Comparison of ANFIS and ANN for Estimation of Biochemical Oxygen Demand Parameter in Surface Water
Nowadays, several techniques such as; Fuzzy
Inference System (FIS) and Neural Network (NN) are employed for
developing of the predictive models to estimate parameters of water
quality. The main objective of this study is to compare between the
predictive ability of the Adaptive Neuro-Fuzzy Inference System
(ANFIS) model and Artificial Neural Network (ANN) model to
estimate the Biochemical Oxygen Demand (BOD) on data from 11
sampling sites of Saen Saep canal in Bangkok, Thailand. The data is
obtained from the Department of Drainage and Sewerage, Bangkok
Metropolitan Administration, during 2004-2011. The five parameters
of water quality namely Dissolved Oxygen (DO), Chemical Oxygen
Demand (COD), Ammonia Nitrogen (NH3N), Nitrate Nitrogen
(NO3N), and Total Coliform bacteria (T-coliform) are used as the
input of the models. These water quality indices affect the
biochemical oxygen demand. The experimental results indicate that
the ANN model provides a higher correlation coefficient (R=0.73)
and a lower root mean square error (RMSE=4.53) than the
corresponding ANFIS model.
[1] D. Chapman, "Water Quality Assesment," 1st ed. London: Chapman
and Hall, 1992, pp. 80-81.
[2] M. Radwan, P.Willems, and J.Berlamomnt, "Modeling of dissolved
oxygen and biochemical oxygen demand in river water using a detailed
and simplified model,"2nd ed. River Basin Manage, 2003, pp. 97-103.
[3] J.F. Lopes, J.M Dias and A.C Cadoso, "The water quality of the Ria de
Aveiro lagoon, Portugal: from the observations to the implementation of
a numerical model," Mar Environ Res, pp.594-628.
[4] G.C Delzer, S.W Mckenzie, "Five -day Biochemical Oxygen Demand,"
U.S .Geological Survey TWRI Book vol.9, 1999 pp.25-99.
[5] J.P Suen, J.W Eheart and M.Asce, "Evaluation of neural networks for
modeling nitrate concentration in rivers," J Water Res Plan Manage,
pp.505-510.
[6] P.A Aguilera, A.G Frenich and J.A Torres, "Application of the Konohen
neural network in coastal water management: methodological
development for the assessment and prediction of water quality," Water
Res, 2001, pp.4053-4062.
[7] 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.
[8] 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.
[9] 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.
[10] 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, pp.353-362, Sep 2007.
[11] S.Msiza, V.Nelwamondo and T.Marwala, "Water Demand Prediction
using Artificial Neural Networks and Support Vector Regression,"
Journal of Computers, vol. 3, no.11, Nov 2008.
[12] Dogan, E., Sengorur, B., Koklu, Robia. :Modeling biological oxygen
demand of the Melen River in Turkey using an artificial neural network
technique. :Journal of Environmental Management (2008)
[13] Simon Haykin, "Neural Networks:A Comprehensive foundation second
edition", Pearson Prentice Hall, Delhi India, 2005.
[14] 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.
[15] L.Fausett, "Fundamentals of Neural Networks Architecture.Algorithms
and Applications", Pearson Prentice Hall, USA, 1994.
[16] S.Areerachakul and S.Sanguansintukul "A Comparison between the
Multiple Linear Regression Model and Neural Networks for
Biochemical Oxygen Demand Estimations", The Eight International
Symposium on Natural Language Processing, Thailand, 2009.
[17] Jang, J.S.R., 1993. ANFIS: "Adaptive-network-based fuzzy inference
ystems". IEEE Trans. Syst. Man Cybern, 23: 665-685.
[18] Dastorani, M.T., A. Moghadamnia, J. Piri and M. Rico-Ramirez,
"Application of ANN and ANFIS models for reconstructing missing
flow data". Environ. Monitoring Assess, 2009.
[19] M. Buragohain and C. Mahanta "A novel approach for ANFIS
modelling based on full factorial design" Applied Soft Computing
(2008), pp. 609-625.
[1] D. Chapman, "Water Quality Assesment," 1st ed. London: Chapman
and Hall, 1992, pp. 80-81.
[2] M. Radwan, P.Willems, and J.Berlamomnt, "Modeling of dissolved
oxygen and biochemical oxygen demand in river water using a detailed
and simplified model,"2nd ed. River Basin Manage, 2003, pp. 97-103.
[3] J.F. Lopes, J.M Dias and A.C Cadoso, "The water quality of the Ria de
Aveiro lagoon, Portugal: from the observations to the implementation of
a numerical model," Mar Environ Res, pp.594-628.
[4] G.C Delzer, S.W Mckenzie, "Five -day Biochemical Oxygen Demand,"
U.S .Geological Survey TWRI Book vol.9, 1999 pp.25-99.
[5] J.P Suen, J.W Eheart and M.Asce, "Evaluation of neural networks for
modeling nitrate concentration in rivers," J Water Res Plan Manage,
pp.505-510.
[6] P.A Aguilera, A.G Frenich and J.A Torres, "Application of the Konohen
neural network in coastal water management: methodological
development for the assessment and prediction of water quality," Water
Res, 2001, pp.4053-4062.
[7] 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.
[8] 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.
[9] 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.
[10] 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, pp.353-362, Sep 2007.
[11] S.Msiza, V.Nelwamondo and T.Marwala, "Water Demand Prediction
using Artificial Neural Networks and Support Vector Regression,"
Journal of Computers, vol. 3, no.11, Nov 2008.
[12] Dogan, E., Sengorur, B., Koklu, Robia. :Modeling biological oxygen
demand of the Melen River in Turkey using an artificial neural network
technique. :Journal of Environmental Management (2008)
[13] Simon Haykin, "Neural Networks:A Comprehensive foundation second
edition", Pearson Prentice Hall, Delhi India, 2005.
[14] 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.
[15] L.Fausett, "Fundamentals of Neural Networks Architecture.Algorithms
and Applications", Pearson Prentice Hall, USA, 1994.
[16] S.Areerachakul and S.Sanguansintukul "A Comparison between the
Multiple Linear Regression Model and Neural Networks for
Biochemical Oxygen Demand Estimations", The Eight International
Symposium on Natural Language Processing, Thailand, 2009.
[17] Jang, J.S.R., 1993. ANFIS: "Adaptive-network-based fuzzy inference
ystems". IEEE Trans. Syst. Man Cybern, 23: 665-685.
[18] Dastorani, M.T., A. Moghadamnia, J. Piri and M. Rico-Ramirez,
"Application of ANN and ANFIS models for reconstructing missing
flow data". Environ. Monitoring Assess, 2009.
[19] M. Buragohain and C. Mahanta "A novel approach for ANFIS
modelling based on full factorial design" Applied Soft Computing
(2008), pp. 609-625.
@article{"International Journal of Earth, Energy and Environmental Sciences:52820", author = "S. Areerachakul", title = "Comparison of ANFIS and ANN for Estimation of Biochemical Oxygen Demand Parameter in Surface Water", abstract = "Nowadays, several techniques such as; Fuzzy
Inference System (FIS) and Neural Network (NN) are employed for
developing of the predictive models to estimate parameters of water
quality. The main objective of this study is to compare between the
predictive ability of the Adaptive Neuro-Fuzzy Inference System
(ANFIS) model and Artificial Neural Network (ANN) model to
estimate the Biochemical Oxygen Demand (BOD) on data from 11
sampling sites of Saen Saep canal in Bangkok, Thailand. The data is
obtained from the Department of Drainage and Sewerage, Bangkok
Metropolitan Administration, during 2004-2011. The five parameters
of water quality namely Dissolved Oxygen (DO), Chemical Oxygen
Demand (COD), Ammonia Nitrogen (NH3N), Nitrate Nitrogen
(NO3N), and Total Coliform bacteria (T-coliform) are used as the
input of the models. These water quality indices affect the
biochemical oxygen demand. The experimental results indicate that
the ANN model provides a higher correlation coefficient (R=0.73)
and a lower root mean square error (RMSE=4.53) than the
corresponding ANFIS model.", keywords = "adaptive neuro-fuzzy inference system, artificial
neural network, biochemical oxygen demand, surface water.", volume = "6", number = "4", pages = "168-5", }
