PMID- 37004487
OWN - NLM
STAT- MEDLINE
DCOM- 20230418
LR  - 20230418
IS  - 1095-8630 (Electronic)
IS  - 0301-4797 (Linking)
VI  - 338
DP  - 2023 Jul 15
TI  - Multi-variable approach to groundwater vulnerability elucidation: A risk-based 
      multi-objective optimization model.
PG  - 117842
LID - S0301-4797(23)00630-8 [pii]
LID - 10.1016/j.jenvman.2023.117842 [doi]
AB  - 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.
CI  - Copyright (c) 2023 Elsevier Ltd. All rights reserved.
FAU - Zare, Masoumeh
AU  - Zare M
AD  - Department of Civil and Environmental Engineering, Shiraz University, Shiraz, 
      Iran. Electronic address: masoumezare86@gmail.com.
FAU - Nikoo, Mohammad Reza
AU  - Nikoo MR
AD  - Department of Civil and Architectural Engineering, Sultan Qaboos University, 
      Muscat, Oman. Electronic address: m.reza@squ.edu.om.
FAU - Nematollahi, Banafsheh
AU  - Nematollahi B
AD  - Department of Environmental Sciences, University of California, Riverside, USA. 
      Electronic address: bnema001@ucr.edu.
FAU - Gandomi, Amir H
AU  - Gandomi AH
AD  - Faculty of Engineering and IT, University of Technology Sydney, NSW, 2007, 
      Australia; University Research and Innovation Center (EKIK), Obuda University, 
      1034, Budapest, Hungary. Electronic address: gandomi@uts.edu.au.
FAU - Farmani, Raziyeh
AU  - Farmani R
AD  - Centre for Water Systems, Department of Engineering, Mathematics and Physical 
      Sciences, University of Exeter, Exeter, United Kingdom. Electronic address: 
      R.Farmani@exeter.ac.uk.
LA  - eng
PT  - Journal Article
DEP - 20230331
PL  - England
TA  - J Environ Manage
JT  - Journal of environmental management
JID - 0401664
RN  - 0 (Nitrates)
SB  - IM
MH  - *Nitrates/analysis
MH  - Algorithms
MH  - *Groundwater
MH  - Uncertainty
OTO - NOTNLM
OT  - Complex proportional assessment (COPRAS) technique
OT  - Groundwater vulnerability map
OT  - Land use-based index model
OT  - Multi-criteria decision-making (MCDM) model
OT  - Non-dominated sorting genetic algorithm- II and III (NSGA-II and NSGA-III)
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/04/03 06:00
MHDA- 2023/04/18 06:41
CRDT- 2023/04/02 18:11
PHST- 2022/09/26 00:00 [received]
PHST- 2023/01/10 00:00 [revised]
PHST- 2023/03/28 00:00 [accepted]
PHST- 2023/04/18 06:41 [medline]
PHST- 2023/04/03 06:00 [pubmed]
PHST- 2023/04/02 18:11 [entrez]
AID - S0301-4797(23)00630-8 [pii]
AID - 10.1016/j.jenvman.2023.117842 [doi]
PST - ppublish
SO  - J Environ Manage. 2023 Jul 15;338:117842. doi: 10.1016/j.jenvman.2023.117842. 
      Epub 2023 Mar 31.