Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels

Area of Science:

• Environmental Science
• Water Quality Assessment
• Eutrophication Studies

Background:

• Eutrophication poses a significant threat to aquatic ecosystems worldwide.
• Accurate evaluation of eutrophication levels is crucial for effective water resource management.
• Existing methods may struggle with data uncertainty and inherent fuzziness.

Purpose of the Study:

• To develop and validate a robust approach for eutrophication evaluation.
• To integrate the TOPSIS method with Monte Carlo Simulation for improved reliability.
• To assess the performance of the proposed method using a real-world case study (Lake Erhai).

Main Methods:

• Application of the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for multi-criteria decision analysis.
• Utilization of Monte Carlo Simulation (MCS) to generate a normally distributed dataset from observed data, enhancing reliability.
• Incorporation of a membership function to evaluate the degree of eutrophication, with a focus on the coefficient P.

Main Results:

• The developed approach effectively evaluated eutrophication in Lake Erhai, showing consistency with the real situation when the coefficient P was 1.
• The method demonstrated capability in handling inherent fuzziness and uncertainties in evaluation items.
• Monte Carlo Simulation improved the reliability of the evaluation results and increased tolerance to errors in measured data.

Conclusions:

• The combined TOPSIS and MCS approach offers a reliable and robust method for eutrophication assessment.
• The study identified potassium permanganate index (COD Mn ) and Secchi disc (SD) as the most sensitive factors influencing the evaluation.
• A recommended range for the coefficient P in the membership function was provided for practical application.