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Anomaly detection in geostatistical models with application togroundwater level data in the Gaza Coastal Aquifer |
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| รหัสดีโอไอ | |
| Creator | 1. Ali H. Abuzaid 2. Diana A. Aish 3. Maroua Benghoul |
| Title | Anomaly detection in geostatistical models with application togroundwater level data in the Gaza Coastal Aquifer |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2565 |
| Journal Title | Songklanakarin Journal of Science an Technology (SJST) |
| Journal Vol. | 44 |
| Journal No. | 6 |
| Page no. | 1434-1441 |
| Keyword | geostatistics, variogram, kriging, sample influence function, intrinsic random functions, R |
| URL Website | https://sjst.psu.ac.th/ |
| ISSN | 0125-3395 |
| Abstract | In geostatistics, the detection of anomalous observations has a particular importance because of the changes they cancreate in environmental and geological patterns. Few methods for detecting such observations in univariate data have been proposedfor the spatial case, namely sample influence function (SIF), kriging, Intrinsic Random Functions (IRF), and geostatisticalfunctional data. This article reviews the main outlier detection procedures in the context of geostatistics, and due to the absence ofa numerical comparison between them, this article obtained the cut-off points of these methods for three different variogrammodels, and evaluated their performance via a simulation study. The results show that for all detection methods and the threeconsidered models, there is an inverse relationship between the level of contamination and power of performance. In addition, theSIF for the cubic variogram model outperforms the exponential and Mat?rn. Because of the peculiarities of the Gaza Strip, asregards Palestine water condition, and for illustration purposes, we consider real groundwater level data in the Gaza CoastalAquifer, where a set of possible outliers were identified. |
