Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters

In micro-irrigation systems, distinct media filters and filtering materials are employed to remove suspended solids from irrigation water and thereby avoid emitter obstruction. Turbidity is related to suspended solids and dissolved oxygen depends on organic matter load. At this time, no models exist...

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Autores: García Nieto, P. J., García-Gonzalo, E., Arbat Pujolràs, Gerard, Duran i Ros, Miquel, Pujol i Sagaró, Toni, Puig Bargués, Jaume
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
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:10256/24855
Acceso en línea:http://hdl.handle.net/10256/24855
Access Level:acceso abierto
Palabra clave:Regatge per degoteig
Trickle irrigation
Filtres i filtració
Filters and filtration
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spelling Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filtersGarcía Nieto, P. J.García-Gonzalo, E.Arbat Pujolràs, GerardDuran i Ros, MiquelPujol i Sagaró, ToniPuig Bargués, JaumeRegatge per degoteigTrickle irrigationFiltres i filtracióFilters and filtrationIn micro-irrigation systems, distinct media filters and filtering materials are employed to remove suspended solids from irrigation water and thereby avoid emitter obstruction. Turbidity is related to suspended solids and dissolved oxygen depends on organic matter load. At this time, no models exist that are trustworthy enough to forecast the dissolved oxygen and turbidity at the outlet when utilising various media configurations and filter types. The objective of this investigation was to construct a model that can identify turbidity and dissolved oxygen at the filter outlet in advance. This study presents an algorithm for meta-heuristic optimisation inspired by populations termed Differential Evolution (DE) in conjunction with Support Vector Regression (SVR) (DE/SVR-relied model). This is an effective machine learning method, with seven kernel types for calculating the output turbidity (Turbo) and the output dissolved oxygen (DOo) from a dataset comprising 1,016 samples of various reclaimed water-using filter types. The type of media and filter, the height of the filter bed, the cycle duration, and the filtration velocity, as well as the electrical conductivity at the filter inlet, pH, inlet dissolved oxygen, water temperature, and the input turbidity are all tracked and analysed in order to achieve this. The best-fitted DE/SVR-relied model was constructed to predict the Turbo and DOo as well as the input variables' relative importance. Determination coefficients for the best-fitted DE/SVR-relied model for the testing dataset were 0.89 and 0.92 for outlet turbidity (Turbo) and outlet dissolved oxygen (DOo), respectively, showing a good predictive performance which are of great importance for the management of drip irrigation systemsElsevier2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewed15 p.application/pdfhttp://hdl.handle.net/10256/24855http://hdl.handle.net/10256/24855Biosystems Engineering, 2024, vol. 243, p. 42-56García Nieto, P. J. García-Gonzalo, E. Arbat Pujolràs, Gerard Duran i Ros, Miquel Pujol i Sagaró, Toni Puig Bargués, Jaume 2024 Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters Biosystems Engineering 243 42 56reponame: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.biosystemseng.2024.04.020info:eu-repo/semantics/altIdentifier/issn/1537-5110info:eu-repo/semantics/altIdentifier/eissn/1537-5129Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessoai:recercat.cat:10256/248552026-05-29T05:05:01Z
dc.title.none.fl_str_mv Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
title Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
spellingShingle Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
García Nieto, P. J.
Regatge per degoteig
Trickle irrigation
Filtres i filtració
Filters and filtration
title_short Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
title_full Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
title_fullStr Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
title_full_unstemmed Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
title_sort Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
dc.creator.none.fl_str_mv García Nieto, P. J.
García-Gonzalo, E.
Arbat Pujolràs, Gerard
Duran i Ros, Miquel
Pujol i Sagaró, Toni
Puig Bargués, Jaume
author García Nieto, P. J.
author_facet García Nieto, P. J.
García-Gonzalo, E.
Arbat Pujolràs, Gerard
Duran i Ros, Miquel
Pujol i Sagaró, Toni
Puig Bargués, Jaume
author_role author
author2 García-Gonzalo, E.
Arbat Pujolràs, Gerard
Duran i Ros, Miquel
Pujol i Sagaró, Toni
Puig Bargués, Jaume
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 In micro-irrigation systems, distinct media filters and filtering materials are employed to remove suspended solids from irrigation water and thereby avoid emitter obstruction. Turbidity is related to suspended solids and dissolved oxygen depends on organic matter load. At this time, no models exist that are trustworthy enough to forecast the dissolved oxygen and turbidity at the outlet when utilising various media configurations and filter types. The objective of this investigation was to construct a model that can identify turbidity and dissolved oxygen at the filter outlet in advance. This study presents an algorithm for meta-heuristic optimisation inspired by populations termed Differential Evolution (DE) in conjunction with Support Vector Regression (SVR) (DE/SVR-relied model). This is an effective machine learning method, with seven kernel types for calculating the output turbidity (Turbo) and the output dissolved oxygen (DOo) from a dataset comprising 1,016 samples of various reclaimed water-using filter types. The type of media and filter, the height of the filter bed, the cycle duration, and the filtration velocity, as well as the electrical conductivity at the filter inlet, pH, inlet dissolved oxygen, water temperature, and the input turbidity are all tracked and analysed in order to achieve this. The best-fitted DE/SVR-relied model was constructed to predict the Turbo and DOo as well as the input variables' relative importance. Determination coefficients for the best-fitted DE/SVR-relied model for the testing dataset were 0.89 and 0.92 for outlet turbidity (Turbo) and outlet dissolved oxygen (DOo), respectively, showing a good predictive performance which are of great importance for the management of drip irrigation systems
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/24855
http://hdl.handle.net/10256/24855
url http://hdl.handle.net/10256/24855
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.biosystemseng.2024.04.020
info:eu-repo/semantics/altIdentifier/issn/1537-5110
info:eu-repo/semantics/altIdentifier/eissn/1537-5129
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 15 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Biosystems Engineering, 2024, vol. 243, p. 42-56
García Nieto, P. J. García-Gonzalo, E. Arbat Pujolràs, Gerard Duran i Ros, Miquel Pujol i Sagaró, Toni Puig Bargués, Jaume 2024 Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters Biosystems Engineering 243 42 56
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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