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

机译：黑河地区溶解氧的人工神经网络建模

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摘要

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 (Ca~(2+)), total alkalinity, total hardness, nitrate nitrogen (NO_3-N), and ammonical nitrogen (NH_4-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, NO_3-N, NH_4-N, and Ca~(2+). 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.
机译：识别和量化河流中的溶解氧（DO）曲线是水资源管理者最关注的问题之一。在这项研究中，开发了一个人工神经网络（ANN）来模拟中国西北黑河中的DO浓度。贝叶斯正则化训练算法使用了三层反向传播ANN。神经网络的输入变量为pH，电导率，氯化物（Cl〜-），钙（Ca〜（2+）），总碱度，总硬度，硝酸盐氮（NO_3-N）和氨氮（NH_4- N）。具有14个隐藏神经元的ANN结构获得了最佳选择。通过在相关系数（r）和均方根误差（RMSE）的基础上对ANN模型的结果与测量数据进行比较，良好的模型拟合DO值表明了神经网络模型的有效性。发现训练，验证和测试集的相关系数（r）值分别为0.9654、0.9841和0.9680，而训练，验证和测试集的RMSE的分别值为0.4272、0.3667 ，和0.4570。灵敏度分析用于确定输入变量对因变量的影响。确定最有效的输入为pH，NO_3-N，NH_4-N和Ca〜（2+）。在建议的模型中，发现Cl〜-是最不有效的变量。所识别的ANN模型可用于模拟水质参数。

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  • 来源
    《Environmental Monitoring and Assessment》 |2013年第5期|4361-4371|共11页
  • 作者

    Xiaohu Wen; Jing Fang; Meina Diao; Chuanqi Zhang;

  • 作者单位

    Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research, ChineseAcademy of Sciences,Chunhui Rd 17,Yantai 264003 Shandong Province, China,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;

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

    Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research, ChineseAcademy of Sciences,Chunhui Rd 17,Yantai 264003 Shandong Province, China,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,Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences,West Donggang Rd 320,Lanzhou 730000 Gansu Province, China;

    Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research, ChineseAcademy of Sciences,Chunhui Rd 17,Yantai 264003 Shandong Province, China,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,Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences,West Donggang Rd 320,Lanzhou 730000 Gansu Province, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    artificial neural network; dissolved oxygen; modeling; heihe river;

    机译：人工神经网络;溶解氧;造型;黑河;

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