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Method for lake eutrophication levels evaluation: TOPSIS-MCS.

Song-Shun Lin¹, Shui-Long Shen^2,3,4, Ning Zhang²

¹Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, Minhang District 200240, China.

|August 26, 2021

Summary

This summary is machine-generated.

This study introduces a novel method combining Monte Carlo simulation (MCS) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for reliable lake eutrophication assessment. The approach accurately evaluates water quality, enhancing environmental monitoring.

Keywords:

Lake eutrophication evaluation Membership function Monte Carlo simulation TOPSIS method

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Area of Science:

Environmental Science
Water Quality Assessment
Eutrophication Studies

Background:

Lake eutrophication poses significant environmental challenges.
Accurate assessment of eutrophication levels is crucial for effective water resource management.
Existing methods may face uncertainties and data collection errors.

Purpose of the Study:

To develop and validate a robust approach for evaluating lake eutrophication levels.
To integrate Monte Carlo simulation (MCS) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for enhanced accuracy.
To address uncertainties and errors in measured water quality data.

Main Methods:

The study merges Monte Carlo simulation (MCS) for data generation with the TOPSIS method for multi-criteria decision-making.
Membership functions were used to determine membership degrees of evaluated indices.
Global sensitivity analysis (GSA) was performed to identify key influencing factors.

Main Results:

The integrated MCS-TOPSIS approach demonstrated consistent results with the actual eutrophication level in Lake Erhai, China.
Potassium permanganate index (COD_Mn) showed the highest negative correlation, while Secchi disc (SD) exhibited the highest positive correlation with evaluated results.
The method effectively overcomes errors from human and equipment in data collection.

Conclusions:

The developed approach enhances the reliability of lake eutrophication assessment by combining MCS and TOPSIS.
This method provides a more accurate and dependable evaluation of water quality compared to traditional methods.
The findings contribute to improved understanding and management of lake ecosystems.