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 |