Research graph
References from Spatial prediction of groundwater nitrate through machine learning and stakeholder co-creation: the Chalk aquifer case in East Anglia, UK. Local targets link to admitted publications; unresolved targets remain external evidence.
Global diagnosis of nitrate pollution in groundwater and review of removal technologies
2022 · External reference
10.1016/j.psep.2024.02.041
10.1016/j.psep.2024.02.041 · External reference
A hybrid intelligent model for spatial analysis of groundwater potential around Urmia Lake, Iran
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10.3390/w13213096
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10.1023/a:1010933404324 · 2001 · External reference
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10.1038/s41598-025-18996-7
10.1038/s41598-025-18996-7 · External reference
Nitrate contamination in groundwater across Switzerland: spatial prediction and data-driven assessment of anthropogenic and environmental drivers
10.1016/j.scitotenv.2025.179121 · 2025 · External reference
Predictive modeling and analysis of key drivers of groundwater nitrate pollution based on machine learning
10.1016/j.jhydrol.2023.129934 · 2023 · External reference
Advances in groundwater potential mapping
10.1007/s10040-019-02001-3 · 2019 · External reference
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
10.1111/j.1600-0587.2012.07348.x · 2013 · External reference
Approach to mapping groundwater-dependent ecosystems through machine learning in central Chile
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10.3389/fenvs.2025.1543852
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10.1006/jcss.1997.1504 · 1997 · External reference
Greedy function approximation: a gradient boosting machine
10.1214/aos/1013203451 · 2001 · External reference
Soil texture effect on nitrate leaching in soil percolates
10.1080/00103629409369207 · 1994 · External reference
10.1016/j.scitotenv.2023.169188
10.1016/j.scitotenv.2023.169188 · External reference
Unresolved reference
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Extremely randomized trees
10.1007/s10994-006-6226-1 · 2006 · External reference
Combining artificial neural networks and genetic algorithms to model nitrate contamination in groundwater
10.1007/s11069-023-06387-y · 2024 · External reference
Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
10.1080/10106049.2021.2007298 · 2022 · External reference
10.1007/s13201-024-02320-1
10.1007/s13201-024-02320-1 · External reference
Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali
10.5194/hess-26-221-2022 · 2022 · External reference
10.1016/j.chemosphere.2021.133388
10.1016/j.chemosphere.2021.133388 · External reference
10.1144/qjegh2024-072
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Predictive soil parent material mapping at a regional-scale: a random forest approach
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10.1016/j.jhydrol.2024.130982
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Application of machine learning and deep neural networks for spatial prediction of groundwater nitrate concentration to improve land use management practices
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Unresolved reference
External reference
Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning
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Unresolved reference
2017 · External reference
Improving groundwater nitrate concentration prediction using local ensemble of machine learning models
10.1016/j.jenvman.2023.118782 · 2023 · External reference
10.1016/j.scitotenv.2023.166863
10.1016/j.scitotenv.2023.166863 · External reference
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10.1016/j.rineng.2024.102831 · External reference
10.1016/j.jhydrol.2021.126026
10.1016/j.jhydrol.2021.126026 · External reference
10.1007/s13201-025-02572-5
10.1007/s13201-025-02572-5 · External reference
10.3390/w17192861
10.3390/w17192861 · External reference
10.5285/b963ead70580451aa7455782224479d5
10.5285/b963ead70580451aa7455782224479d5 · External reference
Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility
10.1016/j.jenvman.2019.109867 · 2020 · External reference
Unresolved reference
2020 · External reference
Ensemble boosting and bagging based machine learning models for groundwater potential prediction
10.1007/s11269-020-02704-3 · 2021 · External reference
Prediction on the fluoride contamination in groundwater at the Datong Basin, Northern China: comparison of random forest, logistic regression and artificial neural network
10.1016/j.apgeochem.2021.105054 · 2021 · External reference
Scikit-learn: machine learning in python
2011 · External reference
Global threat of arsenic in groundwater
10.1126/science.aba1510 · 2020 · External reference
Soilgrids 2.0: producing soil information for the globe with quantified spatial uncertainty
10.5194/soil-7-217-2021 · 2021 · External reference
Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States
10.1016/j.scitotenv.2021.151065 · 2022 · External reference
Model-agnostic interpretability of machine learning
2016 · External reference
10.1144/1470-9236/07-032
10.1144/1470-9236/07-032 · External reference
Predictive modeling of groundwater nitrate pollution using random forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain)
10.1016/j.scitotenv.2014.01.001 · 2014 · External reference
10.1016/j.scitotenv.2021.150960
10.1016/j.scitotenv.2021.150960 · External reference
Agriculture’s contribution to nitrate contamination of Californian groundwater (1945–2005)
10.2134/jeq2013.10.0411 · 2014 · External reference
10.1016/j.chemosphere.2024.141830
10.1016/j.chemosphere.2024.141830 · External reference
Nitrates in the environment: a critical review of their distribution, sensing techniques, ecological effects and remediation
10.1016/j.chemosphere.2021.131996 · 2022 · External reference
10.2134/jeq1993.00472425002200030002x
10.2134/jeq1993.00472425002200030002x · External reference
A machine learning based modelling framework to predict nitrate leaching from agricultural soils across the Netherlands
10.1088/2515-7620/abf15f · 2021 · External reference
Unresolved reference
External reference
Unresolved reference
2005 · External reference
Variation of nitrate sources affected by precipitation with different intensities in groundwater in the piedmont plain area of alluvial-pluvial fan
10.1016/j.jenvman.2024.121885 · 2024 · External reference
10.5285/b6b92ce3-dcd7-4f0b-8e43-e937ddf1d4eb
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10.1016/j.scitotenv.2015.10.127 · External reference
Controls and predictions of geogenic redox-sensitive contaminants in Danish groundwater
10.1016/j.gsd.2026.101600 · 2026 · External reference
Prediction of phosphorus concentrations in shallow groundwater in intensive agricultural regions based on machine learning
10.1016/j.chemosphere.2022.137623 · 2023 · External reference
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10.3390/w16172375 · External reference
Extreme precipitation accelerates nitrate leaching in the intensive agricultural region with thick unsaturated zones
10.1016/j.scitotenv.2024.170789 · 2024 · External reference
Ensemble machine learning paradigms in hydrology: a review
10.1016/j.jhydrol.2021.126266 · 2021 · External reference
10.1002/hyp.8164
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A decision-theoretic generalization of on-line learning and an application to boosting
10.1006/jcss.1997.1504 · ExternalCitation · doi-reference
A hybrid intelligent model for spatial analysis of groundwater potential around Urmia Lake, Iran
10.1007/s00477-022-02368-y · ExternalCitation · doi-reference
10.1007/s10040-008-0390-2
10.1007/s10040-008-0390-2 · ExternalCitation · doi-reference
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10.1007/s10040-019-02001-3 · ExternalCitation · doi-reference
10.1007/s10653-013-9550-y
10.1007/s10653-013-9550-y · ExternalCitation · doi-reference
Extremely randomized trees
10.1007/s10994-006-6226-1 · ExternalCitation · doi-reference
Combining artificial neural networks and genetic algorithms to model nitrate contamination in groundwater
10.1007/s11069-023-06387-y · ExternalCitation · doi-reference
Ensemble boosting and bagging based machine learning models for groundwater potential prediction
10.1007/s11269-020-02704-3 · ExternalCitation · doi-reference
10.1007/s13201-023-02043-9
10.1007/s13201-023-02043-9 · ExternalCitation · doi-reference
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10.1007/s13201-024-02320-1 · ExternalCitation · doi-reference
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10.1007/s13201-025-02572-5 · ExternalCitation · doi-reference
Prediction on the fluoride contamination in groundwater at the Datong Basin, Northern China: comparison of random forest, logistic regression and artificial neural network
10.1016/j.apgeochem.2021.105054 · ExternalCitation · doi-reference
Nitrates in the environment: a critical review of their distribution, sensing techniques, ecological effects and remediation
10.1016/j.chemosphere.2021.131996 · ExternalCitation · doi-reference
10.1016/j.chemosphere.2021.133388
10.1016/j.chemosphere.2021.133388 · ExternalCitation · doi-reference
Prediction of phosphorus concentrations in shallow groundwater in intensive agricultural regions based on machine learning
10.1016/j.chemosphere.2022.137623 · ExternalCitation · doi-reference
10.1016/j.chemosphere.2024.141830
10.1016/j.chemosphere.2024.141830 · ExternalCitation · doi-reference
Predictive soil parent material mapping at a regional-scale: a random forest approach
10.1016/j.geoderma.2013.09.016 · ExternalCitation · doi-reference
Approach to mapping groundwater-dependent ecosystems through machine learning in central Chile
10.1016/j.gsd.2025.101526 · ExternalCitation · doi-reference
Controls and predictions of geogenic redox-sensitive contaminants in Danish groundwater
10.1016/j.gsd.2026.101600 · ExternalCitation · doi-reference
10.1016/j.heliyon.2024.e27867
10.1016/j.heliyon.2024.e27867 · ExternalCitation · doi-reference
Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility
10.1016/j.jenvman.2019.109867 · ExternalCitation · doi-reference
Improving groundwater nitrate concentration prediction using local ensemble of machine learning models
10.1016/j.jenvman.2023.118782 · ExternalCitation · doi-reference
Variation of nitrate sources affected by precipitation with different intensities in groundwater in the piedmont plain area of alluvial-pluvial fan
10.1016/j.jenvman.2024.121885 · ExternalCitation · doi-reference
10.1016/j.jhydrol.2021.126026
10.1016/j.jhydrol.2021.126026 · ExternalCitation · doi-reference
Ensemble machine learning paradigms in hydrology: a review
10.1016/j.jhydrol.2021.126266 · ExternalCitation · doi-reference
Predictive modeling and analysis of key drivers of groundwater nitrate pollution based on machine learning
10.1016/j.jhydrol.2023.129934 · ExternalCitation · doi-reference
10.1016/j.jhydrol.2024.130982
10.1016/j.jhydrol.2024.130982 · ExternalCitation · doi-reference
The relationship between land use and groundwater resources and quality
10.1016/j.landusepol.2009.09.005 · ExternalCitation · doi-reference
10.1016/j.psep.2024.02.041
10.1016/j.psep.2024.02.041 · ExternalCitation · doi-reference
10.1016/j.rineng.2024.102831
10.1016/j.rineng.2024.102831 · ExternalCitation · doi-reference
Predictive modeling of groundwater nitrate pollution using random forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain)
10.1016/j.scitotenv.2014.01.001 · ExternalCitation · doi-reference
10.1016/j.scitotenv.2015.10.127
10.1016/j.scitotenv.2015.10.127 · ExternalCitation · doi-reference
Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning
10.1016/j.scitotenv.2019.03.045 · ExternalCitation · doi-reference
10.1016/j.scitotenv.2021.150960
10.1016/j.scitotenv.2021.150960 · ExternalCitation · doi-reference
Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States
10.1016/j.scitotenv.2021.151065 · ExternalCitation · doi-reference
10.1016/j.scitotenv.2023.166863
10.1016/j.scitotenv.2023.166863 · ExternalCitation · doi-reference
10.1016/j.scitotenv.2023.169188
10.1016/j.scitotenv.2023.169188 · ExternalCitation · doi-reference
Extreme precipitation accelerates nitrate leaching in the intensive agricultural region with thick unsaturated zones
10.1016/j.scitotenv.2024.170789 · ExternalCitation · doi-reference
Nitrate contamination in groundwater across Switzerland: spatial prediction and data-driven assessment of anthropogenic and environmental drivers
10.1016/j.scitotenv.2025.179121 · ExternalCitation · doi-reference
10.1021/acs.est.3c06150
10.1021/acs.est.3c06150 · ExternalCitation · doi-reference
Random forests
10.1023/a:1010933404324 · ExternalCitation · doi-reference
Bagging predictors
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10.1038/s41598-025-18996-7 · ExternalCitation · doi-reference
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10.1038/s42256-019-0138-9 · ExternalCitation · doi-reference
Soil texture effect on nitrate leaching in soil percolates
10.1080/00103629409369207 · ExternalCitation · doi-reference
Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
10.1080/10106049.2021.2007298 · ExternalCitation · doi-reference
A machine learning based modelling framework to predict nitrate leaching from agricultural soils across the Netherlands
10.1088/2515-7620/abf15f · ExternalCitation · doi-reference
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
10.1111/j.1600-0587.2012.07348.x · ExternalCitation · doi-reference
Global threat of arsenic in groundwater
10.1126/science.aba1510 · ExternalCitation · doi-reference
10.1144/1470-9236/07-032
10.1144/1470-9236/07-032 · ExternalCitation · doi-reference
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10.1186/1758-2946-6-10
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Greedy function approximation: a gradient boosting machine
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10.2134/jeq1993.00472425002200030002x
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Agriculture’s contribution to nitrate contamination of Californian groundwater (1945–2005)
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10.3389/fenvs.2025.1543852
10.3389/fenvs.2025.1543852 · ExternalCitation · doi-reference
Application of machine learning and deep neural networks for spatial prediction of groundwater nitrate concentration to improve land use management practices
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10.3808/jei.202100470 · ExternalCitation · doi-reference
Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali
10.5194/hess-26-221-2022 · ExternalCitation · doi-reference
Soilgrids 2.0: producing soil information for the globe with quantified spatial uncertainty
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10.5285/b6b92ce3-dcd7-4f0b-8e43-e937ddf1d4eb
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