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

This study presents an approach for eutrophication evaluation based on the technique for order preference by similarity to an ideal solution (TOPSIS) method and Monte Carlo simulation (MCS). The MCS is employed to produce a normally distributed dataset based on the observed data while the TOPSIS method and membership function are used to evaluate the level of eutrophication. Herein, a eutrophication problem in Lake Erhai is evaluated to check the performance of the proposed approach. The evaluation results were consistent with the real situation when the coefficient P in the membership function is equal to 1. Moreover, the developed approach is able to (i) deal with evaluation items with inherent fuzziness and uncertainties, (ii) improve the reliability of evaluation results via MCS, and (iii) raise the tolerance to errors in measured data. A global sensitivity analysis indicated that the potassium permanganate index (COD_Mn) and Secchi disc (SD) are the most sensitive factors in the developed approach. Finally, a range for the coefficient P value in the membership function was recommended.