![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/42F7CF1D-F688-456D-AAF1-C7F5480B4D16 | - |
| Identifier | https://doi.org/10.1007/s10040-023-02693-8 | - |
| Identifier | https://link.springer.com/article/10.1007/s10040-023-02693-8 | - |
| Language | en | - |
| Extent | 4 pages | en |
| Title | Preface: Geostatistics and hydrogeology | en |
| Creator | Renard Philippe | en |
| Creator | Gómez-Hernández J. Jaime | en |
| Creator | Schafmeister Maria-Theresia | en |
| Creator | Varouchakis Emmanouil | en |
| Creator | Βαρουχακης Εμμανουηλ | el |
| Publisher | Springer | en |
| Content Summary | Geostatistics is a branch of statistics that deals with the analysis and modeling of spatial and temporal variability of natural phenomena. It is a discipline that grew at the boundaries of probability theory and earth sciences; it is now applied widely, for example, in environmental sciences, health sciences, or geographical information systems. It is also the foundation of a family of methods (Gaussian processes) that are now employed heavily in machine learning and artificial intelligence. This special issue reflects some of the typical and current applications of geostatistics in the field of hydrogeology. It also introduces some recent developments at the intersection of these two fields. In particular, many of the questions that are at the heart of the geostatistical approach are identical to those that are treated by machine learning or artificial intelligence algorithms. The idea of learning some patterns from data sets and making statistical predictions is exactly what geostatisticians have been doing for the last 60 years. Some of the algorithms recently used in the machine learning community are identical to what is commonly used in geostatistics. However, the rise and diversity of machine learning approaches and the dynamism of this new community has also opened new doors in the field of spatial statistics for hydrogeological applications; therefore, this special issue also explores some of these new methods. | en |
| Type of Item | Εισαγωγή Επιμελητή | el |
| Type of Item | Editorial | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2025-08-21 | - |
| Date of Publication | 2023 | - |
| Subject | Geostatistics | en |
| Subject | Hydrogeology | en |
| Subject | Interpolating groundwater levels | en |
| Subject | Groundwater quality | en |
| Subject | Aquifer parameters | en |
| Bibliographic Citation | P. Renard, J. J. Gómez-Hernández, M.-T. Schafmeister and E. A. Varouchakis, “Preface: Geostatistics and hydrogeology,” Hydrogeol. J., vol. 31, no. 6, pp. 1383–1386, Sep. 2023, doi: 10.1007/s10040-023-02693-8. | en |
Copyright © DIAS 2013
