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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/F5573C91-DFE0-4237-9F70-C493BDBF15AA | - |
| Identifier | https://doi.org/10.1007/s00477-023-02436-x | - |
| Identifier | https://link.springer.com/article/10.1007/s00477-023-02436-x | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | Combination of geostatistics and self-organizing maps for the spatial analysis of groundwater level variations in complex hydrogeological systems | en |
| Creator | Varouchakis Emmanouil | en |
| Creator | Βαρουχακης Εμμανουηλ | el |
| Creator | Solomatine Dimitri | en |
| Creator | Corzo Perez Gerald A. | en |
| Creator | Jomaa Seifeddine | en |
| Creator | Karatzas Georgios | en |
| Creator | Καρατζας Γεωργιος | el |
| Publisher | Springer | en |
| Description | The authors would like to thank the Special water secretariat of Greece for providing the data online. The national water monitoring program is presented in http://nmwn.ypeka.gr/?q=en. The InTheMED project, which is part of the PRIMA Programme supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 1923. | en |
| Content Summary | Successful modelling of the groundwater level variations in hydrogeological systems in complex formations considerably depends on spatial and temporal data availability and knowledge of the boundary conditions. Geostatistics plays an important role in model-related data analysis and preparation, but has specific limitations when the aquifer system is inhomogeneous. This study combines geostatistics with machine learning approaches to solve problems in complex aquifer systems. Herein, the emphasis is given to cases where the available dataset is large and randomly distributed in the different aquifer types of the hydrogeological system. Self-Organizing Maps can be applied to identify locally similar input data, to substitute the usually uncertain correlation length of the variogram model that estimates the correlated neighborhood, and then by means of Transgaussian Kriging to estimate the bias corrected spatial distribution of groundwater level. The proposed methodology was tested on a large dataset of groundwater level data in a complex hydrogeological area. The obtained results have shown a significant improvement compared to the ones obtained by classical geostatistical approaches. | 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 | 2025-08-18 | - |
| Date of Publication | 2023 | - |
| Subject | Transgaussian Kriging | en |
| Subject | Geostatistics | en |
| Subject | Self-organizing maps | en |
| Subject | Machine learning | en |
| Subject | Groundwater | en |
| Subject | Box-Cox | en |
| Bibliographic Citation | E. A. Varouchakis, D. Solomatine, G. A. Corzo Perez, S. Jomaa and G. P. Karatzas, “Combination of geostatistics and self-organizing maps for the spatial analysis of groundwater level variations in complex hydrogeological systems,” Stoch. Environ. Res. Risk Assess., vol. 37, no. 8, pp. 3009–3020, Aug. 2023, doi: 10.1007/s00477-023-02436-x. | en |
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