Optimising sampling designs for the maximum coverage problem of plume detection

Helle K., Pebesma E.

Forschungsartikel (Zeitschrift)

Zusammenfassung

The location of sensors to detect outbreaks of hazardous plumes in the atmosphere can be improved by considering possible paths of such plumes. Atmospheric dispersion models can provide simulations of such paths under realistic weather conditions. Numeric simulation always goes along with discretisation, and if a plume is detected or not can be regarded as a binary question. Thus optimising the locations of a fixed number of sensors can be regarded as finite; in fact it is a variety of the classical maximum coverage problem. We present an algorithm to completely solve this problem. It is based on complete deep tree search that finds all globally optimal sensor configurations, but the effort is reduced by skipping non-promising configurations and deleting redundant data. To determine the effort and to learn about optimal sensor configurations, the algorithm was tested on several scenarios based on plume simulations or on random data. This was completed by some theoretical results on the effect of problem size and plume detection probability on the effort. Finally we used the determined optimal configurations to evaluate two well known heuristics: greedy search and a genetic algorithm.

Details zur Publikation

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Seiten: 24
Veröffentlichungsjahr: 2015
Verlag: Elsevier
Sprache, in der die Publikation verfasst ist: Englisch
Link zum Volltext: http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84929581233&origin=inward