About regression-kriging: From equations to case studies
Computers & Geosciences
Sampling Optimization Trade-Offs for Long-Term Monitoring of Gamma Dose Rates
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Optimization of mobile radioactivity monitoring networks
International Journal of Geographical Information Science
INTAMAP: The design and implementation of an interoperable automated interpolation web service
Computers & Geosciences
Network optimization algorithms and scenarios in the context of automatic mapping
Computers & Geosciences
Environmental Modelling & Software
Value of information and mobility constraints for sampling with mobile sensors
Computers & Geosciences
Accelerating universal Kriging interpolation algorithm using CUDA-enabled GPU
Computers & Geosciences
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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.