Abstract
Eutrophication has become one of the most serious problems threatening the lakes/reservoirs in China over 50 years. Evaluation of eutrophication is a multi-criteria decision-making process with uncertainties. In this study, a cloud matter element (CME) model was developed in order to evaluate eutrophication level objectively and scientifically, which incorporated the randomness and fuzziness of eutrophication evaluation process. The elements belonging to each eutrophication level in the CME model were determined by means of certainty degrees through repeated simulations of cloud model with reasonable parameters of expectation Ex, entropy En, and hyper-entropy He. The weights of evaluation indicators were decided by a combination of entropy technology and analytic hierarchy process method. The neartudes of water samples to each eutrophication level of lakes/reservoirs in the CME model were generated and the eutrophication levels were determined by maximum neartude principal. The proposed CME model was applied to evaluate eutrophication levels of 24 typical lakes/reservoirs in China. The results of the CME model were compared with those of comprehensive index method, matter element model, fuzzy matter element model, and cloud model. Most of the results obtained by the CME model were consistent with the results obtained by other methods, which proved the CME model is an effective tool to evaluate eutrophication.
Highlights
Rapid population growth and economic rise in past decades contribute to the pollution of water bodies in China, including lakes, reservoirs, rivers, and estuaries, which leads to the deterioration of water environment
The eutrophication levels of 24 lakes/reservoirs were determined by the cloud matter element (CME) model, shown in
A cloud matter element (CME) model is proposed with combination of cloud model and matter element (ME) model and applied to assess the eutrophication levels of 24 typical lakes and reservoirs in China
Summary
Rapid population growth and economic rise in past decades contribute to the pollution of water bodies in China, including lakes, reservoirs, rivers, and estuaries, which leads to the deterioration of water environment. To solve the fuzziness problem, of eutrophication evaluation, fuzziness and randomness exist in in monitor selection functions of statistical fuzzy mathematics theory was introduced into ME method terms of ofdata, membership to methods, and determination of weights [13]. A and B” [18].toUnder fuzziness and randomness of objects leads to inaccurate evaluation since the the character of such circumstance, the cloud modeland was introduced into FME modelresults by extending accurate fuzzy objects was described as “Both Under such circumstance, the cloud model was membership functions to random membership functions in terms of statistical distributions of introduced into. The cloud matter element consideration of randomness and fuzziness of eutrophication evaluation systematically by (CME).
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