Clustering and forecasting of dissolved oxygen concentration on a river basin
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/8422 |
Resumo: | The aim of this contribution is to combine statistical methodologies to geographically classify homogeneous groups of water quality monitoring sites based on similarities in the temporal dynamics of the dissolved oxygen (DO) concentration, in order to obtain accurate forecasts of this quality variable. Our methodology intends to classify the water quality monitoring sites into spatial homogeneous groups, based on the DO concentration, which has been selected and considered relevant to characterize the water quality. We apply clustering techniques based on Kullback Information, measures that are obtained in the state space modelling process. For each homogeneous group of water quality monitoring sites we model the DO concentration using linear and state space models, which incorporate tendency and seasonality components in different ways. Both approaches are compared by the mean squared error (MSE) of forecasts. |
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Clustering and forecasting of dissolved oxygen concentration on a river basinHydrological basinWater qualityKalman filterLinear modelState space modelClusteringThe aim of this contribution is to combine statistical methodologies to geographically classify homogeneous groups of water quality monitoring sites based on similarities in the temporal dynamics of the dissolved oxygen (DO) concentration, in order to obtain accurate forecasts of this quality variable. Our methodology intends to classify the water quality monitoring sites into spatial homogeneous groups, based on the DO concentration, which has been selected and considered relevant to characterize the water quality. We apply clustering techniques based on Kullback Information, measures that are obtained in the state space modelling process. For each homogeneous group of water quality monitoring sites we model the DO concentration using linear and state space models, which incorporate tendency and seasonality components in different ways. Both approaches are compared by the mean squared error (MSE) of forecasts.Springer Verlag2012-05-02T14:10:54Z2011-01-01T00:00:00Z2011info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/8422eng1436-324010.1007/s00477-010-0429-5Costa, MarcoGoncalves, A. Manuelainfo: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:RCAAP2023-07-17T03:24:21ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
Clustering and forecasting of dissolved oxygen concentration on a river basin |
title |
Clustering and forecasting of dissolved oxygen concentration on a river basin |
spellingShingle |
Clustering and forecasting of dissolved oxygen concentration on a river basin Costa, Marco Hydrological basin Water quality Kalman filter Linear model State space model Clustering |
title_short |
Clustering and forecasting of dissolved oxygen concentration on a river basin |
title_full |
Clustering and forecasting of dissolved oxygen concentration on a river basin |
title_fullStr |
Clustering and forecasting of dissolved oxygen concentration on a river basin |
title_full_unstemmed |
Clustering and forecasting of dissolved oxygen concentration on a river basin |
title_sort |
Clustering and forecasting of dissolved oxygen concentration on a river basin |
author |
Costa, Marco |
author_facet |
Costa, Marco Goncalves, A. Manuela |
author_role |
author |
author2 |
Goncalves, A. Manuela |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Costa, Marco Goncalves, A. Manuela |
dc.subject.por.fl_str_mv |
Hydrological basin Water quality Kalman filter Linear model State space model Clustering |
topic |
Hydrological basin Water quality Kalman filter Linear model State space model Clustering |
description |
The aim of this contribution is to combine statistical methodologies to geographically classify homogeneous groups of water quality monitoring sites based on similarities in the temporal dynamics of the dissolved oxygen (DO) concentration, in order to obtain accurate forecasts of this quality variable. Our methodology intends to classify the water quality monitoring sites into spatial homogeneous groups, based on the DO concentration, which has been selected and considered relevant to characterize the water quality. We apply clustering techniques based on Kullback Information, measures that are obtained in the state space modelling process. For each homogeneous group of water quality monitoring sites we model the DO concentration using linear and state space models, which incorporate tendency and seasonality components in different ways. Both approaches are compared by the mean squared error (MSE) of forecasts. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-01-01T00:00:00Z 2011 2012-05-02T14:10:54Z |
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/10773/8422 |
url |
http://hdl.handle.net/10773/8422 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1436-3240 10.1007/s00477-010-0429-5 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer Verlag |
publisher.none.fl_str_mv |
Springer Verlag |
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 |
|
repository.mail.fl_str_mv |
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1777303479218339840 |