Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing

Nitrate leaching losses from arable lands into groundwater were a main driver in designating Nitrate Vulnerable Zones (NVZs) according to the Nitrates Directive, with a view to enhancing their water quality. Despite this, developing common strategies for effective water quality control in these area...

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Autores: Cárdenas Martínez, Aarón, Rodríguez Galiano, Víctor Francisco, Luque-Espinar, Juan Antonio, Mendes, Maria Paula
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/127173
Acceso en línea:https://hdl.handle.net/11441/127173
https://doi.org/10.1016/j.jhydrol.2021.127092
Access Level:acceso abierto
Palabra clave:Nitrates
Machine learning
Feature selection
Groundwater
Nitrate Vulnerable Zones
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spelling Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensingCárdenas Martínez, AarónRodríguez Galiano, Víctor FranciscoLuque-Espinar, Juan AntonioMendes, Maria PaulaNitratesMachine learningFeature selectionGroundwaterNitrate Vulnerable ZonesNitrate leaching losses from arable lands into groundwater were a main driver in designating Nitrate Vulnerable Zones (NVZs) according to the Nitrates Directive, with a view to enhancing their water quality. Despite this, developing common strategies for effective water quality control in these areas remains a challenge in the European Union. This paper evaluates the performance of the Random Forest (RF) machine learning algorithm combined with Feature Selection (FS) techniques in predicting nitrate pollution in NVZs groundwater bodies in different periods and using updated environmental features in Andalusia, Spain. A set of forty-four features extrinsic to groundwater bodies were used as environmental predictors, with an aim to make this methodology exportable to other regions. Phenological features obtained through remote-sensing techniques were included to measure the dynamics of agricultural activity. In addition, other dynamic features derived from weather and livestock effluents were included to analyse seasonal and interannual changes in nitrate pollution. Three feature stacks and two nitrate databases were used in the predictive modelling: Period 1 (2009), with 321 nitrate samples for training; Period 2 (2010), with 282 nitrate samples for validation and initial spatial prediction; and Period 3 (2017), to assess the changes in the probability of groundwater nitrate content exceeding 50 mg/L. Random Forest as a wrapper with four sequential search methods was considered: sequential backward selection (SBS), sequential forward selection (SFS), sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). From among all the Feature Selection methods applied, Random Forest with SFS had the best performance (overall accuracy = 0.891 and six predictor features) and linked the highest probability of nitrate pollution with three dynamic features: the Normalized Difference Vegetation Index (NDVI) base level, NDVI value for the end of the growing season and accumulated manure production of livestock farms; and three static features: slope, sediment depositional areas and valley depth.Geografía Física y Análisis Geográfico Regional2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/127173https://doi.org/10.1016/j.jhydrol.2021.127092reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésJournal of Hydrology, 603, 1-15.https://www.elsevier.com/locate/jhydrolinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1271732026-06-17T12:51:07Z
dc.title.none.fl_str_mv Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing
title Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing
spellingShingle Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing
Cárdenas Martínez, Aarón
Nitrates
Machine learning
Feature selection
Groundwater
Nitrate Vulnerable Zones
title_short Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing
title_full Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing
title_fullStr Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing
title_full_unstemmed Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing
title_sort Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing
dc.creator.none.fl_str_mv Cárdenas Martínez, Aarón
Rodríguez Galiano, Víctor Francisco
Luque-Espinar, Juan Antonio
Mendes, Maria Paula
author Cárdenas Martínez, Aarón
author_facet Cárdenas Martínez, Aarón
Rodríguez Galiano, Víctor Francisco
Luque-Espinar, Juan Antonio
Mendes, Maria Paula
author_role author
author2 Rodríguez Galiano, Víctor Francisco
Luque-Espinar, Juan Antonio
Mendes, Maria Paula
author2_role author
author
author
dc.contributor.none.fl_str_mv Geografía Física y Análisis Geográfico Regional
dc.subject.none.fl_str_mv Nitrates
Machine learning
Feature selection
Groundwater
Nitrate Vulnerable Zones
topic Nitrates
Machine learning
Feature selection
Groundwater
Nitrate Vulnerable Zones
description Nitrate leaching losses from arable lands into groundwater were a main driver in designating Nitrate Vulnerable Zones (NVZs) according to the Nitrates Directive, with a view to enhancing their water quality. Despite this, developing common strategies for effective water quality control in these areas remains a challenge in the European Union. This paper evaluates the performance of the Random Forest (RF) machine learning algorithm combined with Feature Selection (FS) techniques in predicting nitrate pollution in NVZs groundwater bodies in different periods and using updated environmental features in Andalusia, Spain. A set of forty-four features extrinsic to groundwater bodies were used as environmental predictors, with an aim to make this methodology exportable to other regions. Phenological features obtained through remote-sensing techniques were included to measure the dynamics of agricultural activity. In addition, other dynamic features derived from weather and livestock effluents were included to analyse seasonal and interannual changes in nitrate pollution. Three feature stacks and two nitrate databases were used in the predictive modelling: Period 1 (2009), with 321 nitrate samples for training; Period 2 (2010), with 282 nitrate samples for validation and initial spatial prediction; and Period 3 (2017), to assess the changes in the probability of groundwater nitrate content exceeding 50 mg/L. Random Forest as a wrapper with four sequential search methods was considered: sequential backward selection (SBS), sequential forward selection (SFS), sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). From among all the Feature Selection methods applied, Random Forest with SFS had the best performance (overall accuracy = 0.891 and six predictor features) and linked the highest probability of nitrate pollution with three dynamic features: the Normalized Difference Vegetation Index (NDVI) base level, NDVI value for the end of the growing season and accumulated manure production of livestock farms; and three static features: slope, sediment depositional areas and valley depth.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/127173
https://doi.org/10.1016/j.jhydrol.2021.127092
url https://hdl.handle.net/11441/127173
https://doi.org/10.1016/j.jhydrol.2021.127092
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Hydrology, 603, 1-15.
https://www.elsevier.com/locate/jhydrol
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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