Sampling Optimization Trade-Offs for Long-Term Monitoring of Gamma Dose Rates Export

@incollection{citeulike:5040834, abstract = {This paper applies a recently developed optimization method to examine the design of networks that monitor radiation under routine conditions. Annual gamma dose rates were modelled by combining regression with interpolation of the regression residuals using spatially exhaustive predictors and an anisotropic variogram of the residuals. Locations of monitoring stations were optimized by minimizing the spatially averaged regression kriging standard deviation. Results suggest that the current network design is near optimal in terms of interpolation error in predicted gamma dose rates. When the network was thinned to fewer stations, spatial optimization was more effective at reducing the interpolation error. Given that some EU countries are considering reducing station density in border regions, the analysis reported here may be useful in guiding which stations can be removed.}, author = {Melles, S. and Heuvelink, G. and Twenh\"{o}fel, C. and St\"{o}hlker, U.}, citeulike-article-id = {5040834}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-69839-5_33}, citeulike-linkout-1 = {http://www.springerlink.com/content/y0647m2q22h74500}, doi = {10.1007/978-3-540-69839-5_33}, journal = {Computational Science and Its Applications – ICCSA 2008}, pages = {444--458}, posted-at = {2009-07-02 09:11:21}, priority = {0}, title = {Sampling Optimization Trade-Offs for Long-Term Monitoring of Gamma Dose Rates}, url = {http://dx.doi.org/10.1007/978-3-540-69839-5_33}, year = {2008} }

TY - CHAP ID - citeulike:5040834 L3 - citeulike-article-id:5040834 N2 - This paper applies a recently developed optimization method to examine the design of networks that monitor radiation under routine conditions. Annual gamma dose rates were modelled by combining regression with interpolation of the regression residuals using spatially exhaustive predictors and an anisotropic variogram of the residuals. Locations of monitoring stations were optimized by minimizing the spatially averaged regression kriging standard deviation. Results suggest that the current network design is near optimal in terms of interpolation error in predicted gamma dose rates. When the network was thinned to fewer stations, spatial optimization was more effective at reducing the interpolation error. Given that some EU countries are considering reducing station density in border regions, the analysis reported here may be useful in guiding which stations can be removed. JF - Computational Science and Its Applications – ICCSA 2008 EP - 458 TI - Sampling Optimization Trade-Offs for Long-Term Monitoring of Gamma Dose Rates SP - 444 AU - Melles, S AU - Heuvelink, G AU - Twenhöfel, C AU - Stöhlker, U PY - 2008/// UR - http://dx.doi.org/10.1007/978-3-540-69839-5_33 ER -