Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

Awais, Muhammad and Aslam, Bilal and Maqsoom, Ahsen and Khalil, Umer and Ullah, Fahim ORCID: https://orcid.org/0000-0002-6221-1175 and Azam, Sheheryar and Imran, Muhammad (2021) Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach. Applied Sciences, 11 (21):10034. pp. 1-21. ISSN 2076-3417

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Abstract

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Date Deposited: 09 Nov 2021 03:55
Last Modified: 21 Jul 2022 03:33
Uncontrolled Keywords: groundwater; machine learning; contamination risk mapping; policymaking; nitrate contamination
Fields of Research (2008): 09 Engineering > 0904 Chemical Engineering > 090410 Water Treatment Processes
09 Engineering > 0905 Civil Engineering > 090508 Water Quality Engineering
09 Engineering > 0915 Interdisciplinary Engineering > 091507 Risk Engineering (excl. Earthquake Engineering)
17 Psychology and Cognitive Sciences > 1702 Cognitive Sciences > 170203 Knowledge Representation and Machine Learning
Fields of Research (2020): 40 ENGINEERING > 4011 Environmental engineering > 401104 Health and ecological risk assessment
41 ENVIRONMENTAL SCIENCES > 4105 Pollution and contamination > 410504 Surface water quality processes and contaminated sediment assessment
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified
40 ENGINEERING > 4004 Chemical engineering > 400411 Water treatment processes
Identification Number or DOI: https://doi.org/10.3390/app112110034
URI: http://eprints.usq.edu.au/id/eprint/44074

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