Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major conce...

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Autores principales:	Muhammad Awais, Bilal Aslam, Ahsen Maqsoom, Umer Khalil, Fahim Ullah, Sheheryar Azam, Muhammad Imran
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	groundwater machine learning contamination risk mapping policymaking nitrate contamination Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/7a5fce90c52642fd885e092d0a879232
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spelling	oai:doaj.org-article:7a5fce90c52642fd885e092d0a8792322021-11-11T15:06:54ZAssessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach10.3390/app1121100342076-3417https://doaj.org/article/7a5fce90c52642fd885e092d0a8792322021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10034https://doaj.org/toc/2076-3417Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks.Muhammad AwaisBilal AslamAhsen MaqsoomUmer KhalilFahim UllahSheheryar AzamMuhammad ImranMDPI AGarticlegroundwatermachine learningcontamination risk mappingpolicymakingnitrate contaminationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10034, p 10034 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	groundwater machine learning contamination risk mapping policymaking nitrate contamination Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	groundwater machine learning contamination risk mapping policymaking nitrate contamination Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Muhammad Awais Bilal Aslam Ahsen Maqsoom Umer Khalil Fahim Ullah Sheheryar Azam Muhammad Imran Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
description	Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks.
format	article
author	Muhammad Awais Bilal Aslam Ahsen Maqsoom Umer Khalil Fahim Ullah Sheheryar Azam Muhammad Imran
author_facet	Muhammad Awais Bilal Aslam Ahsen Maqsoom Umer Khalil Fahim Ullah Sheheryar Azam Muhammad Imran
author_sort	Muhammad Awais
title	Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
title_short	Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
title_full	Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
title_fullStr	Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
title_full_unstemmed	Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
title_sort	assessing nitrate contamination risks in groundwater: a machine learning approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7a5fce90c52642fd885e092d0a879232
work_keys_str_mv	AT muhammadawais assessingnitratecontaminationrisksingroundwateramachinelearningapproach AT bilalaslam assessingnitratecontaminationrisksingroundwateramachinelearningapproach AT ahsenmaqsoom assessingnitratecontaminationrisksingroundwateramachinelearningapproach AT umerkhalil assessingnitratecontaminationrisksingroundwateramachinelearningapproach AT fahimullah assessingnitratecontaminationrisksingroundwateramachinelearningapproach AT sheheryarazam assessingnitratecontaminationrisksingroundwateramachinelearningapproach AT muhammadimran assessingnitratecontaminationrisksingroundwateramachinelearningapproach
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Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

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