Stationary Sampling Designs Based on Plume Simulations

Optimising sampling designs for the maximum coverage problem of plume detection

“…A plume was considered to be detectable in a location if the maximal hourly average dose rate exceeded 100 nSv/h at any time during plume passage. For more details of the plume simulations see Helle and Pebesma (2013). Earlier tests (Helle et al, 2014) showed the detection capacity of optimised sensors to be only little impaired through coarsening of the spatial resolution in the optimisation.…”

“…In a previous study (Helle and Pebesma, 2013), the tested region of 736 cells and with no internal sources was reached by 1226 potential plumes from 13 surrounding sources; ignoring half of them in the optimisation reduced the detection potential of the resulting sensor sets by no more than 5%.…”

“…For an overview of the literature related to atmospheric plume detection see Helle and Pebesma (2013).…”

“…As the effort of ACS is high, heuristics remain an important alternative and the globally optimal sensor configurations provide an objective benchmark to assess them. Greedy search is the classical deterministic heuristic to solve this problem and the probabilistic binary genetic algorithm looked promising in similar settings (Helle and Pebesma, 2013), therefore we repeated all tests with these algorithms. The empirical results about the effort of ACS, about properties of optimal sampling configurations, and about the goodness of the heuristics are given in Section 7, followed by a final discussion in Section 8.…”

Artificial neural networks for monitoring network optimisation—a practical example using a national insect survey

“…Monitoring network optimisation has been applied to weather stations (Heuvelink et al, 2012) and air pollution networks (Helle and Pebesma, 2012). However, the question of where and when to sample is also routinely considered by ecologists who are often confronted with domestic and practical concerns that dominate their decision process.…”