Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks
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/10174/3300 |
Resumo: | The Health Surveillance Program was established by the Health Authority to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups (P1, P2 and P3) for which the sampling frequency is different. Thus, the development of models is important to predict the chemical parameters included in group P2 (nitrates and manganese) and included in group P3 (sodium and potassium), for which the sampling frequency is lower, based on the chemical parameters included in group P1 (pH and conductivity). In the present work, Artificial Neural Networks (ANNs) were used to predict the concentration of nitrates, manganese, sodium and potassium from pH and conductivity. The neural network selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R2 values varying in the range 0.9960-0.9989 for training set and 0.9993-0.9952 for test set. |
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Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural NetworksArtificial Neural NetworksPredictionPublic Water SupplyWater Quality ParametersThe Health Surveillance Program was established by the Health Authority to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups (P1, P2 and P3) for which the sampling frequency is different. Thus, the development of models is important to predict the chemical parameters included in group P2 (nitrates and manganese) and included in group P3 (sodium and potassium), for which the sampling frequency is lower, based on the chemical parameters included in group P1 (pH and conductivity). In the present work, Artificial Neural Networks (ANNs) were used to predict the concentration of nitrates, manganese, sodium and potassium from pH and conductivity. The neural network selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R2 values varying in the range 0.9960-0.9989 for training set and 0.9993-0.9952 for test set.2012-01-11T12:44:19Z2012-01-112011-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/3300http://hdl.handle.net/10174/3300engVicente, H., Dias, S. & Neves, J., Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks. Proceedings of WATER & INDUSTRY 2011 – International Water Association Specialist Conference, pp. 42, University of Valladolid Edition, Valladolid, Spain, 2011.42QUIhvicente@uevora.ptsusana.dias@arsalentejo.min-saude.ptjneves@di.uminho.ptWATER & INDUSTRY 2011 - IWA Specialist Conference239Vicente, HenriqueDias, SusanaNeves, Joséinfo: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:RCAAP2024-01-03T18:40:27Zoai:dspace.uevora.pt:10174/3300Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:58:48.655252Repositório 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 |
Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks |
title |
Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks |
spellingShingle |
Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks Vicente, Henrique Artificial Neural Networks Prediction Public Water Supply Water Quality Parameters |
title_short |
Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks |
title_full |
Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks |
title_fullStr |
Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks |
title_full_unstemmed |
Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks |
title_sort |
Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks |
author |
Vicente, Henrique |
author_facet |
Vicente, Henrique Dias, Susana Neves, José |
author_role |
author |
author2 |
Dias, Susana Neves, José |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Vicente, Henrique Dias, Susana Neves, José |
dc.subject.por.fl_str_mv |
Artificial Neural Networks Prediction Public Water Supply Water Quality Parameters |
topic |
Artificial Neural Networks Prediction Public Water Supply Water Quality Parameters |
description |
The Health Surveillance Program was established by the Health Authority to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups (P1, P2 and P3) for which the sampling frequency is different. Thus, the development of models is important to predict the chemical parameters included in group P2 (nitrates and manganese) and included in group P3 (sodium and potassium), for which the sampling frequency is lower, based on the chemical parameters included in group P1 (pH and conductivity). In the present work, Artificial Neural Networks (ANNs) were used to predict the concentration of nitrates, manganese, sodium and potassium from pH and conductivity. The neural network selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R2 values varying in the range 0.9960-0.9989 for training set and 0.9993-0.9952 for test set. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-01-01T00:00:00Z 2012-01-11T12:44:19Z 2012-01-11 |
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/10174/3300 http://hdl.handle.net/10174/3300 |
url |
http://hdl.handle.net/10174/3300 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Vicente, H., Dias, S. & Neves, J., Modelling of Public Water Supply Quality in the District of Évora Using Artificial Neural Networks. Proceedings of WATER & INDUSTRY 2011 – International Water Association Specialist Conference, pp. 42, University of Valladolid Edition, Valladolid, Spain, 2011. 42 QUI hvicente@uevora.pt susana.dias@arsalentejo.min-saude.pt jneves@di.uminho.pt WATER & INDUSTRY 2011 - IWA Specialist Conference 239 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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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1799136470986915840 |