Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Artificial neural network modelling of concentrations of nitrogen, phosphorus and dissolved... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Hydrological Processes Wiley

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

Hydrological Processes , Volume 24 (3) – Jan 30, 2010

Loading next page...
 
/lp/wiley/artificial-neural-network-modelling-of-concentrations-of-nitrogen-hkKKSWwfph

References (49)

Publisher
Wiley
Copyright
Copyright © 2009 John Wiley & Sons, Ltd.
ISSN
0885-6087
eISSN
1099-1085
DOI
10.1002/hyp.7482
Publisher site
See Article on Publisher Site

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.

Journal

Hydrological ProcessesWiley

Published: Jan 30, 2010

There are no references for this article.