Multi-variable approach to groundwater vulnerability elucidation: A risk-based multi-objective optimization model.

Zare, M; Nikoo, MR; Nematollahi, B; Gandomi, AH; Farmani, R

Multi-variable approach to groundwater vulnerability elucidation: A risk-based multi-objective optimization model.

Zare, M Nikoo, MR Nematollahi, B Gandomi, AH Farmani, R

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Publisher:: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: J Environ Manage, 2023, 338, pp. 117842
Issue Date:: 2023-07-15

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Field	Value	Language
dc.contributor.author	Zare, M
dc.contributor.author	Nikoo, MR
dc.contributor.author	Nematollahi, B
dc.contributor.author	Gandomi, AH
dc.contributor.author	Farmani, R
dc.date.accessioned	2024-04-29T03:47:33Z
dc.date.available	2023-03-28
dc.date.available	2024-04-29T03:47:33Z
dc.date.issued	2023-07-15
dc.identifier.citation	J Environ Manage, 2023, 338, pp. 117842
dc.identifier.issn	0301-4797
dc.identifier.issn	1095-8630
dc.identifier.uri	http://hdl.handle.net/10453/178448
dc.description.abstract	Groundwater vulnerability mapping is essential in environmental management since there is an increase in contamination caused by excessive population growth. However, to our knowledge, there is rare research dedicated to optimizing the groundwater vulnerability models, considering risk conditions, using a robust multi-objective optimization algorithm coupled with a multi-criteria decision-making model (MCDM). This study filled this knowledge gap by developing an innovative hybrid risk-based multi-objective optimization model using three distinguished models. The first model generated two series of scenarios for rate modifications associated with two common contaminations, Nitrate and Sulfate, based on susceptibility index (SI) and DRASTICA models. The second model was a multi-objective optimization framework using non-dominated sorting genetic algorithms- II and III (NSGA-II and NSGA-III), considering uncertainties in the input rates by the conditional value-at-risk (CVaR) technique. Finally, the third model was a well-known MCDM model, the COmplex PRoportional ASsessment (COPRAS), which identified the best compromise solution among Pareto-optimal solutions for weights of the contaminations. Regarding the Sulfate's results, although the optimized DRASTICA model led to the same correlation as the initial model, 0.7, the optimized SI model increased the correlation to 0.8 compared to the initial model as 0.58. For the Nitrate, both the optimized SI and the optimized DRASTICA models raised the correlation to 0.6 and 0.7 compared to the initial model with a correlation value of 0.36, respectively. Hence, the best and the lowest correlation among the optimized models were between SI and Sulfate concentration and SI and Nitrate concentration, respectively.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.relation.ispartof	J Environ Manage
dc.relation.isbasedon	10.1016/j.jenvman.2023.117842
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Sciences
dc.subject.mesh	Nitrates
dc.subject.mesh	Algorithms
dc.subject.mesh	Groundwater
dc.subject.mesh	Uncertainty
dc.subject.mesh	Nitrates
dc.subject.mesh	Uncertainty
dc.subject.mesh	Algorithms
dc.subject.mesh	Groundwater
dc.subject.mesh	Nitrates
dc.subject.mesh	Algorithms
dc.subject.mesh	Groundwater
dc.subject.mesh	Uncertainty
dc.title	Multi-variable approach to groundwater vulnerability elucidation: A risk-based multi-objective optimization model.
dc.type	Journal Article
utslib.citation.volume	338
utslib.location.activity	England
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-29T03:47:30Z
pubs.publication-status	Published
pubs.volume	338

Abstract:

Groundwater vulnerability mapping is essential in environmental management since there is an increase in contamination caused by excessive population growth. However, to our knowledge, there is rare research dedicated to optimizing the groundwater vulnerability models, considering risk conditions, using a robust multi-objective optimization algorithm coupled with a multi-criteria decision-making model (MCDM). This study filled this knowledge gap by developing an innovative hybrid risk-based multi-objective optimization model using three distinguished models. The first model generated two series of scenarios for rate modifications associated with two common contaminations, Nitrate and Sulfate, based on susceptibility index (SI) and DRASTICA models. The second model was a multi-objective optimization framework using non-dominated sorting genetic algorithms- II and III (NSGA-II and NSGA-III), considering uncertainties in the input rates by the conditional value-at-risk (CVaR) technique. Finally, the third model was a well-known MCDM model, the COmplex PRoportional ASsessment (COPRAS), which identified the best compromise solution among Pareto-optimal solutions for weights of the contaminations. Regarding the Sulfate's results, although the optimized DRASTICA model led to the same correlation as the initial model, 0.7, the optimized SI model increased the correlation to 0.8 compared to the initial model as 0.58. For the Nitrate, both the optimized SI and the optimized DRASTICA models raised the correlation to 0.6 and 0.7 compared to the initial model with a correlation value of 0.36, respectively. Hence, the best and the lowest correlation among the optimized models were between SI and Sulfate concentration and SI and Nitrate concentration, respectively.

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http://hdl.handle.net/10453/178448