Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/F94A8E13-059C-4A6C-9065-6F430B8C16B7 | - |
| Αναγνωριστικό | https://doi.org/10.2166/wst.2010.442. | - |
| Γλώσσα | en | - |
| Μέγεθος | 11 | en |
| Τίτλος | Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: saltwater intrusion. | en |
| Δημιουργός | Papadopoulou MP | en |
| Δημιουργός | Nikolos Ioannis | en |
| Δημιουργός | Νικολος Ιωαννης | el |
| Δημιουργός | Karatzas Giorgos | en |
| Δημιουργός | Καρατζας Γιωργος | el |
| Περιγραφή | Δημοσίευση σε επιστημονικό περιοδικό | el |
| Περίληψη | 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. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-10-21 | - |
| Ημερομηνία Δημοσίευσης | 2010 | - |
| Θεματική Κατηγορία | Artificial Neural Networks | en |
| Θεματική Κατηγορία | Radial Basis Function Artificial Neural Network | en |
| Θεματική Κατηγορία | Crete | en |
| Θεματική Κατηγορία | DE algorithm | en |
| Βιβλιογραφική Αναφορά | M.P. Papadopoulou , I.K. Nikolos, and G.P. Karatzas, "Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: saltwater intrusion.,"Water Science and Technology, vol. 62, no. 7,pp. 1479-1490, 2010. doi: 10.2166/wst.2010.442. | en |
Copyright © DIAS 2013
