Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/02A02B31-83B9-424C-A008-AAF4FB23E56A | - |
| Αναγνωριστικό | https://doi.org/10.1016/j.jhydrol.2014.10.040 | - |
| Αναγνωριστικό | http://www.sciencedirect.com/science/article/pii/S002216941400835X | - |
| Γλώσσα | en | - |
| Μέγεθος | 11 pages | en |
| Τίτλος | A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation | en |
| Δημιουργός | Tapoglou Evdokia | en |
| Δημιουργός | Ταπογλου Ευδοκια | el |
| Δημιουργός | Karatzas Giorgos | en |
| Δημιουργός | Καρατζας Γιωργος | el |
| Δημιουργός | Trichakis Ioannis | en |
| Δημιουργός | Τριχακης Ιωαννης | el |
| Δημιουργός | Varouchakis Emmanouil | en |
| Δημιουργός | Βαρουχακης Εμμανουηλ | el |
| Εκδότης | Elsevier | en |
| Περιγραφή | Δημοσίευση σε επιστημονικό περιοδικό | el |
| Περίληψη | Artificial Neural Networks (ANNs) and Kriging have both been used for hydraulic head simulation. In this study, the two methodologies were combined in order to simulate the spatial and temporal distribution of hydraulic head in a study area. In order to achieve that, a fuzzy logic inference system can also be used. Different ANN architectures and variogram models were tested, together with the use or not of a fuzzy logic system. The developed algorithm was implemented and applied for predicting, spatially and temporally, the hydraulic head in an area located in Bavaria, Germany. The performance of the algorithm was evaluated using leave one out cross validation and various performance indicators were derived. The best results were achieved by using ANNs with two hidden layers, with the use of the fuzzy logic system and by utilizing the power-law variogram. The results obtained from this procedure can be characterized as favorable, since the RMSE of the method is in the order of magnitude of 10−2 m. Therefore this method can be used successfully in aquifers where geological characteristics are obscure, but a variety of other, easily accessible data, such as meteorological data can be easily found. | 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 | 2015-10-20 | - |
| Ημερομηνία Δημοσίευσης | 2014 | - |
| Θεματική Κατηγορία | Artificial Neural Networks | en |
| Θεματική Κατηγορία | Kriging | en |
| Θεματική Κατηγορία | Groundwater hydraulic head simulation | en |
| Θεματική Κατηγορία | Spatial and temporal simulation | en |
| Βιβλιογραφική Αναφορά | E. Tapoglou , G. P. Karatzas, I. C. Trichakis and E. A. Varouchakis,"A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation," Journal of Hydrology, vol. 519, Part D, pp. 3193–3203, Nov. 2014. doi: 10.1016/j.jhydrol.2014.10.040 | en |
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