Comprehensive Eutrophication Assessment Based on Fuzzy Matter Element Model and Monte Carlo-Triangular Fuzzy Numbers...

Area of Science:

• Environmental Science
• Water Quality Assessment
• Eutrophication Studies

Background:

• Assessing lake eutrophication is complex due to uncertain and fuzzy data.
• Traditional methods struggle with the inherent complexities of eutrophication evaluation.
• Key parameters include chlorophyll-a, COD, TP, TN, and clarity.

Purpose of the Study:

• To develop a robust model for evaluating lake eutrophication levels.
• To address data uncertainties and fuzziness in eutrophication assessment.
• To provide comprehensive and reliable information for lake management.

Main Methods:

• Application of triangular fuzzy numbers (TFN) to represent data fuzziness.
• Integration of an improved fuzzy matter element (FME) approach with TFNs.
• Utilization of entropy and analytic hierarchy process (AHP) for weight determination.
• Incorporation of the Monte Carlo (MC) simulation for arithmetic operations and uncertainty analysis.

Main Results:

• The hybrid TFN-MC-FME model was successfully applied to 24 lakes in China.
• The model effectively processed uncertain and fuzzy data, providing quantitative intervals and probabilities.
• Evaluation results for most lakes aligned with other methods, confirming model accuracy.
• The model demonstrated capability in assessing current eutrophication levels and predicting trends.

Conclusions:

• The TFN-MC-FME model offers a superior approach for evaluating lake eutrophication by managing data uncertainty.
• The methodology provides a deeper understanding of eutrophication dynamics and trends.
• The model serves as a valuable tool for environmental decision-making and lake management authorities.