期刊
JOURNAL OF ENVIRONMENTAL MANAGEMENT
卷 248, 期 -, 页码 -出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2019.109259
关键词
Lake eutrophication; Random errors; Fuzziness; Random weighting method; Multidimensional similarity cloud model; Cloud digital characteristics
资金
- Chinese National Special Science and Technology Program of Water Pollution Control and Treatment [2017ZX07302004]
- National Natural Science Foundation of China [51679006, 51879006]
Lake eutrophication is characterized by a variety of indicators, including nitrogen and phosphorus concentrations, chemical oxygen demand, chlorophyll levels, and water transparency. In this study, a multidimensional similarity cloud model (MSCM) is combined with a random weighting method to reduce the impacts of random errors in eutrophication monitoring data and the fuzziness of lake eutrophication definitions on the consistency and reliability of lake eutrophication evaluations. Measured samples are assigned to lake eutrophication levels based on the cosine of the angle between the cloud digital characteristics vectors of each sample and those of each eutrophication grade. To field test this method, the eutrophication level of Nansi Lake in Shandong Province was evaluated based on monitoring data collected in 2009-2016. Results demonstrate that, in 2009 and in 2011-2015, the upper lake of Nansi Lake exhibited moderate eutrophication while the lower lake exhibited mild eutrophication. In 2010, 2016, elevated concentrations of total nitrogen and total phosphorus led to an increase in the eutrophication level of the lower lake, matching that of the upper lake. Based on the results of these field tests, we conclude that the MSCM presented in this study provides a more flexible and effective method for evaluating lake eutrophication data than the existing multidimensional normal cloud model.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据