Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI: | http://purl.tuc.gr/dl/dias/2BCC53D8-0654-4816-8639-F54188F780D6 | ||
| Έτος | 2023 | ||
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | A. Pavlides, E. A. Varouchakis and D. T. Hristopulos, “Geostatistical analysis of groundwater levels in a mining area with three active mines,” Hydrogeol. J., vol. 31, no. 6, pp. 1425–1441, Sep. 2023, doi: 10.1007/s10040-023-02676-9. https://doi.org/10.1007/s10040-023-02676-9 | ||
| Εμφανίζεται στις Συλλογές |
Mining activities can significantly impact groundwater reservoirs in their vicinity. Different approaches have been employed, with varying success, to investigate the spatial variability of groundwater levels in mining areas. Typical problems include the small sample size, the non-Gaussian distribution of the data, and the clustering of sample locations near the mines. These conditions complicate the estimation of spatial dependence. Under sparse and irregular sampling conditions, stochastic methods, which can provide estimates of prediction uncertainty, are preferable to deterministic ones. This research focuses on the comparison of two stochastic methods, stochastic local interactions (SLI) and universal Kriging (UK), using water level data from 72 locations around three mines in Northern Greece. UK is a well-known, variogram-based geostatistical method, while SLI is a computationally efficient kernel-based method that can cope with large spatial datasets. The non-Gaussian distribution of the data is handled by means of a flexible, data-driven Gaussian anamorphosis method that uses kernel functions. The spatial prediction performance of both methods is assessed based on cross-validation. UK performs better than SLI, due to the fact that the former incorporates a linear trend function. On the other hand, a comparison of the two methods using data from a single mine that contains only 28 measurement locations shows that SLI performs slightly better than UK. The prediction uncertainties for both methods are also estimated and compared. The results suggest that SLI can provide better estimates than classical geostatistical methods for small sample sizes that do not allow reliable estimation of the variogram model.
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