Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques

The presence and persistence of pesticides in the environment are environmental problems of great concern due to the health implications for humans and wildlife. The persistence of DDT–DDE in a Mediterranean coastal plain where pesticides were widely used and were banned decades ago is the aim of th...

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Autores: Melendez-Pastor, Ignacio, Navarro-Pedreño, Jose, López Granado, Otoniel Mario, Hernández, Encarni I., JORDAN VIDAL, MANUEL MIGUEL, Gómez Lucas, Ignacio
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Miguel Hernández de Elche
Repositorio:REDIUMH. Depósito Digital de la UMH
OAI Identifier:oai:dspace.umh.es:11000/34552
Acceso en línea:https://hdl.handle.net/11000/34552
Access Level:acceso abierto
Palabra clave:DDT
DDE
Spatial distribution
Soil texture
Hydrology
Random forest
Mutual information
CDU::6 - Ciencias aplicadas::63 - Agricultura. Silvicultura. Zootecnia. Caza. Pesca::631 - Agricultura. Agronomía. Maquinaria agrícola. Suelos. Edafología agrícola
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spelling Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniquesMelendez-Pastor, IgnacioNavarro-Pedreño, JoseLópez Granado, Otoniel MarioHernández, Encarni I.JORDAN VIDAL, MANUEL MIGUELGómez Lucas, IgnacioDDTDDESpatial distributionSoil textureHydrologyRandom forestMutual informationCDU::6 - Ciencias aplicadas::63 - Agricultura. Silvicultura. Zootecnia. Caza. Pesca::631 - Agricultura. Agronomía. Maquinaria agrícola. Suelos. Edafología agrícolaThe presence and persistence of pesticides in the environment are environmental problems of great concern due to the health implications for humans and wildlife. The persistence of DDT–DDE in a Mediterranean coastal plain where pesticides were widely used and were banned decades ago is the aim of this study. Different sources of analytical information from water and soil analysis and topography and geographical variables were combined with the purpose of analyzing which environmental factors are more likely to condition the spatial distribution of DDT–DDE in the drainage watercourses of the area. An approach combining machine learning techniques, such as Random Forest and Mutual Information (MI), for classifying DDT–DDE concentration levels based on other environmental predictive variables was applied. In addition, classification procedure was iteratively performed with different training/validation partitions in order to extract the most informative parameters denoted by the highest MI scores and larger accuracy assessment metrics. Distance to drain canals, soil electrical conductivity, and soil sand texture fraction were the most informative environmental variables for predicting DDT–DDE water concentration clustersSpringer NatureDepartamentos de la UMH::Agroquímica y Medio Ambiente202520252023info:eu-repo/semantics/articleapplication/pdf19application/pdfhttps://hdl.handle.net/11000/34552reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheInglés45https://doi.org/10.1007/s10653-023-01486-yinfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:dspace.umh.es:11000/345522026-05-27T13:36:21Z
dc.title.none.fl_str_mv Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques
title Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques
spellingShingle Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques
Melendez-Pastor, Ignacio
DDT
DDE
Spatial distribution
Soil texture
Hydrology
Random forest
Mutual information
CDU::6 - Ciencias aplicadas::63 - Agricultura. Silvicultura. Zootecnia. Caza. Pesca::631 - Agricultura. Agronomía. Maquinaria agrícola. Suelos. Edafología agrícola
title_short Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques
title_full Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques
title_fullStr Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques
title_full_unstemmed Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques
title_sort Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques
dc.creator.none.fl_str_mv Melendez-Pastor, Ignacio
Navarro-Pedreño, Jose
López Granado, Otoniel Mario
Hernández, Encarni I.
JORDAN VIDAL, MANUEL MIGUEL
Gómez Lucas, Ignacio
author Melendez-Pastor, Ignacio
author_facet Melendez-Pastor, Ignacio
Navarro-Pedreño, Jose
López Granado, Otoniel Mario
Hernández, Encarni I.
JORDAN VIDAL, MANUEL MIGUEL
Gómez Lucas, Ignacio
author_role author
author2 Navarro-Pedreño, Jose
López Granado, Otoniel Mario
Hernández, Encarni I.
JORDAN VIDAL, MANUEL MIGUEL
Gómez Lucas, Ignacio
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamentos de la UMH::Agroquímica y Medio Ambiente
dc.subject.none.fl_str_mv DDT
DDE
Spatial distribution
Soil texture
Hydrology
Random forest
Mutual information
CDU::6 - Ciencias aplicadas::63 - Agricultura. Silvicultura. Zootecnia. Caza. Pesca::631 - Agricultura. Agronomía. Maquinaria agrícola. Suelos. Edafología agrícola
topic DDT
DDE
Spatial distribution
Soil texture
Hydrology
Random forest
Mutual information
CDU::6 - Ciencias aplicadas::63 - Agricultura. Silvicultura. Zootecnia. Caza. Pesca::631 - Agricultura. Agronomía. Maquinaria agrícola. Suelos. Edafología agrícola
description The presence and persistence of pesticides in the environment are environmental problems of great concern due to the health implications for humans and wildlife. The persistence of DDT–DDE in a Mediterranean coastal plain where pesticides were widely used and were banned decades ago is the aim of this study. Different sources of analytical information from water and soil analysis and topography and geographical variables were combined with the purpose of analyzing which environmental factors are more likely to condition the spatial distribution of DDT–DDE in the drainage watercourses of the area. An approach combining machine learning techniques, such as Random Forest and Mutual Information (MI), for classifying DDT–DDE concentration levels based on other environmental predictive variables was applied. In addition, classification procedure was iteratively performed with different training/validation partitions in order to extract the most informative parameters denoted by the highest MI scores and larger accuracy assessment metrics. Distance to drain canals, soil electrical conductivity, and soil sand texture fraction were the most informative environmental variables for predicting DDT–DDE water concentration clusters
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/11000/34552
url https://hdl.handle.net/11000/34552
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv 45
https://doi.org/10.1007/s10653-023-01486-y
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
19
application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:REDIUMH. Depósito Digital de la UMH
instname:Universidad Miguel Hernández de Elche
instname_str Universidad Miguel Hernández de Elche
reponame_str REDIUMH. Depósito Digital de la UMH
collection REDIUMH. Depósito Digital de la UMH
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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