The Using Artificial Neural Network to Estimate of Chemical Oxygen Demand
Nowadays, the increase of human population every
year results in increasing of water usage and demand. Saen Saep
canal is important canal in Bangkok. The main objective of this study
is using Artificial Neural Network (ANN) model to estimate the
Chemical Oxygen Demand (COD) on data from 11 sampling sites.
The data is obtained from the Department of Drainage and Sewerage,
Bangkok Metropolitan Administration, during 2007-2011. The
twelve parameters of water quality are used as the input of the
models. These water quality indices affect the COD. The
experimental results indicate that the ANN model provides a high
correlation coefficient (R=0.89).
<p>[1] A. Talib and M. I. Amat, “Prediction of Chemical Oxygen Demand In
Dondang River Using Artificial Neural Network,” June , 2012
[2] Q. Cong,W. Yu, and T. Chai, “Hierarchical Neural Network Model for
Water Quality Prediction in Waste water Treatment Plants”
[3] Talib, A., Y. Abu Hasan and N.N. Abdul Rahman, Predicting
Biochemical Oxygen Demand As Indicator Of River Pollution Using
Artificial Neural Networks, present at the World IMACS / MODSIM
Congress, Cairns, Australia, July 13-17, 2009
[4] S. Palani, S. Liong, P. Tkalich and J. Palanichamy. Development of a
neural network for dissolved oxygen in seawater. Indian Journal of
Marine Science. 2009, pp.151-159.
[5] Cox, B. A., A review of dissolved oxygen modeling techniques for
lowland rivers, The Science of the Total Environment, 2003, pp. 303-
334.
[6] M. J. Diamantopoulou, V. Z. Antonopoulos and D. M. Papamichail,
“The Use of a Neural Network Technique for the Prediction of Water
Quality Parameters of Axios River in Northern Greece”, European
Water, 2005, pp.55-62.
[7] G. Civelekoglu, N. O. Yigit, E. Diamadopoulos and M. Kitis,
“Prediction of Bromate Formation Using Multi-Linear Regression and
Artificial Neural Networks”, Journal of Ozone Science and Engineer,
Taylor & Francis, vol.5, no.5, 2007, pp.353-362.
[8] Simon Haykin, “Neural Networks:A Comprehensive foundation second
edition”, Pearson Prentice Hall, Delhi India, 2005.
[9] S. H. Musavi and M. Golabi “Application of Artificial Neural Networks
in the River Water Quality Modeling: Karoon River, Iran”, Journal 0f
Applied Sciences, Asian Network for Scientific Information, 2008, pp.
2324-2328.
[10] S. Areerachakul , “Comparison of ANFIS and ANN for Estimation of
Biochemical Oxygen Demand Parameter in Surface Water”, World
academy of Science, Engineering and Technology, 2012,pp.378-382.
[11] L. Fausett, “Fundamentals of Neural Networks Architecture. Algorithms
and Applications”, Pearson Prentice Hall, USA, 1994.
[12] Ministry of Natural Resource and Environment, http://www.mnre.go.th.</p>
<p>[1] A. Talib and M. I. Amat, “Prediction of Chemical Oxygen Demand In
Dondang River Using Artificial Neural Network,” June , 2012
[2] Q. Cong,W. Yu, and T. Chai, “Hierarchical Neural Network Model for
Water Quality Prediction in Waste water Treatment Plants”
[3] Talib, A., Y. Abu Hasan and N.N. Abdul Rahman, Predicting
Biochemical Oxygen Demand As Indicator Of River Pollution Using
Artificial Neural Networks, present at the World IMACS / MODSIM
Congress, Cairns, Australia, July 13-17, 2009
[4] S. Palani, S. Liong, P. Tkalich and J. Palanichamy. Development of a
neural network for dissolved oxygen in seawater. Indian Journal of
Marine Science. 2009, pp.151-159.
[5] Cox, B. A., A review of dissolved oxygen modeling techniques for
lowland rivers, The Science of the Total Environment, 2003, pp. 303-
334.
[6] M. J. Diamantopoulou, V. Z. Antonopoulos and D. M. Papamichail,
“The Use of a Neural Network Technique for the Prediction of Water
Quality Parameters of Axios River in Northern Greece”, European
Water, 2005, pp.55-62.
[7] G. Civelekoglu, N. O. Yigit, E. Diamadopoulos and M. Kitis,
“Prediction of Bromate Formation Using Multi-Linear Regression and
Artificial Neural Networks”, Journal of Ozone Science and Engineer,
Taylor & Francis, vol.5, no.5, 2007, pp.353-362.
[8] Simon Haykin, “Neural Networks:A Comprehensive foundation second
edition”, Pearson Prentice Hall, Delhi India, 2005.
[9] S. H. Musavi and M. Golabi “Application of Artificial Neural Networks
in the River Water Quality Modeling: Karoon River, Iran”, Journal 0f
Applied Sciences, Asian Network for Scientific Information, 2008, pp.
2324-2328.
[10] S. Areerachakul , “Comparison of ANFIS and ANN for Estimation of
Biochemical Oxygen Demand Parameter in Surface Water”, World
academy of Science, Engineering and Technology, 2012,pp.378-382.
[11] L. Fausett, “Fundamentals of Neural Networks Architecture. Algorithms
and Applications”, Pearson Prentice Hall, USA, 1994.
[12] Ministry of Natural Resource and Environment, http://www.mnre.go.th.</p>
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:57695", author = "S. Areerachakul", title = "The Using Artificial Neural Network to Estimate of Chemical Oxygen Demand", abstract = "Nowadays, the increase of human population every
year results in increasing of water usage and demand. Saen Saep
canal is important canal in Bangkok. The main objective of this study
is using Artificial Neural Network (ANN) model to estimate the
Chemical Oxygen Demand (COD) on data from 11 sampling sites.
The data is obtained from the Department of Drainage and Sewerage,
Bangkok Metropolitan Administration, during 2007-2011. The
twelve parameters of water quality are used as the input of the
models. These water quality indices affect the COD. The
experimental results indicate that the ANN model provides a high
correlation coefficient (R=0.89).
", keywords = "Artificial neural network, chemical oxygen demand,
estimate, surface water.", volume = "7", number = "7", pages = "547-4", }
