A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)

Detalhes bibliográficos
Autor(a) principal: Nunes, Jorge
Data de Publicação: 2012
Outros Autores: Madeira, Manuel, Gazarini, Luiz, Neves, José, Vicente, Henrique
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/3624
https://doi.org/10.1007/s10457-011-9416-1
Resumo: The changes in the soil nitrate concentration were studied during 2 years in a ‘‘montado’’ ecosystem, in the South of Portugal. Total rainfall, air and soil temperature and soil water content under and outside Quercus rotundifolia canopy were also evaluated. A cluster analysis was carried out using climatic and microclimatic parameters in order to maximize the intraclass similarity and minimize the interclass similarity. It was used the k-Means Clustering Method. Several cluster models were developed using k values ranging between 2 and 5. Thereafter, in each cluster, the data were divided according to their origin (soil under canopy and open areas, and from surface and deep layers). Multiple regression models were tested for each cluster, to assess the relationship between soil nitrate concentration and a set of climatic and microclimatic parameters and the results were compared with models assessed without clustering. The models achieved with data grouped in result of clustering analysis showed better performance than the models achieved without clustering, mostly for data from open areas soils. When temperature is low and/or water presents excess or scarcity levels, the data from soils in undercanopy areas, give rise to models with worst performance than models from open soil areas data. The results obtained for undercanopy area suggest that nitrification process in soil under Quercus rotundifolia trees influence is more complex than for open areas, and subject to other relevant factors beyond water and temperature.
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spelling A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)Decision treesk-meansNitrogenMediterranean oak woodlandsThe changes in the soil nitrate concentration were studied during 2 years in a ‘‘montado’’ ecosystem, in the South of Portugal. Total rainfall, air and soil temperature and soil water content under and outside Quercus rotundifolia canopy were also evaluated. A cluster analysis was carried out using climatic and microclimatic parameters in order to maximize the intraclass similarity and minimize the interclass similarity. It was used the k-Means Clustering Method. Several cluster models were developed using k values ranging between 2 and 5. Thereafter, in each cluster, the data were divided according to their origin (soil under canopy and open areas, and from surface and deep layers). Multiple regression models were tested for each cluster, to assess the relationship between soil nitrate concentration and a set of climatic and microclimatic parameters and the results were compared with models assessed without clustering. The models achieved with data grouped in result of clustering analysis showed better performance than the models achieved without clustering, mostly for data from open areas soils. When temperature is low and/or water presents excess or scarcity levels, the data from soils in undercanopy areas, give rise to models with worst performance than models from open soil areas data. The results obtained for undercanopy area suggest that nitrification process in soil under Quercus rotundifolia trees influence is more complex than for open areas, and subject to other relevant factors beyond water and temperature.Springer2012-01-16T14:33:58Z2012-01-162012-01-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/3624http://hdl.handle.net/10174/3624https://doi.org/10.1007/s10457-011-9416-1engAgroforest Syst (2012) 84:89–10089-10084Agroforest Syst1ICAAMjdnunes@uevora.ptmavmadeira@isa.utl.ptgazarini@uevora.ptjneves@di.uminho.pthvicente@uevora.pt587Nunes, JorgeMadeira, ManuelGazarini, LuizNeves, JoséVicente, Henriqueinfo: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:RCAAP2024-01-03T18:40:59Zoai:dspace.uevora.pt:10174/3624Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:59:04.269760Repositório 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 A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)
title A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)
spellingShingle A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)
Nunes, Jorge
Decision trees
k-means
Nitrogen
Mediterranean oak woodlands
title_short A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)
title_full A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)
title_fullStr A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)
title_full_unstemmed A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)
title_sort A data mining approach to improve multiple regression models of soil nitrate concentration predictions in Quercus rotundifolia montados (Portugal)
author Nunes, Jorge
author_facet Nunes, Jorge
Madeira, Manuel
Gazarini, Luiz
Neves, José
Vicente, Henrique
author_role author
author2 Madeira, Manuel
Gazarini, Luiz
Neves, José
Vicente, Henrique
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Nunes, Jorge
Madeira, Manuel
Gazarini, Luiz
Neves, José
Vicente, Henrique
dc.subject.por.fl_str_mv Decision trees
k-means
Nitrogen
Mediterranean oak woodlands
topic Decision trees
k-means
Nitrogen
Mediterranean oak woodlands
description The changes in the soil nitrate concentration were studied during 2 years in a ‘‘montado’’ ecosystem, in the South of Portugal. Total rainfall, air and soil temperature and soil water content under and outside Quercus rotundifolia canopy were also evaluated. A cluster analysis was carried out using climatic and microclimatic parameters in order to maximize the intraclass similarity and minimize the interclass similarity. It was used the k-Means Clustering Method. Several cluster models were developed using k values ranging between 2 and 5. Thereafter, in each cluster, the data were divided according to their origin (soil under canopy and open areas, and from surface and deep layers). Multiple regression models were tested for each cluster, to assess the relationship between soil nitrate concentration and a set of climatic and microclimatic parameters and the results were compared with models assessed without clustering. The models achieved with data grouped in result of clustering analysis showed better performance than the models achieved without clustering, mostly for data from open areas soils. When temperature is low and/or water presents excess or scarcity levels, the data from soils in undercanopy areas, give rise to models with worst performance than models from open soil areas data. The results obtained for undercanopy area suggest that nitrification process in soil under Quercus rotundifolia trees influence is more complex than for open areas, and subject to other relevant factors beyond water and temperature.
publishDate 2012
dc.date.none.fl_str_mv 2012-01-16T14:33:58Z
2012-01-16
2012-01-12T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10174/3624
http://hdl.handle.net/10174/3624
https://doi.org/10.1007/s10457-011-9416-1
url http://hdl.handle.net/10174/3624
https://doi.org/10.1007/s10457-011-9416-1
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Agroforest Syst (2012) 84:89–100
89-100
84
Agroforest Syst
1
ICAAM
jdnunes@uevora.pt
mavmadeira@isa.utl.pt
gazarini@uevora.pt
jneves@di.uminho.pt
hvicente@uevora.pt
587
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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