The Using Artificial Neural Network to Estimate of Chemical Oxygen Demand
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33104
The Using Artificial Neural Network to Estimate of Chemical Oxygen Demand

Authors: S. Areerachakul

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087470

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2268

References:


[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.