Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/F41D0EFB-1CA3-4CA2-8D28-6AA2C4916272 | - |
| Αναγνωριστικό | https://doi.org/10.2166/hydro.2021.045 | - |
| Αναγνωριστικό | https://iwaponline.com/jh/article/23/5/1066/83242/A-decision-support-tool-for-optimising-groundwater | - |
| Γλώσσα | en | - |
| Μέγεθος | 17 pages | en |
| Τίτλος | A decision support tool for optimising groundwater-level monitoring networks using an adaptive genetic algorithm | en |
| Δημιουργός | Parasyris Antonios | en |
| Δημιουργός | Spanoudaki Katerina | en |
| Δημιουργός | Varouchakis Emmanouil | en |
| Δημιουργός | Βαρουχακης Εμμανουηλ | el |
| Δημιουργός | Kampanis, Nikolaos A | en |
| Εκδότης | IWA Publishing | en |
| Περίληψη | Mapping of the spatial variability of sparse groundwater-level measurements is usually achieved by means of geostatistical methods. This work tackles the problem of deficient sampling of an aquifer, by employing an innovative integer adaptive genetic algorithm (iaGA) coupled with geostatistical modelling by means of ordinary kriging, to optimise the monitoring network. Fitness functions based on three different errors are used for removing a constant number of boreholes from the monitoring network. The developed methodology has been applied to the Mires basin in Crete, Greece. The methodological improvement proposed concerns the adaptive method for the GA, which affects the crossover–mutation fractions depending on the stall parameter, aiming at higher accuracy and faster convergence of the GA. The initial dataset consists of 70 monitoring boreholes and the applied methodology shows that as many as 40 boreholes can be removed, while still retaining an accurate mapping of groundwater levels. The proposed scenario for optimising the monitoring network consists of removing 30 boreholes, in which case the estimated uncertainty is considerably smaller. A sensitivity analysis is conducted to compare the performance of the standard GA with the proposed iaGA. The integrated methodology presented is easily replicable for other areas for efficient monitoring networks design. | 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 | 2022-10-21 | - |
| Ημερομηνία Δημοσίευσης | 2021 | - |
| Θεματική Κατηγορία | Adaptive genetic algorithm | en |
| Θεματική Κατηγορία | Geostatistical modelling | en |
| Θεματική Κατηγορία | Groundwater monitoring network optimisation | en |
| Θεματική Κατηγορία | Kriging-based genetic algorithm optimisation | en |
| Βιβλιογραφική Αναφορά | A. Parasyris, K. Spanoudaki, E. A. Varouchakis, and N. A. Kampanis, “A decision support tool for optimising groundwater-level monitoring networks using an adaptive genetic algorithm,” J. Hydroinf., vol. 23, no. 5, pp. 1066–1082, Sep. 2021, doi: 10.2166/hydro.2021.045. | en |
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