Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant

Autores
Gauto, Víctor Hugo; Utgés, Enid Marta; Hervot, Elsa Ivonne; Tenev, María Daniela; Farías, Alejandro R.
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Clean water is a scarce resource, fundamental for human development and well-being. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. In situ sampling is an essential and labour-intensive task with high costs. As an alternative, a large water quality dataset from a potabilisation plant can be beneficial to this step. Combining laboratory measurements from a water treatment plant in North-East Argentina and spectral data from the Sentinel-2 satellite platform, several regression algorithms were proposed, trained, and compared for turbidity estimation at the plant inlet water in a local river. The highest performance metrics were from a Random Forest model with a coefficient of determination close to 1 (0.913) and the lowest root-mean-squared error (143.9 nephelometric turbidity units). Global feature importance and partial dependencies profile techniques identified the most influential spectral bands. Maps and histograms were made to explore the spatial distribution of turbidity.
Fil: Gauto, Víctor Hugo. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano. Universidad Nacional del Nordeste. Facultad de Arquitectura y Urbanismo. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano.; Argentina
Fil: Utgés, Enid Marta. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina
Fil: Hervot, Elsa Ivonne. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina
Fil: Tenev, María Daniela. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina
Fil: Farías, Alejandro R.. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina
Materia
RANDOM FOREST
REMOTE SENSING
SENTINEL-2
TURBIDITY
WATER QUALITY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/274809
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment PlantGauto, Víctor HugoUtgés, Enid MartaHervot, Elsa IvonneTenev, María DanielaFarías, Alejandro R.RANDOM FORESTREMOTE SENSINGSENTINEL-2TURBIDITYWATER QUALITYhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Clean water is a scarce resource, fundamental for human development and well-being. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. In situ sampling is an essential and labour-intensive task with high costs. As an alternative, a large water quality dataset from a potabilisation plant can be beneficial to this step. Combining laboratory measurements from a water treatment plant in North-East Argentina and spectral data from the Sentinel-2 satellite platform, several regression algorithms were proposed, trained, and compared for turbidity estimation at the plant inlet water in a local river. The highest performance metrics were from a Random Forest model with a coefficient of determination close to 1 (0.913) and the lowest root-mean-squared error (143.9 nephelometric turbidity units). Global feature importance and partial dependencies profile techniques identified the most influential spectral bands. Maps and histograms were made to explore the spatial distribution of turbidity.Fil: Gauto, Víctor Hugo. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano. Universidad Nacional del Nordeste. Facultad de Arquitectura y Urbanismo. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano.; ArgentinaFil: Utgés, Enid Marta. Universidad Tecnológica Nacional. Facultad Regional Resistencia; ArgentinaFil: Hervot, Elsa Ivonne. Universidad Tecnológica Nacional. Facultad Regional Resistencia; ArgentinaFil: Tenev, María Daniela. Universidad Tecnológica Nacional. Facultad Regional Resistencia; ArgentinaFil: Farías, Alejandro R.. Universidad Tecnológica Nacional. Facultad Regional Resistencia; ArgentinaThe International Centre for Sustainable Development of Energy, Water and Environment Systems2025-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/274809Gauto, Víctor Hugo; Utgés, Enid Marta; Hervot, Elsa Ivonne; Tenev, María Daniela; Farías, Alejandro R.; Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant; The International Centre for Sustainable Development of Energy, Water and Environment Systems; Journal of Sustainable Development of Energy, Water and Environment Systems; 13; 2; 6-2025; 1-171848-9257CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sdewes.org/jsdewes/pid13.0539info:eu-repo/semantics/altIdentifier/doi/10.13044/j.sdewes.d13.0539info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-12-03T09:45:16Zoai:ri.conicet.gov.ar:11336/274809instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-12-03 09:45:17.162CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant
title Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant
spellingShingle Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant
Gauto, Víctor Hugo
RANDOM FOREST
REMOTE SENSING
SENTINEL-2
TURBIDITY
WATER QUALITY
title_short Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant
title_full Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant
title_fullStr Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant
title_full_unstemmed Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant
title_sort Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant
dc.creator.none.fl_str_mv Gauto, Víctor Hugo
Utgés, Enid Marta
Hervot, Elsa Ivonne
Tenev, María Daniela
Farías, Alejandro R.
author Gauto, Víctor Hugo
author_facet Gauto, Víctor Hugo
Utgés, Enid Marta
Hervot, Elsa Ivonne
Tenev, María Daniela
Farías, Alejandro R.
author_role author
author2 Utgés, Enid Marta
Hervot, Elsa Ivonne
Tenev, María Daniela
Farías, Alejandro R.
author2_role author
author
author
author
dc.subject.none.fl_str_mv RANDOM FOREST
REMOTE SENSING
SENTINEL-2
TURBIDITY
WATER QUALITY
topic RANDOM FOREST
REMOTE SENSING
SENTINEL-2
TURBIDITY
WATER QUALITY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Clean water is a scarce resource, fundamental for human development and well-being. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. In situ sampling is an essential and labour-intensive task with high costs. As an alternative, a large water quality dataset from a potabilisation plant can be beneficial to this step. Combining laboratory measurements from a water treatment plant in North-East Argentina and spectral data from the Sentinel-2 satellite platform, several regression algorithms were proposed, trained, and compared for turbidity estimation at the plant inlet water in a local river. The highest performance metrics were from a Random Forest model with a coefficient of determination close to 1 (0.913) and the lowest root-mean-squared error (143.9 nephelometric turbidity units). Global feature importance and partial dependencies profile techniques identified the most influential spectral bands. Maps and histograms were made to explore the spatial distribution of turbidity.
Fil: Gauto, Víctor Hugo. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano. Universidad Nacional del Nordeste. Facultad de Arquitectura y Urbanismo. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano.; Argentina
Fil: Utgés, Enid Marta. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina
Fil: Hervot, Elsa Ivonne. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina
Fil: Tenev, María Daniela. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina
Fil: Farías, Alejandro R.. Universidad Tecnológica Nacional. Facultad Regional Resistencia; Argentina
description Clean water is a scarce resource, fundamental for human development and well-being. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. In situ sampling is an essential and labour-intensive task with high costs. As an alternative, a large water quality dataset from a potabilisation plant can be beneficial to this step. Combining laboratory measurements from a water treatment plant in North-East Argentina and spectral data from the Sentinel-2 satellite platform, several regression algorithms were proposed, trained, and compared for turbidity estimation at the plant inlet water in a local river. The highest performance metrics were from a Random Forest model with a coefficient of determination close to 1 (0.913) and the lowest root-mean-squared error (143.9 nephelometric turbidity units). Global feature importance and partial dependencies profile techniques identified the most influential spectral bands. Maps and histograms were made to explore the spatial distribution of turbidity.
publishDate 2025
dc.date.none.fl_str_mv 2025-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/274809
Gauto, Víctor Hugo; Utgés, Enid Marta; Hervot, Elsa Ivonne; Tenev, María Daniela; Farías, Alejandro R.; Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant; The International Centre for Sustainable Development of Energy, Water and Environment Systems; Journal of Sustainable Development of Energy, Water and Environment Systems; 13; 2; 6-2025; 1-17
1848-9257
CONICET Digital
CONICET
url http://hdl.handle.net/11336/274809
identifier_str_mv Gauto, Víctor Hugo; Utgés, Enid Marta; Hervot, Elsa Ivonne; Tenev, María Daniela; Farías, Alejandro R.; Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant; The International Centre for Sustainable Development of Energy, Water and Environment Systems; Journal of Sustainable Development of Energy, Water and Environment Systems; 13; 2; 6-2025; 1-17
1848-9257
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sdewes.org/jsdewes/pid13.0539
info:eu-repo/semantics/altIdentifier/doi/10.13044/j.sdewes.d13.0539
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv The International Centre for Sustainable Development of Energy, Water and Environment Systems
publisher.none.fl_str_mv The International Centre for Sustainable Development of Energy, Water and Environment Systems
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1850505884477685760
score 13.214268

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