Dissolved oxygen (DO) concentrations have been used as primary indicator of stream water quality. A problem of great social importance is determining how to best retain the quality of stream water and maintain DO concentrations using various pollution control activities. This paper presents the use of artificial neural network (ANN) technique to estimate the DO concentrations at the downstream of Mathura city, India, located at the bank of River Yamuna in the state of Uttar Pradesh, India. In the analysis, the most commonly used feed forward error back propagation neural network technique has been applied. Monthly data sets on flow discharge, temperature, pH, biochemical oxygen demand (BOD) and dissolved oxygen (DO) at three locations, namely, Mathura (upstream), Mathura (central) and Mathura (downstream) have been used for the analysis. Feed forward error back propagation algorithm, the most commonly used ANN technique, was used to develop three types of ANN models using different combinations of input variables and input stations, namely: (a) All the data sets for stations Mathura (upstream), Mathura (central) and Mathura (downstream) except DO values at Mathura (downstream) (b) All data sets for the stations Mathura (upstream), and Mathura (central), and (c) All the data sets for the stations Mathura (upstream). The performance of the ANN technique has been evaluated using statistical tools (in terms of root mean square error and coefficient of correlation). The predicted values of DO showed prominent accuracy by producing high correlations (upto 0.9) between measured and predicted values.
[1]
B. Yegnanarayana,et al.
Artificial Neural Networks
,
2004
.
[2]
Kishan G. Mehrotra,et al.
Forecasting the behavior of multivariate time series using neural networks
,
1992,
Neural Networks.
[3]
V. Rao Vemuri,et al.
Artificial neural networks: Concepts and control applications
,
1992
.
[4]
Nachimuthu Karunanithi,et al.
Neural Networks for River Flow Prediction
,
1994
.
[5]
Simon Haykin,et al.
Neural Networks: A Comprehensive Foundation
,
1998
.
[6]
Bernard Widrow,et al.
Adaptive switching circuits
,
1988
.
[7]
A. H. Harker,et al.
Multi-Variate Financial Index Prediction - A Neural Network Study
,
1990
.
[8]
Geoffrey E. Hinton,et al.
Learning representations by back-propagating errors
,
1986,
Nature.
[9]
Kuolin Hsu,et al.
Artificial Neural Network Modeling of the Rainfall‐Runoff Process
,
1995
.
[10]
Teuvo Kohonen,et al.
Self-Organization and Associative Memory
,
1988
.
[11]
Qiwen Wang,et al.
Predicting salinity in the chesapeake bay using backpropagation
,
1992,
Comput. Oper. Res..
[12]
Kevin Swingler,et al.
Applying neural networks - a practical guide
,
1996
.
[13]
J. David Fuller,et al.
Backpropagation in Hydrological Time Series Forecasting
,
1994
.
[14]
J. J. Hopfield,et al.
“Neural” computation of decisions in optimization problems
,
1985,
Biological Cybernetics.
[15]
S. Grossberg.
Studies of mind and brain : neural principles of learning, perception, development, cognition, and motor control
,
1982
.