Application of Artificial Neural Network to Classification Surface Water Quality
Water quality is a subject of ongoing concern.
Deterioration of water quality has initiated serious management
efforts in many countries. This study endeavors to automatically
classify water quality. The water quality classes are evaluated using 6
factor indices. These factors are pH value (pH), Dissolved Oxygen
(DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen
(NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (TColiform).
The methodology involves applying data mining
techniques using multilayer perceptron (MLP) neural network
models. The data consisted of 11 sites of canals in Dusit district in
Bangkok, Thailand. The data is obtained from the Department of
Drainage and Sewerage Bangkok Metropolitan Administration
during 2007-2011. The results of multilayer perceptron neural
network exhibit a high accuracy multilayer perception rate at 96.52%
in classifying the water quality of Dusit district canal in Bangkok
Subsequently, this encouraging result could be applied with plan and
management source of water quality.
[1] H. Murdun, "Application of an unsupervised artificial neural network
technique to multivariant surface water quality data," The Ecological
Society, 2009, 24: 163-173
[2] Walley, W.J., DZeroski ,S Biological monitoring "A Comparison
between Bayesian, neural and machine learning methods of water quality
classification", International Symposium on Environmental Software
System, 1996.
[3] Li, Y., Jiang, J.H., Chen, Z.P., Xu, C.J., Yu, R.Q.: A New Method Based
on Counter Propagation Network Algorithm for Chemical Pattern
Recognition , 1999, pp. 161-170.
[4] L.Khuan, N.Hamzah and R Jailani, "Prediction of Water Quality
Index(WQI) Based on Artificial Neural Network(ANN)",Conference on
Research and Development Proceedings, Malasia, 2002, pp. 157-161.
[5] A.Najah, A.Elshafie,O.K arim and O.Jaffar "Prediction of Johor River
Water Qualit y Parameter Using Artificial Neural Networks", Journal
0f Scientific Research, EuroJournals Publishing, 2009, pp. 422-435.
[6] Breiman L, FriedmanJH, Olshen RA, and Stone CJ "Classification and
Regression Tree" Champman &Hall (Wadsworth, Inc.), Newyork, 1984.
[7] 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", Journal 0f
Operational Research, Springer-Verlag, Jan 2005, pp. 115-125.
[8] S.Areerachakul and S.Sanguansintukul "Water Classification Using
Neural Network: A Case Study of Canals in Bangkok, Thailand", The
4th International Conference for Internet Technology and Secured
Transactions (ICITST-2009), United Kingdom, 2009.
[9] Department of Drainage and Sewerage Bangkok Metropolitan
Administration, http://dds.bangkok.go.th/wqm/thai/home.html
[10] Simon Haykin, "Neural Networks:A Comprehensive foundation second
edition ", Pearson Prentice Hall, Delhi India, 2005.
[11] Ministry of Natural Resources and Environment:
http://www.mnre.go.th/
[12] S.Areerachakul and S.Sanguansintukul "Classification and Regression
Trees and MLP Neural Network to Classify Water Quality of Canals in
Bangkok, Thailand", International Journal of Intelligent Computing
Research (IJICR), 2010.
[13] D.Marquardt, "An Algorithm for Least Squares Estimation of Non-
Linear Parameter" , J. Soc. Ind. Appl.Math., pp. 1963.
[14] L.Fausett, "Fundamentals of Neural Networks Architecture.Algorithms
and Applications", Pearson Prentice Hall, USA, 1994.
[15] 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", Journal 0f
Operational Research, Springer-Verlag, Jan 2005, pp. 115-125.
[16] D. Anguita, S.Ridella and F.Rivieccio, "K-folds Generalization
Capability Assessment for Support Vector Classifiers", Proceeding of
International Joint Conference on Neural Network ,
Canada,2005,PP.855-858.
[17] Li-hua Chen, and Xiao-yun Zhang, "Application of Artificial Neural
Network to Classify Water Quality of the Yellow River", Journal 0f
Fuzzy Information and Engineering , Springer-Verlag, Jan 2009, pp. 15-
23.
[18] 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", Journal 0f
Operational Research, Springer-Verlag, Jan 2005, pp. 115-125.
[19] S.H.Musavi and M.Golabi "Application of Artificial Neural Networks in
the River Water Quality Modeling: Karoon River,Iran", Journal 0f
Applied Sciences, A sian Network for Scientific Information, 2008, pp.
2324-2328.
[20] Chi Zhou, Liang Gao and Chuanyong Peng, "Pattern Classification and
Prediction of Water Quality by Neural Network with Particle Swarm
Optimization", Proceedings of the 6th World Congress on Intelligent
Control and Automation , China, June 2006, pp. 2864-2868.
[21] Martin T.Hagen and Mohammad B.Menhaj, "Training Feedforward
Networks with the Marquardt Algorithm", IEEE Transactions on Neural
Networks,vol.5, no.6,Nov 1994, pp.989-993.
[22] M.Yesilnacar, E.Sahinkaya and M.Naz "Neural network prediction of
nitrate in groundwater of Harran Plain,Turkey", Journal 0f
Environmental Geology, Springer-Verlag, 2007, pp. 19-25.
[23] A. Gamal El-Din, D.W Smith and M. Gamal El-Din, "Application of
artificial neural networks in wastewater treatment," J. Environ. Eng Sci.,
pp.81-95, Jan 2004.
[24] A. Jain, A.K. Varshney and U.C. Joshi , "Short-term Water Demand
Forecast Modeling ai IIT Kanpur Using Artificial Neural Networks,"
IEEE Transactions on Water Resources Management, vol. 15, no.1,
pp.299-321, Aug 2001.
[25] H.R. Maier and G.C. Dandy, "Neural networks for the prediction and
forecasting of water resources variables: a review of modeling issues and
applications," Environmental Modeling and Software, pp.101-124, Jan
2000.
[26] Baughman, D.R. and Liu, Y.A., (1990).Neural Networks in
Bioprocessing and Chemical Engineering. Academic Press, New York.
[27] Hubick, K.T., (1992). Artificial neural networks in Australia.
Department of Industry, Technology and Commerce, Commonwealth of
Australia, Canberra.
[28] Crespo, J. L. and Mora, E., (1993). Drought estimation with neural
networks. Advances in Engineering Software, 18, p.167-170.
[29] Karunanithi, N., Grenney, W. J., Whiley, B., and Bovee, K., (1994).
Neural Networks for River Flow Prediction. Journal Computing in Civil
Engineering,8, p.201-209.
[30] Hsu, K., Gupta, H. V. and Sorooshian, S., (1995). Artificial neural
network modeling of the rainfall-runoff process. Wat. Resour. Res., 31,
p. 2517-2530
[31] Abrahart, R. J. and Kneale, P. E., (1997). Exploring neural network
rainfall-runoff modeling. BHS National Hydrology Symposium, Salford,
UK, 9.35-9.43.
[32] Dawson, C. W., and Wilby, R., (1998). An artificial neural network
approach to rainfall-runoff modeling. Hydro. Sci. J., 43, p.47-66.
[33] Thirumalaiah, K. and Deo, M.C., 2000. Hydrological forecasting
usingartificial neural networks. ASCE Journal of Hydrologic
Engineering 5, p.145-163.
[34] Cigizoglu, H.K., (2004). Estimation and forecasting of daily suspended
sediment data by multi-layer perceptrons. Advances in Water Resources
27,p.185-195
[1] H. Murdun, "Application of an unsupervised artificial neural network
technique to multivariant surface water quality data," The Ecological
Society, 2009, 24: 163-173
[2] Walley, W.J., DZeroski ,S Biological monitoring "A Comparison
between Bayesian, neural and machine learning methods of water quality
classification", International Symposium on Environmental Software
System, 1996.
[3] Li, Y., Jiang, J.H., Chen, Z.P., Xu, C.J., Yu, R.Q.: A New Method Based
on Counter Propagation Network Algorithm for Chemical Pattern
Recognition , 1999, pp. 161-170.
[4] L.Khuan, N.Hamzah and R Jailani, "Prediction of Water Quality
Index(WQI) Based on Artificial Neural Network(ANN)",Conference on
Research and Development Proceedings, Malasia, 2002, pp. 157-161.
[5] A.Najah, A.Elshafie,O.K arim and O.Jaffar "Prediction of Johor River
Water Qualit y Parameter Using Artificial Neural Networks", Journal
0f Scientific Research, EuroJournals Publishing, 2009, pp. 422-435.
[6] Breiman L, FriedmanJH, Olshen RA, and Stone CJ "Classification and
Regression Tree" Champman &Hall (Wadsworth, Inc.), Newyork, 1984.
[7] 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", Journal 0f
Operational Research, Springer-Verlag, Jan 2005, pp. 115-125.
[8] S.Areerachakul and S.Sanguansintukul "Water Classification Using
Neural Network: A Case Study of Canals in Bangkok, Thailand", The
4th International Conference for Internet Technology and Secured
Transactions (ICITST-2009), United Kingdom, 2009.
[9] Department of Drainage and Sewerage Bangkok Metropolitan
Administration, http://dds.bangkok.go.th/wqm/thai/home.html
[10] Simon Haykin, "Neural Networks:A Comprehensive foundation second
edition ", Pearson Prentice Hall, Delhi India, 2005.
[11] Ministry of Natural Resources and Environment:
http://www.mnre.go.th/
[12] S.Areerachakul and S.Sanguansintukul "Classification and Regression
Trees and MLP Neural Network to Classify Water Quality of Canals in
Bangkok, Thailand", International Journal of Intelligent Computing
Research (IJICR), 2010.
[13] D.Marquardt, "An Algorithm for Least Squares Estimation of Non-
Linear Parameter" , J. Soc. Ind. Appl.Math., pp. 1963.
[14] L.Fausett, "Fundamentals of Neural Networks Architecture.Algorithms
and Applications", Pearson Prentice Hall, USA, 1994.
[15] 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", Journal 0f
Operational Research, Springer-Verlag, Jan 2005, pp. 115-125.
[16] D. Anguita, S.Ridella and F.Rivieccio, "K-folds Generalization
Capability Assessment for Support Vector Classifiers", Proceeding of
International Joint Conference on Neural Network ,
Canada,2005,PP.855-858.
[17] Li-hua Chen, and Xiao-yun Zhang, "Application of Artificial Neural
Network to Classify Water Quality of the Yellow River", Journal 0f
Fuzzy Information and Engineering , Springer-Verlag, Jan 2009, pp. 15-
23.
[18] 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", Journal 0f
Operational Research, Springer-Verlag, Jan 2005, pp. 115-125.
[19] S.H.Musavi and M.Golabi "Application of Artificial Neural Networks in
the River Water Quality Modeling: Karoon River,Iran", Journal 0f
Applied Sciences, A sian Network for Scientific Information, 2008, pp.
2324-2328.
[20] Chi Zhou, Liang Gao and Chuanyong Peng, "Pattern Classification and
Prediction of Water Quality by Neural Network with Particle Swarm
Optimization", Proceedings of the 6th World Congress on Intelligent
Control and Automation , China, June 2006, pp. 2864-2868.
[21] Martin T.Hagen and Mohammad B.Menhaj, "Training Feedforward
Networks with the Marquardt Algorithm", IEEE Transactions on Neural
Networks,vol.5, no.6,Nov 1994, pp.989-993.
[22] M.Yesilnacar, E.Sahinkaya and M.Naz "Neural network prediction of
nitrate in groundwater of Harran Plain,Turkey", Journal 0f
Environmental Geology, Springer-Verlag, 2007, pp. 19-25.
[23] A. Gamal El-Din, D.W Smith and M. Gamal El-Din, "Application of
artificial neural networks in wastewater treatment," J. Environ. Eng Sci.,
pp.81-95, Jan 2004.
[24] A. Jain, A.K. Varshney and U.C. Joshi , "Short-term Water Demand
Forecast Modeling ai IIT Kanpur Using Artificial Neural Networks,"
IEEE Transactions on Water Resources Management, vol. 15, no.1,
pp.299-321, Aug 2001.
[25] H.R. Maier and G.C. Dandy, "Neural networks for the prediction and
forecasting of water resources variables: a review of modeling issues and
applications," Environmental Modeling and Software, pp.101-124, Jan
2000.
[26] Baughman, D.R. and Liu, Y.A., (1990).Neural Networks in
Bioprocessing and Chemical Engineering. Academic Press, New York.
[27] Hubick, K.T., (1992). Artificial neural networks in Australia.
Department of Industry, Technology and Commerce, Commonwealth of
Australia, Canberra.
[28] Crespo, J. L. and Mora, E., (1993). Drought estimation with neural
networks. Advances in Engineering Software, 18, p.167-170.
[29] Karunanithi, N., Grenney, W. J., Whiley, B., and Bovee, K., (1994).
Neural Networks for River Flow Prediction. Journal Computing in Civil
Engineering,8, p.201-209.
[30] Hsu, K., Gupta, H. V. and Sorooshian, S., (1995). Artificial neural
network modeling of the rainfall-runoff process. Wat. Resour. Res., 31,
p. 2517-2530
[31] Abrahart, R. J. and Kneale, P. E., (1997). Exploring neural network
rainfall-runoff modeling. BHS National Hydrology Symposium, Salford,
UK, 9.35-9.43.
[32] Dawson, C. W., and Wilby, R., (1998). An artificial neural network
approach to rainfall-runoff modeling. Hydro. Sci. J., 43, p.47-66.
[33] Thirumalaiah, K. and Deo, M.C., 2000. Hydrological forecasting
usingartificial neural networks. ASCE Journal of Hydrologic
Engineering 5, p.145-163.
[34] Cigizoglu, H.K., (2004). Estimation and forecasting of daily suspended
sediment data by multi-layer perceptrons. Advances in Water Resources
27,p.185-195
@article{"International Journal of Earth, Energy and Environmental Sciences:58199", author = "S. Wechmongkhonkon and N.Poomtong and S. Areerachakul", title = "Application of Artificial Neural Network to Classification Surface Water Quality", abstract = "Water quality is a subject of ongoing concern.
Deterioration of water quality has initiated serious management
efforts in many countries. This study endeavors to automatically
classify water quality. The water quality classes are evaluated using 6
factor indices. These factors are pH value (pH), Dissolved Oxygen
(DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen
(NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (TColiform).
The methodology involves applying data mining
techniques using multilayer perceptron (MLP) neural network
models. The data consisted of 11 sites of canals in Dusit district in
Bangkok, Thailand. The data is obtained from the Department of
Drainage and Sewerage Bangkok Metropolitan Administration
during 2007-2011. The results of multilayer perceptron neural
network exhibit a high accuracy multilayer perception rate at 96.52%
in classifying the water quality of Dusit district canal in Bangkok
Subsequently, this encouraging result could be applied with plan and
management source of water quality.", keywords = "artificial neural network, classification, surface
water quality", volume = "6", number = "9", pages = "618-5", }
