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Comparison of Evaluation Methods for Eutrophication of Water Quality in Honghu Lake
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Yong-xi SUN1, Yan-fei CHEN1, Yuan ZHOU2, Yu-ru DONG1
Water Resources and Power | 2023, 41(9) : 36 - 39
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Water Resources and Power | 2023, 41(9): 36-39
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Comparison of Evaluation Methods for Eutrophication of Water Quality in Honghu Lake
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Yong-xi SUN1, Yan-fei CHEN1, Yuan ZHOU2, Yu-ru DONG1
Affiliations
  • 1.College of Resources and Environment, Yangtze University, Wuhan 430100, China
  • 2.Hubei Jingzhou Survey Bureau of Hydrology Resources, Jingzhou 434000, China
Published: 2023-09-25 doi: 10.20040/j.cnki.1000-7709.2023.20230294
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To scientifically evaluate the eutrophication degree of water bodies, based on the comparison and analysis of various eutrophication evaluation methods, convolutional neural network (CNN) was introduced to establish the convolutional-eutrophication (CNN-E) model. Based on the monthly-scale water quality and algae monitoring data of Honghu Lake from 2014 to 2019, the comprehensive nutrient index method, BP neural network method and CNN-E model were used to evaluate its eutrophication degree. The mean absolute error, root mean square error, coefficient of determination and Nash-Sutcliffe efficiency coefficient were used to evaluate the performance of the neural network model. The results show that Hong Lake was in a mild eutrophic state for a long time, and the eutrophication level was increasing. In terms of model performance, the four evaluation indexes of CNN-E model are better than BP neural network 0.166, 0.098, 0.078 and 0.087, respectively. The CNN-E model can provide technical support for the prevention and comprehensive management of eutrophication in lake water bodies.

eutrophication  /  integrated nutrition index  /  BP neural network  /  CNN-E
Yong-xi SUN, Yan-fei CHEN, Yuan ZHOU, Yu-ru DONG. Comparison of Evaluation Methods for Eutrophication of Water Quality in Honghu Lake[J]. Water Resources and Power, 2023 , 41 (9) : 36 -39 . DOI: 10.20040/j.cnki.1000-7709.2023.20230294
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20230294
  • Receive Date：2023-03-01
  • Online Date：2026-01-28
  • Published：2023-09-25
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  • Received：2023-03-01
  • Revised：2023-04-10
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Affiliations
    1.College of Resources and Environment, Yangtze University, Wuhan 430100, China
    2.Hubei Jingzhou Survey Bureau of Hydrology Resources, Jingzhou 434000, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20230294
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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