Artificial neural network modelling of concentrations of nitrogen, phosphorus and dissolved oxygen in a non-point source polluted river in Zhejiang Province, southeast China

Authors

  • Dingjiang Chen,

    1. Department of Natural Resources, College of Environmental Science and Natural Resources, Zhejiang University, Hangzhou 310029, China
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  • Jun Lu,

    Corresponding author
    1. Department of Natural Resources, College of Environmental Science and Natural Resources, Zhejiang University, Hangzhou 310029, China
    2. China Ministry of Education Key Lab of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou 310029, China
    • Department of Natural Resources, College of Environmental Science and Natural Resources, Zhejiang University, Kaixuan Road 258#, Hangzhou 310029, Zhejiang Province, China.
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  • Yena Shen

    1. Zhejiang Provincial Key Laboratory of Subtropical Soil and Plant Nutrition, Zhejiang University, Hangzhou 310029, China
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Abstract

A back-propagation algorithm neural network (BPNN) was developed to synchronously simulate concentrations of total nitrogen (TN), total phosphorus (TP) and dissolved oxygen (DO) in response to agricultural non-point source pollution (AGNPS) for any month and location in the Changle River, southeast China. Monthly river flow, water temperature, flow travel time, rainfall and upstream TN, TP and DO concentrations were selected as initial inputs of the BPNN through coupling correlation analysis and quadratic polynomial stepwise regression analysis for the outputs, i.e. downstream TN, TP and DO concentrations. The input variables and number of hidden nodes of the BPNN were then optimized using a combination of growing and pruning methods. The final structure of the BPNN was determined from simulated data based on experimental data for both the training and validation phases. The predicted values obtained using a BPNN consisting of the seven initial input variables (described above), one hidden layer with four nodes and three output variables matched well with observed values. The model indicated that decreasing upstream input concentrations during the dry season and control of NPS along the reach during average and flood seasons may be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data are available, the methodology developed here can easily be applied to other case studies. The BPNN model is an easy-to-use modelling tool for managers to obtain rapid preliminary identification of spatiotemporal water quality variations in response to natural and artificial modifications of an agricultural drainage river. Copyright © 2009 John Wiley & Sons, Ltd.

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