Optimal estuarine sediment monitoring network design with simulated annealing

Detalhes bibliográficos
Autor(a) principal: Nunes, L. M.
Data de Publicação: 2006
Outros Autores: Caeiro, S., Cunha, M. C., Ribeiro, L.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/3978
https://doi.org/10.1016/j.jenvman.2005.04.024
Resumo: An objective function based on geostatistical variance reduction, constrained to the reproduction of the probability distribution functions of selected physical and chemical sediment variables, is applied to the selection of the best set of compliance monitoring stations in the Sado river estuary in Portugal. These stations were to be selected from a large set of sampling stations from a prior field campaign. Simulated annealing was chosen to solve the optimisation function model. Both the combinatorial problem structure and the resulting candidate sediment monitoring networks are discussed, and the optimal dimension and spatial distribution are proposed. An optimal network of sixty stations was obtained from an original 153-station sampling campaign.
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spelling Optimal estuarine sediment monitoring network design with simulated annealingAn objective function based on geostatistical variance reduction, constrained to the reproduction of the probability distribution functions of selected physical and chemical sediment variables, is applied to the selection of the best set of compliance monitoring stations in the Sado river estuary in Portugal. These stations were to be selected from a large set of sampling stations from a prior field campaign. Simulated annealing was chosen to solve the optimisation function model. Both the combinatorial problem structure and the resulting candidate sediment monitoring networks are discussed, and the optimal dimension and spatial distribution are proposed. An optimal network of sixty stations was obtained from an original 153-station sampling campaign.http://www.sciencedirect.com/science/article/B6WJ7-4GX6J7S-8/1/a856112ea58d8f9d6bf0b51627b5e2a22006info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleaplication/PDFhttp://hdl.handle.net/10316/3978http://hdl.handle.net/10316/3978https://doi.org/10.1016/j.jenvman.2005.04.024engJournal of Environmental Management. 78:3 (2006) 294-304Nunes, L. M.Caeiro, S.Cunha, M. C.Ribeiro, L.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2020-11-06T16:49:10Zoai:estudogeral.uc.pt:10316/3978Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:57:14.674737Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Optimal estuarine sediment monitoring network design with simulated annealing
title Optimal estuarine sediment monitoring network design with simulated annealing
spellingShingle Optimal estuarine sediment monitoring network design with simulated annealing
Nunes, L. M.
title_short Optimal estuarine sediment monitoring network design with simulated annealing
title_full Optimal estuarine sediment monitoring network design with simulated annealing
title_fullStr Optimal estuarine sediment monitoring network design with simulated annealing
title_full_unstemmed Optimal estuarine sediment monitoring network design with simulated annealing
title_sort Optimal estuarine sediment monitoring network design with simulated annealing
author Nunes, L. M.
author_facet Nunes, L. M.
Caeiro, S.
Cunha, M. C.
Ribeiro, L.
author_role author
author2 Caeiro, S.
Cunha, M. C.
Ribeiro, L.
author2_role author
author
author
dc.contributor.author.fl_str_mv Nunes, L. M.
Caeiro, S.
Cunha, M. C.
Ribeiro, L.
description An objective function based on geostatistical variance reduction, constrained to the reproduction of the probability distribution functions of selected physical and chemical sediment variables, is applied to the selection of the best set of compliance monitoring stations in the Sado river estuary in Portugal. These stations were to be selected from a large set of sampling stations from a prior field campaign. Simulated annealing was chosen to solve the optimisation function model. Both the combinatorial problem structure and the resulting candidate sediment monitoring networks are discussed, and the optimal dimension and spatial distribution are proposed. An optimal network of sixty stations was obtained from an original 153-station sampling campaign.
publishDate 2006
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/3978
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https://doi.org/10.1016/j.jenvman.2005.04.024
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https://doi.org/10.1016/j.jenvman.2005.04.024
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dc.relation.none.fl_str_mv Journal of Environmental Management. 78:3 (2006) 294-304
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