Optimizing bioremediation techniques for soil decontamination in a linguistic intuitionistic fuzzy framework

Area of Science:

• Environmental Science and Engineering
• Computational Intelligence
• Decision Science

Background:

• Bioremediation offers sustainable soil decontamination by using microbial metabolic activities.
• Selecting the most effective bioremediation technique requires precise decision-making methods.
• Existing methods struggle with the uncertainties inherent in complex environmental data.

Purpose of the Study:

• To develop novel aggregation operators within the Linguistic Intuitionistic Fuzzy (LIF) environment.
• To address the challenge of selecting optimal bioremediation techniques for soil decontamination.
• To enhance the reliability and precision of decision-making in Multiple Attribute Decision Making (MADM) problems.

Main Methods:

• Utilized Linguistic Intuitionistic Fuzzy Sets (LIFSs) to manage linguistic uncertainties and intuitionistic assessments.
• Devised new aggregation operators: linguistic intuitionistic fuzzy Dombi weighted averaging (LIFDWA) and geometric (LIFDWG) operators.
• Formulated novel score and accuracy functions for MADM problems in the LIF environment and developed a supporting algorithm.

Main Results:

• Demonstrated the structural properties of the proposed LIFDWA and LIFDWG operators.
• Successfully applied the developed methodologies to an MADM problem for optimal bioremediation technique selection.
• Presented a comparative evaluation highlighting the efficacy and superiority of the new approaches over existing techniques.

Conclusions:

• The proposed LIF Dombi aggregation operators provide a robust framework for decision-making in soil decontamination.
• These novel methods effectively handle ambiguous data, leading to more reliable and precise selection of bioremediation strategies.
• The study underscores the practical applicability and authenticity of the developed techniques for environmental decision support.