Optimising an observational water monitoring network for Archipelago Sea, South West Finland

Water quality monitoring in topographically fragmented archipelago coasts calls for a dense observational network. However, visiting multiple sites and analyzing the samples requires a significant amount of work, leading to considerable economic cost. It is of interest to determine an efficient set...

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Bibliographic Details
Published in:Computers & geosciences 2011-07, Vol.37 (7), p.844-854
Main Authors: Murtojärvi, Mika, Suominen, Tapio, Uusipaikka, Esa, Nevalainen, Olli S.
Format: Article
Language:English
Subjects:
Acceptability
Coasts
computers
data collection
Earth sciences
Earth, ocean, space
economic costs
Exact sciences and technology
External geophysics
Geochemistry
Heuristic
Mineralogy
Monitoring
Networks
Observational network
Optimization
Physical and chemical properties of sea water
Physics of the oceans
Pruning
Pruning algorithms
Silicates
The Baltic Sea
Water geochemistry
Water quality
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Summary:Water quality monitoring in topographically fragmented archipelago coasts calls for a dense observational network. However, visiting multiple sites and analyzing the samples requires a significant amount of work, leading to considerable economic cost. It is of interest to determine an efficient set of sites, which still offers adequate information on the water quality with a sufficient spatial accuracy. A method for optimizing an existing observational network is proposed. The method is concretized by applying it for an observational network in the Archipelago Sea, South West Finland. The network is pruned with the requirement that the observations of the removed sites can be estimated using those of the remaining sites. Suboptimal heuristics are used in pruning to keep the computational time acceptable. Some observations are not available and need to be estimated (imputed) before the pruning. For the network in the Archipelago Sea, the results of the pruning are somewhat sensitive to differences in imputed datasets and heuristics used for site selection.
ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2011.01.006