Wang, Y. and Du, Y., 2024. Eutrophication evaluation assessment based on the multidimension cloud model and projection pursuit method.

Lake eutrophication evaluation is challenging because the evaluation process is uncertain and random and monitored data are usually inaccurate within a wide range. To deal with the uncertainty and randomness in evaluation eutrophication, the integration of multidimension cloud model (MDCM) and projection pursuit (PP) method were proposed, called the MDCM-PP method. The MDCM considers each evaluation factor as a one-dimension attribute, and the weights of evaluation factors were determined by PP method. In addition, the uncertainty and fuzziness of data was processed by triangular fuzzy numbers (TFNs). The combination of the MDCM-PP model and TFNs was applied to Dongting Lake in China to evaluated eutrophication statuses. The results indicated that the eutrophication levels in the East Dongting Lake were more serious than the South Dongting Lake and West Dongting Lake, which is in accordance with other research. The proposed method can consider fuzziness and randomness with the MDCM-PP model and TFNs in the eutrophication evaluation, which can also be applied to other evaluation processes with character of fuzziness and randomness.

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