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

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Abstract

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.

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Acknowledgments

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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Authors and Affiliations

  1. Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Chunhui Rd 17, Yantai, 264003, Shandong Province, China

    Xiaohu Wen, Meina Diao & Chuanqi Zhang

  2. Shandong Provincial Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Chunhui Rd 17, Yantai, 264003, Shandong Province, China

    Xiaohu Wen, Meina Diao & Chuanqi Zhang

  3. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, West Donggang Rd 320, Lanzhou, 730000, Gansu Province, China

    Jing Fang

  4. University of Chinese Academy of Sciences, 19A Yuquanlu, 100049, Beijing, China

    Meina Diao & Chuanqi Zhang

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  1. Xiaohu Wen
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Correspondence to Xiaohu Wen.

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Wen, X., Fang, J., Diao, M. et al. Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China. Environ Monit Assess 185, 4361–4371 (2013). https://doi.org/10.1007/s10661-012-2874-8

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