Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/02A02B31-83B9-424C-A008-AAF4FB23E56A | - |
| Identifier | https://doi.org/10.1016/j.jhydrol.2014.10.040 | - |
| Identifier | http://www.sciencedirect.com/science/article/pii/S002216941400835X | - |
| Language | en | - |
| Extent | 11 pages | en |
| Title | A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation | en |
| Creator | Tapoglou Evdokia | en |
| Creator | Ταπογλου Ευδοκια | el |
| Creator | Karatzas Giorgos | en |
| Creator | Καρατζας Γιωργος | el |
| Creator | Trichakis Ioannis | en |
| Creator | Τριχακης Ιωαννης | el |
| Creator | Varouchakis Emmanouil | en |
| Creator | Βαρουχακης Εμμανουηλ | el |
| Publisher | Elsevier | en |
| Description | Δημοσίευση σε επιστημονικό περιοδικό | el |
| Content Summary | 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 |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-10-20 | - |
| Date of Publication | 2014 | - |
| Subject | Artificial Neural Networks | en |
| Subject | Kriging | en |
| Subject | Groundwater hydraulic head simulation | en |
| Subject | Spatial and temporal simulation | en |
| Bibliographic Citation | 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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