Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet

Autores
Gauto, Víctor Hugo; Utgés, Enid Marta; Hervot, Elsa Ivonne; Tenev, María Daniela; Farías, Alejandro Rubén
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Water availability and sanitation are among the UN Sustainable Development goals for 2030. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. Clean water is a scarce resource fundamental for human development and well-being. Treatment plants depend on the current water quality state to properly provide clean water. Combining laboratory measurements, provided by a water plant in Resistencia city, Argentina, and remote sensing data, i.e., surface reflectance, from Sentinel-2 platform, several algorithms were developed, trained, and compared for turbidity estimation. The model with the highest performance metrics was a random forest model, with Pearson’s coefficient of determination (R2) 0.918 and root-mean squared error (RMSE) 138.8 nephelometric turbidity units (NTU). Global feature importance and partial dependencies profiles techniques were applied to the random forest model to understand the spectral bands effects. Turbidity maps and time series were made and analyzed.
Fil: Gauto, Víctor Hugo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Fil: Utgés, Enid Marta. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Fil: Hervot, Elsa Ivonne. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Fil: Tenev, María Daniela. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Fil: Farías, Alejandro Rubén. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Peer Reviewed
Materia
machine learning
random forest
remote sensing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-10-07T18:29:47Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/11589
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inletGauto, Víctor HugoUtgés, Enid MartaHervot, Elsa IvonneTenev, María DanielaFarías, Alejandro Rubénmachine learningrandom forestremote sensingWater availability and sanitation are among the UN Sustainable Development goals for 2030. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. Clean water is a scarce resource fundamental for human development and well-being. Treatment plants depend on the current water quality state to properly provide clean water. Combining laboratory measurements, provided by a water plant in Resistencia city, Argentina, and remote sensing data, i.e., surface reflectance, from Sentinel-2 platform, several algorithms were developed, trained, and compared for turbidity estimation. The model with the highest performance metrics was a random forest model, with Pearson’s coefficient of determination (R2) 0.918 and root-mean squared error (RMSE) 138.8 nephelometric turbidity units (NTU). Global feature importance and partial dependencies profiles techniques were applied to the random forest model to understand the spectral bands effects. Turbidity maps and time series were made and analyzed.Fil: Gauto, Víctor Hugo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.Fil: Utgés, Enid Marta. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.Fil: Hervot, Elsa Ivonne. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.Fil: Tenev, María Daniela. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.Fil: Farías, Alejandro Rubén. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.Peer Reviewed2024-10-07T18:29:47Z2024-10-07T18:29:47Z2024-01-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdf4th LA Sustainable Development of Energy Water and Environment Systems Conference; SDEWES 20242706-3674http://hdl.handle.net/20.500.12272/11589engEstimar indicadores de calidad de agua en la cuenca media del río Paraná para el desarrollo de un algoritmo mediante técnicas de teledetección satelital.MSECRE0008604info:eu-repo/semantics/openAccess2024-10-07T18:29:47Zhttp://creativecommons.org/licenses/by-nc-sa/4.0/Atribución-NoComercial-CompartirIgual 4.0 InternacionalAcceso abiertoreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-10-01T11:57:28Zoai:ria.utn.edu.ar:20.500.12272/11589instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-10-01 11:57:28.727Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet
title Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet
spellingShingle Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet
Gauto, Víctor Hugo
machine learning
random forest
remote sensing
title_short Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet
title_full Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet
title_fullStr Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet
title_full_unstemmed Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet
title_sort Turbidity estimation by machine learning modeling and remote sensing techniques applied to a treatment plant water inlet
dc.creator.none.fl_str_mv Gauto, Víctor Hugo
Utgés, Enid Marta
Hervot, Elsa Ivonne
Tenev, María Daniela
Farías, Alejandro Rubén
author Gauto, Víctor Hugo
author_facet Gauto, Víctor Hugo
Utgés, Enid Marta
Hervot, Elsa Ivonne
Tenev, María Daniela
Farías, Alejandro Rubén
author_role author
author2 Utgés, Enid Marta
Hervot, Elsa Ivonne
Tenev, María Daniela
Farías, Alejandro Rubén
author2_role author
author
author
author
dc.subject.none.fl_str_mv machine learning
random forest
remote sensing
topic machine learning
random forest
remote sensing
dc.description.none.fl_txt_mv Water availability and sanitation are among the UN Sustainable Development goals for 2030. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. Clean water is a scarce resource fundamental for human development and well-being. Treatment plants depend on the current water quality state to properly provide clean water. Combining laboratory measurements, provided by a water plant in Resistencia city, Argentina, and remote sensing data, i.e., surface reflectance, from Sentinel-2 platform, several algorithms were developed, trained, and compared for turbidity estimation. The model with the highest performance metrics was a random forest model, with Pearson’s coefficient of determination (R2) 0.918 and root-mean squared error (RMSE) 138.8 nephelometric turbidity units (NTU). Global feature importance and partial dependencies profiles techniques were applied to the random forest model to understand the spectral bands effects. Turbidity maps and time series were made and analyzed.
Fil: Gauto, Víctor Hugo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Fil: Utgés, Enid Marta. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Fil: Hervot, Elsa Ivonne. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Fil: Tenev, María Daniela. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Fil: Farías, Alejandro Rubén. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo de Investigación Sobre Temas Ambientales y Químicos; Argentina.
Peer Reviewed
description Water availability and sanitation are among the UN Sustainable Development goals for 2030. Remote sensing techniques are used to monitor and retrieve quality estimators from water bodies. Clean water is a scarce resource fundamental for human development and well-being. Treatment plants depend on the current water quality state to properly provide clean water. Combining laboratory measurements, provided by a water plant in Resistencia city, Argentina, and remote sensing data, i.e., surface reflectance, from Sentinel-2 platform, several algorithms were developed, trained, and compared for turbidity estimation. The model with the highest performance metrics was a random forest model, with Pearson’s coefficient of determination (R2) 0.918 and root-mean squared error (RMSE) 138.8 nephelometric turbidity units (NTU). Global feature importance and partial dependencies profiles techniques were applied to the random forest model to understand the spectral bands effects. Turbidity maps and time series were made and analyzed.
publishDate 2024
dc.date.none.fl_str_mv 2024-10-07T18:29:47Z
2024-10-07T18:29:47Z
2024-01-14
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 4th LA Sustainable Development of Energy Water and Environment Systems Conference; SDEWES 2024
2706-3674
http://hdl.handle.net/20.500.12272/11589
identifier_str_mv 4th LA Sustainable Development of Energy Water and Environment Systems Conference; SDEWES 2024
2706-3674
url http://hdl.handle.net/20.500.12272/11589
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Estimar indicadores de calidad de agua en la cuenca media del río Paraná para el desarrollo de un algoritmo mediante técnicas de teledetección satelital.
MSECRE0008604
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-10-07T18:29:47Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Acceso abierto
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-10-07T18:29:47Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Acceso abierto
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.364332

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