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Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/0862FBC9-8DA3-4579-9FC0-4A6DC816D63A | ||
| Year | 2017 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | P. G. Theodoridou, E. A. Varouchakis and G. P. Karatzas, "Spatial analysis of groundwater levels using fuzzy logic and geostatistical tools," J. Hydrol., vol. 555, pp. 242-252, Dec., 2017. doi:10.1016/j.jhydrol.2017.10.027 https://doi.org/10.1016/j.jhydrol.2017.10.027 | ||
| Appears in Collections |
The spatial variability evaluation of the water table of an aquifer provides useful information in water resources management plans. Geostatistical methods are often employed to map the free surface of an aquifer. In geostatistical analysis using Kriging techniques the selection of the optimal variogram is very important for the optimal method performance. This work compares three different criteria to assess the theoretical variogram that fits to the experimental one: the Least Squares Sum method, the Akaike Information Criterion and the Cressie's Indicator. Moreover, variable distance metrics such as the Euclidean, Minkowski, Manhattan, Canberra and Bray-Curtis are applied to calculate the distance between the observation and the prediction points, that affects both the variogram calculation and the Kriging estimator. A Fuzzy Logic System is then applied to define the appropriate neighbors for each estimation point used in the Kriging algorithm. The two criteria used during the Fuzzy Logic process are the distance between observation and estimation points and the groundwater level value at each observation point. The proposed techniques are applied to a data set of 250 hydraulic head measurements distributed over an alluvial aquifer. The analysis showed that the Power-law variogram model and Manhattan distance metric within ordinary kriging provide the best results when the comprehensive geostatistical analysis process is applied. On the other hand, the Fuzzy Logic approach leads to a Gaussian variogram model and significantly improves the estimation performance. The two different variogram models can be explained in terms of a fractional Brownian motion approach and of aquifer behavior at local scale. Finally, maps of hydraulic head spatial variability and of predictions uncertainty are constructed for the area with the two different approaches comparing their advantages and drawbacks.
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