Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 429818
Title Optimizing the spatial pattern of networks for monitoring radioactive releases
Author(s) Melles, S.J.; Heuvelink, G.B.M.; Twenhofel, C.J.W.; Dijk, A. van; Hiemstra, P.H.; Baume, O.P.; Stohlker, U.
Source Computers and Geosciences 37 (2011)3. - ISSN 0098-3004 - p. 280 - 288.
DOI https://doi.org/10.1016/j.cageo.2010.04.007
Department(s) Land Dynamics
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) constrained optimization - sampling optimization - dose-rate - design - prediction - soil - contamination
Abstract This study presents a method to optimize the sampling design of environmental monitoring networks in a multi-objective setting. We optimize the permanent network of radiation monitoring stations in the Netherlands and parts of Germany as an example. The optimization method proposed combines minimization of prediction error under routine conditions with maximizing calamity detection capability in emergency cases. To calculate calamity detection capability, an atmospheric dispersion model was used to simulate potentially harmful radioactive releases. For each candidate monitoring network, we determined if the releases were detected within one, two and three hours. Four types of accidents were simulated: small and large nuclear power plant accidents, deliberate radioactive releases using explosive devices, and accidents involving the transport of radioactive materials. Spatial simulated annealing (SSA) was used to search for the optimal monitoring design. SSA was implemented by iteratively moving stations around and accepting all designs that improved a weighted sum of average spatial prediction error and calamity detection capability. Designs that worsened the multi-objective criterion were accepted with a certain probability, which decreased to zero as iterations proceeded. Results were promising and the method should prove useful for assessing the efficacy of environmental monitoring networks designed to monitor both routine and emergency conditions in other applications as well.
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