Locating monitoring wells in groundwater systems using embedded optimization and simulation models

Area of Science:

• Environmental Science
• Hydrogeology
• Water Resource Management

Background:

• Identifying unknown pollution sources in groundwater is crucial for environmental protection.
• Effective monitoring well placement is essential for accurate contamination detection and source identification.
• Existing methods may lack efficiency in optimizing well locations and minimizing costs.

Purpose of the Study:

• To propose a novel methodology for the optimal location of monitoring wells in groundwater systems.
• To enhance the identification of unknown pollution sources using monitoring data.
• To balance cost-effectiveness with the reliability of contamination detection.

Main Methods:

• Developed single and multi-objective optimization models.
• Integrated Monte Carlo analysis, MODFLOW, and MT3D groundwater simulation models.
• Utilized Probabilistic Support Vector Machines (PSVM) for calibration and verification.

Main Results:

• The single-objective model provided initial well locations maximizing detection reliability.
• The multi-objective model minimized monitoring costs while maximizing detection reliability and source identification probability.
• The methodology was successfully applied and validated using a case study at the Tehran Refinery.

Conclusions:

• The proposed methodology offers an effective approach for optimally locating monitoring wells.
• This integrated approach improves the reliability and efficiency of groundwater pollution source identification.
• The study demonstrates practical applicability in real-world scenarios like industrial refineries.