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Eutrophication Assessment Based on the Cloud Matter Element Model

Author

Listed:
  • Yumin Wang

    (School of Energy and Environment, Southeast University, Nanjing 210096, China)

  • Xian’e Zhang

    (School of Environment and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Yifeng Wu

    (School of Energy and Environment, Southeast University, Nanjing 210096, China)

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 E x , entropy E n , and hyper-entropy H e . 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.

Suggested Citation

  • Yumin Wang & Xian’e Zhang & Yifeng Wu, 2020. "Eutrophication Assessment Based on the Cloud Matter Element Model," IJERPH, MDPI, vol. 17(1), pages 1-19, January.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:1:p:334-:d:304852
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    References listed on IDEAS

    as
    1. Zaobao Liu & Jianfu Shao & Weiya Xu & Yongdong Meng, 2013. "Prediction of rock burst classification using the technique of cloud models with attribution weight," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 549-568, September.
    2. Yumin Wang & Weijian Ran, 2019. "Comprehensive Eutrophication Assessment Based on Fuzzy Matter Element Model and Monte Carlo-Triangular Fuzzy Numbers Approach," IJERPH, MDPI, vol. 16(10), pages 1-17, May.
    3. K. Cheng & Q. Fu & J. Meng & T. X. Li & W. Pei, 2018. "Analysis of the Spatial Variation and Identification of Factors Affecting the Water Resources Carrying Capacity Based on the Cloud Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2767-2781, June.
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