
dc.subject.other water quality en
dc.subject.other water supply en
dc.subject.other Algorithms en
dc.subject.other Artificial Intelligence en
dc.subject.other Geography en
dc.subject.other Greece en
dc.subject.other Models, Chemical en
dc.subject.other Neural Networks (Computer) en
dc.subject.other Seawater en
dc.subject.other Water Supply en
dc.subject.other Crete en
dc.subject.other Greece en
dc.subject.other Iraklion [Crete] en
dc.title Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: Saltwater intrusion en
heal.type journalArticle en
heal.identifier.primary 10.2166/wst.2010.442 en
heal.identifier.secondary http://dx.doi.org/10.2166/wst.2010.442 en
heal.language English en
heal.publicationDate 2010 en
heal.abstract 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. © IWA Publishing 2010. en
heal.publisher I W A PUBLISHING en
heal.journalName Water Science and Technology en
dc.identifier.doi 10.2166/wst.2010.442 en
dc.identifier.isi ISI:000283398100004 en
dc.identifier.volume 62 en
dc.identifier.issue 7 en
dc.identifier.spage 1479 en
dc.identifier.epage 1490 en


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