Conservative Updating of Sampling Designs
Kristina B. Helle*, Edzer Pebesma
Institute for Geoinformatics University of Minister Munster, Germany
Abstract: When improving existing monitoring networks, to adapt to changed requirements, keeping as many stations as possible is cheapest and therefore often preferred over a completely new setup. Here, the sampling design for ambient gamma dose monitoring in Norway is optimised. We consider two goals: equal spread of stations, and detection of plumes that affect densely populated areas. For optimisation, we compare and improve algorithms that replace the existing stations one by one: a greedy algorithm replaces the most unimportant station by the best candidate station; random replacement keeps all random improvements. A new approach is random replacement that rejects all sampling designs with too many stations moved. We add a penalty term to the cost function to search sampling designs with few station moves. This combines the advantages of the two previous approaches: The greedy algorithm replaces the most unimportant stations only, therefore as many stations as possible are kept. Random search can consider more candidates and often is faster. Random replacement with penalty is faster than the greedy algorithms, whereas for detection, the resulting sampling designs were of the same quality: moving a station pays off with a similar improvement in cost.
Keywords: update cost, spatial sampling design, space coverage, plume detection, greedy algorithm