TY - JOUR
T1 - Multi-variable approach to groundwater vulnerability elucidation
T2 - A risk-based multi-objective optimization model
AU - Zare, Masoumeh
AU - Nikoo, Mohammad Reza
AU - Nematollahi, Banafsheh
AU - Gandomi, Amir H.
AU - Farmani, Raziyeh
N1 - Copyright © 2023 Elsevier Ltd. All rights reserved.
PY - 2023/7/15
Y1 - 2023/7/15
N2 - 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.
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.
KW - Complex proportional assessment (COPRAS) technique
KW - Groundwater vulnerability map
KW - Land use-based index model
KW - Multi-criteria decision-making (MCDM) model
KW - Non-dominated sorting genetic algorithm- II and III (NSGA-II and NSGA-III)
KW - Uncertainty
KW - Nitrates/analysis
KW - Algorithms
KW - Groundwater
UR - http://www.scopus.com/inward/record.url?scp=85151310250&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85151310250&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/937e46ae-baae-3bd8-8e4d-46a4aa96a095/
U2 - 10.1016/j.jenvman.2023.117842
DO - 10.1016/j.jenvman.2023.117842
M3 - Article
C2 - 37004487
AN - SCOPUS:85151310250
SN - 0301-4797
VL - 338
JO - Journal of Environmental Management
JF - Journal of Environmental Management
M1 - 117842
ER -