A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression

Sand media filters used in microirrigation systems must remove suspended particle load for avoiding emitter physical clogging. Turbidity is a parameter related to suspended particle load that it is easy and quick to measure and it is also included in some guidelines for reusing effluents in irrigati...

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Autores: García Nieto, P. J., García-Gonzalo, E., Puig Bargués, Jaume, Solé Torres, Carles, Duran i Ros, Miquel, Arbat Pujolràs, Gerard
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/24120
Acceso en línea:http://hdl.handle.net/10256/24120
Access Level:acceso abierto
Palabra clave:Regatge per degoteig
Trickle irrigation
Filtres i filtració
Filters and filtration
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spelling A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regressionGarcía Nieto, P. J.García-Gonzalo, E.Puig Bargués, JaumeSolé Torres, CarlesDuran i Ros, MiquelArbat Pujolràs, GerardRegatge per degoteigTrickle irrigationFiltres i filtracióFilters and filtrationSand media filters used in microirrigation systems must remove suspended particle load for avoiding emitter physical clogging. Turbidity is a parameter related to suspended particle load that it is easy and quick to measure and it is also included in some guidelines for reusing effluents in irrigation. Currently, there are not sufficiently accurate models available to predict outlet turbidity for sand filters, which would be useful for both irrigators and engineers. The aim of this study was to obtain a predictive model able to perform an early detection of the sand filter outlet value of turbidity. This study presents a powerful and effective Bayesian nonparametric approach, termed Gaussian process regression (GPR) model, for predicting the output turbidity (Turbo) from data corresponding to 637 samples of different sand filters using reclaimed effluent. This optimization technique involves kernel parameter setting in the GPR training procedure, which significantly influences the regression accuracy. To this end, the most important parameters of this process are monitored and analyzed: type of filter, height of the filter bed (H), filtration velocity (v) and filter inlet values of the electrical conductivity (CEi), dissolved oxygen (DOi), pHi, turbidity (Turbi) and water temperature (Ti). The results of the present study are two-fold. In the first place, the significance of each variable on the filtration is presented through the model. Secondly, a model for forecasting the outlet turbidity was obtained with success. Indeed, regression with optimal hyperparameters was performed and a coefficient of determination equal to 0.8921 for outlet turbidity was obtained when this new predictive GPR-based model was applied to the experimental dataset. The agreement between experimental data and the model confirmed the good performance of the latterElsevier2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/24120http://hdl.handle.net/10256/24120© Computers and Electronics in Agriculture, 2020, vol. 170, art. núm. 105292Articles publicats (D-EQATA)García Nieto, P. J. García-Gonzalo, E. Puig Bargués, Jaume Solé Torres, Carles Duran i Ros, Miquel Arbat Pujolràs, Gerard 2020 A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression Computers and Electronics in Agriculture 170 art.núm.105292reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2020.105292info:eu-repo/semantics/altIdentifier/issn/0168-1699info:eu-repo/semantics/altIdentifier/eissn/1872-7107Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessoai:recercat.cat:10256/241202026-05-29T05:05:01Z
dc.title.none.fl_str_mv A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression
title A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression
spellingShingle A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression
García Nieto, P. J.
Regatge per degoteig
Trickle irrigation
Filtres i filtració
Filters and filtration
title_short A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression
title_full A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression
title_fullStr A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression
title_full_unstemmed A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression
title_sort A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression
dc.creator.none.fl_str_mv García Nieto, P. J.
García-Gonzalo, E.
Puig Bargués, Jaume
Solé Torres, Carles
Duran i Ros, Miquel
Arbat Pujolràs, Gerard
author García Nieto, P. J.
author_facet García Nieto, P. J.
García-Gonzalo, E.
Puig Bargués, Jaume
Solé Torres, Carles
Duran i Ros, Miquel
Arbat Pujolràs, Gerard
author_role author
author2 García-Gonzalo, E.
Puig Bargués, Jaume
Solé Torres, Carles
Duran i Ros, Miquel
Arbat Pujolràs, Gerard
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Regatge per degoteig
Trickle irrigation
Filtres i filtració
Filters and filtration
topic Regatge per degoteig
Trickle irrigation
Filtres i filtració
Filters and filtration
description Sand media filters used in microirrigation systems must remove suspended particle load for avoiding emitter physical clogging. Turbidity is a parameter related to suspended particle load that it is easy and quick to measure and it is also included in some guidelines for reusing effluents in irrigation. Currently, there are not sufficiently accurate models available to predict outlet turbidity for sand filters, which would be useful for both irrigators and engineers. The aim of this study was to obtain a predictive model able to perform an early detection of the sand filter outlet value of turbidity. This study presents a powerful and effective Bayesian nonparametric approach, termed Gaussian process regression (GPR) model, for predicting the output turbidity (Turbo) from data corresponding to 637 samples of different sand filters using reclaimed effluent. This optimization technique involves kernel parameter setting in the GPR training procedure, which significantly influences the regression accuracy. To this end, the most important parameters of this process are monitored and analyzed: type of filter, height of the filter bed (H), filtration velocity (v) and filter inlet values of the electrical conductivity (CEi), dissolved oxygen (DOi), pHi, turbidity (Turbi) and water temperature (Ti). The results of the present study are two-fold. In the first place, the significance of each variable on the filtration is presented through the model. Secondly, a model for forecasting the outlet turbidity was obtained with success. Indeed, regression with optimal hyperparameters was performed and a coefficient of determination equal to 0.8921 for outlet turbidity was obtained when this new predictive GPR-based model was applied to the experimental dataset. The agreement between experimental data and the model confirmed the good performance of the latter
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
peer-reviewed
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/24120
http://hdl.handle.net/10256/24120
url http://hdl.handle.net/10256/24120
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2020.105292
info:eu-repo/semantics/altIdentifier/issn/0168-1699
info:eu-repo/semantics/altIdentifier/eissn/1872-7107
dc.rights.none.fl_str_mv Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv © Computers and Electronics in Agriculture, 2020, vol. 170, art. núm. 105292
Articles publicats (D-EQATA)
García Nieto, P. J. García-Gonzalo, E. Puig Bargués, Jaume Solé Torres, Carles Duran i Ros, Miquel Arbat Pujolràs, Gerard 2020 A new predictive model for the outlet turbidity in micro-irrigation sand filters fed with effluents using Gaussian process regression Computers and Electronics in Agriculture 170 art.núm.105292
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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