Environmental Monitoring and Assessment

, Volume 185, Issue 5, pp 4361–4371 | Cite as

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

  • Xiaohu WenEmail author
  • Jing Fang
  • Meina Diao
  • Chuanqi Zhang


Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl), calcium (Ca2+), total alkalinity, total hardness, nitrate nitrogen (NO3-N), and ammonical nitrogen (NH4-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO3-N, NH4-N, and Ca2+. Cl was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.


Artificial neural network Dissolved oxygen Modeling Heihe River 



This work was supported by the One Hundred Person Project of the Chinese Academy of Sciences (29Y127D01), National Natural Science Foundation of China (41171026, 91025024). The author wishes to thank the anonymous reviewers for their reading of the manuscript, and for their suggestions and critical comments.


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Xiaohu Wen
    • 1
    • 2
    Email author
  • Jing Fang
    • 3
  • Meina Diao
    • 1
    • 2
    • 4
  • Chuanqi Zhang
    • 1
    • 2
    • 4
  1. 1.Key Laboratory of Coastal Zone Environmental ProcessesYantai Institute of Coastal Zone Research, Chinese Academy of SciencesYantaiChina
  2. 2.Shandong Provincial Key Laboratory of Coastal Zone Environmental ProcessesYantai Institute of Coastal Zone Research, Chinese Academy of SciencesYantaiChina
  3. 3.Cold and Arid Regions Environmental and Engineering Research InstituteChinese Academy of SciencesLanzhouChina
  4. 4.University of Chinese Academy of SciencesBeijingChina

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