Computationally efficient approach for identification of fuzzy dynamic groundwater sampling network.

Area of Science:

• Environmental Science
• Geostatistics
• Computational Science

Background:

• Environmental monitoring often faces uncertainty in future concentration values.
• Existing optimal sampling design models can be computationally expensive for multi-objective problems.

Purpose of the Study:

• To develop and evaluate novel uncertainty-based dynamic sampling frameworks for environmental monitoring.
• To address limitations of previous algorithms in handling multiple objectives in sampling network design.

Main Methods:

• Utilized fuzzy numbers to represent future concentration values, generated via multiple realization-based simulations.
• Proposed two frameworks: one focusing on fuzzy variance reduction, the other on mass estimation error reduction and spatial coverage.
• Implemented the Nondominated Sorting Genetic Algorithm-III (NSGA-III) for multi-objective optimization of sampling network design.

Main Results:

• The first framework minimizes total contaminant-concentration variance using fuzzy covariance.
• The second framework minimizes fuzzy mass estimation error and maximizes spatial coverage using fuzzy kriging.
• Both proposed frameworks demonstrated satisfactory performance in hypothetical test cases under uncertain conditions.

Conclusions:

• The enhanced NSGA-III based frameworks provide a robust solution for dynamic sampling network design in uncertain environments.
• These methods offer improved efficiency and effectiveness for environmental monitoring compared to previous approaches.