An application of Spartan spatial random fields in environmental mapping: focus on automatic mapping capabilities |
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Authors: | Samuel N Elogne Dionissios T Hristopulos Emmanouil Varouchakis |
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Institution: | (1) Department of Mineral Resources Engineering, Technical University of Crete, Chania, 73100, Greece |
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Abstract: | This paper investigates the potential of Spartan spatial random fields (SSRFs) in real-time mapping applications. The data
set that we study focuses on the distribution of daily gamma dose rates over part of Germany. Our goal is to determine a Spartan
spatial model from the data, and then use it to generate “predictive” maps of the radioactivity. In the SSRF framework, the
spatial dependence is determined from sample functions that focus on short-range correlations. A recently formulated SSRF
predictor is used to derive isolevel contour maps of the dose rates. The SSRF predictor is explicit. Moreover, the adjustments that it requires by the user are reduced compared to classical geostatistical methods. These features
present clear advantages for an automatic mapping system. The performance of the SSRF predictor is evaluated by means of various
cross-validation measures. The values of the performance measures are similar to those obtained by classical geostatistical
methods. Application of the SSRF method to data that simulate a radioactivity release scenario is also discussed. Hot spots
are detected and removed using a heuristic method. The extreme values that appear in the path of the simulated plume are not
captured by the currently used Spartan spatial model. Modeling of the processes leading to extreme values can enhance the
predictive capabilities of the spatial model, by incorporating physical information. |
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Keywords: | Correlations Stochastic estimation Spatial interpolation Environmental monitoring Hot spots Radioactivity |
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