Multi-objective optimization of spatial sampling
Lark, R.M.. 2016 Multi-objective optimization of spatial sampling. Spatial Statistics, 18 (B). 412-430. 10.1016/j.spasta.2016.09.001
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Abstract/Summary
The optimization of spatial sampling by simulated annealing has been demonstrated and applied for a range of objective functions. In practice more than one objective function may be important for sampling, and there may be complex trade-offs between them. In this paper it is shown how a multi-objective optimization algorithm can be applied to a spatial sampling problem. This generates a set of solutions which is non-dominated (no one solution does better than any other on all objective functions). These solutions represent different feasible trade-offs between the objective functions, and a subset might be practically acceptable. The algorithm is applied to a hypothetical example of sampling for a regional mean with the variance of the mean and the total distance travelled between sample points as the two objective functions. The solutions represent a transition of sample arrays from a loose grid to a tight loop. The potential to develop this approach and apply it to other spatial sampling problems is discussed.
Item Type: | Publication - Article |
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Digital Object Identifier (DOI): | 10.1016/j.spasta.2016.09.001 |
ISSN: | 22116753 |
Date made live: | 07 Dec 2016 09:42 +0 (UTC) |
URI: | https://nora.nerc.ac.uk/id/eprint/515432 |
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