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Journal of Hydrodynamics

, Volume 18, Issue 1, pp 517–521 | Cite as

Study on improved BP artificial neural networks in eutrophication assessment of China eastern lakes

Session B8

Abstract

An improved back propagation artificial neural network model for eutrophication evaluation of China eastern was constructed, and a new approach producing training set data, testing set data and critical values set data distributed a normal distribution between the critical values was established. The model was applied to 4 eastern lakes and the results shows that the method is suitable for eutrophication assessment of lakes.

Key words

eutrophication artificial neural network lake assessment 

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References

  1. [1]
    Wang shitong, ANN system and its appliance. Beijing University of Aeronautics & Astronautics press, 1997:37-43Google Scholar
  2. [2]
    Xie Ping, Li De. A lake eutrophication stochastic assessment method by using Bayesian formula and its verification. Resources and Environment in the Yangtze Basin, 2005, 3:224–228Google Scholar
  3. [3]
    Zhao zhenyu. Fundamental and utilize of Fuzzy theory and ANN. Qinghua university press, Guangxi science and technology press, 1996:23-58Google Scholar
  4. [4]
    Wang X, Li T, Xu A. Study of the distribution of non-point source pollution in the watershed of the Miyun Reservoir, Beijing, China[J].Water Sci Technol, 2001, 44(7):35–40CrossRefGoogle Scholar
  5. [5]
    Sakamoto, M, Primary production by phytoplankton community in some Japanese lake and its dependence on lake depth [J].Archiv fur Hgdrobiologie, 1996, 62:1–28Google Scholar
  6. [6]
    Shu jinhua. Study on the methods of the eutrophication evaluation of China lakes [J]. Environmental Pollution & Prevention, 1990, 12(5):2–7Google Scholar
  7. [7]
    Cai yudong. Neural network model for water quality nutrition evaluation of lakes. China Environmental Science, 1995, 2(4).:123–127Google Scholar
  8. [8]
    Liu Shouwen, Feng shangyou. Application of Artificial Neural Network to Evaluation of Lake Eutrophication. Shanghai Environmental Science, 1996:11-14Google Scholar
  9. [9]
    Liu xia, Du guisen. Phytoplankton and nutrient degree of water body in Miyun Reservoir. Research of Environmental Sciences, 2003, 16:27–29Google Scholar
  10. [10]
    Jin xiangcan, Liu hongliang. eutrophication of china lakes. China environmental science press, 1990:136-229Google Scholar
  11. [11]
    Lai tinghe, Wei manxin. The relation of change of five nutrients and environmental factors in Lianzhou Bay. Journal of Guangxi Academy of Sciences, 2003, 2:.35–39Google Scholar
  12. [12]
    Tang wanying, Yang yuchuan, Huang gang. Studies on BP neural network in synthesize polluting evaluation of Nitrogen in water. Comp. & Applied Chem, 2002, 7(19):438–440Google Scholar
  13. [13]
    Qin qiurong, Long xiaohong. Assessment on eutrophication of Beihai offshore. Marine environmental science, 2000, 5:43–45Google Scholar
  14. [14]
    Jiang tao, Liu Zufa. Assessment of reservoir eutrophication in Guangdong Province. J.Lake Sci, 2005, 17(4):378–382CrossRefGoogle Scholar
  15. [15]
    Duan huanfeng, Yu guoping. Discussion about methods of assessment for water eutrophication. J. of University of science and technology of Suzhou(engineering and technology), 2005, 6, 18 (2): 53–57Google Scholar

Copyright information

© China Ship Scientific Research Center 2006

Authors and Affiliations

  1. 1.College of Environmental Science and EngineeringTongji UniversityShanghaiChina

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