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

Area of Science:

• Environmental Science
• Hydrogeology
• Water Resource Management

Background:

• Groundwater contamination is a growing concern due to population increase.
• Existing groundwater vulnerability models lack optimization under risk conditions.
• There is a need for robust multi-objective optimization coupled with Multi-Criteria Decision-Making (MCDM) models.

Purpose of the Study:

• To develop an innovative hybrid risk-based multi-objective optimization model for groundwater vulnerability.
• To optimize existing models (SI and DRASTICA) for Nitrate and Sulfate contamination.
• To identify the best compromise solution for contamination weights using MCDM.

Main Methods:

• Generated contamination rate scenarios using Susceptibility Index (SI) and DRASTICA models.
• Employed Non-dominated Sorting Genetic Algorithms II and III (NSGA-II and NSGA-III) with Conditional Value-at-Risk (CVaR) for uncertainty.
• Utilized the COmplex PRoportional ASsessment (COPRAS) MCDM model to determine optimal weights.

Main Results:

• Optimized SI model showed improved correlation (0.8) for Sulfate compared to the initial model (0.58).
• Optimized DRASTICA model achieved a correlation of 0.7 for Sulfate, matching the initial model.
• Both optimized SI (0.6) and DRASTICA (0.7) models significantly improved Nitrate correlation from the initial 0.36.

Conclusions:

• The hybrid optimization model enhances the accuracy of groundwater vulnerability assessment.
• The optimized SI model demonstrated the strongest improvement in correlation for Sulfate and Nitrate.
• This approach provides a robust framework for managing groundwater contamination risks.