Combined Sewer Overflow forecasting with Feed-forward Back-propagation Artificial Neural Network
A feed-forward, back-propagation Artificial Neural
Network (ANN) model has been used to forecast the occurrences of
wastewater overflows in a combined sewerage reticulation system.
This approach was tested to evaluate its applicability as a method
alternative to the common practice of developing a complete
conceptual, mathematical hydrological-hydraulic model for the
sewerage system to enable such forecasts. The ANN approach
obviates the need for a-priori understanding and representation of the
underlying hydrological hydraulic phenomena in mathematical terms
but enables learning the characteristics of a sewer overflow from the
historical data.
The performance of the standard feed-forward, back-propagation
of error algorithm was enhanced by a modified data normalizing
technique that enabled the ANN model to extrapolate into the
territory that was unseen by the training data. The algorithm and the
data normalizing method are presented along with the ANN model
output results that indicate a good accuracy in the forecasted sewer
overflow rates. However, it was revealed that the accurate
forecasting of the overflow rates are heavily dependent on the
availability of a real-time flow monitoring at the overflow structure
to provide antecedent flow rate data. The ability of the ANN to
forecast the overflow rates without the antecedent flow rates (as is
the case with traditional conceptual reticulation models) was found to
be quite poor.
[1] R. H. Dwight, J. C. Semenza, D. B. Baker and B. H. Olson, "Association
of urban runoff with coastal water quality in Orange County, California"
Water Environment Research, Water Environment Federation, pp. 82-
90, Jan/Feb 2002
[2] Water Environment Research Foundation, "WERF Project Investigates
Sanitary Sewer Overflow Prediction Technologies", Available
http://www.werf.org/press/fall99/page6.cfm.
[3] S. Birikundavyi, R. Labib, T. Trung and J. Rousselle, "Performance of
neural networks in daily streamflow forecasting, Journal of
Hydrological Engineering, Vol. 7, No. 5, pp. 392-398, 2002
[4] A. Jain and S. Srinivasulu, "Development of effective and efficient
rainfall-runoff models using integration of deterministic, real-coded
genetic algorithms and artificial neural networks techniques", Water
Resources Research, Vol. 40, doi:10.1029/2003 WR002355, (2004)
Americal Geophysical Union, 2004.
[5] D. A. K. Fernando and A.W. Jayawardena, "Runoff forecasting using
RBF networks with OLS Algorithm", Journal of Hydrologic
Engineering, ASCE Water resources Engineering division, Vol. 3, No.
3, pp. 203- 209, July 1998.
[6] M. Tomasino, D. Zunchettin and P. Traverso, "Long term forecasts of
River Po discharges based on predictable solar activity and a fuzzy
neural network model, Hydrological Sciences Journal, Vol. 49, No. 4,
pp. 673-684, August 2004.
[7] Y. Lin, Y. Wang, Y. Li, B. Zhang and G. Wu, "Earthquake prediction by
RBF neural network ensemble", Advances in Neural Networks, Proc. of
the International Symposium on Neural networks, China, Ed. F. Yin, J.
Wang, and C. Guo, pp. 962-969, Springer, August 2004.
[8] D. E. Rumelhart, R. Durbin, R. Golden, and Chauvin,
"Backpropagation: The Basic Theory, In Mathematical Perspectives on
Neural Networks", Eds. P. Smolensky, M. C.Mozer, and D. E.
Rumelhart, Lawrence Erlbaum Associates, Mahwah, NJ., pp. 533-566,
1996.
[9] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning internal
representations by error propagation, In Parallel Distributed
Processing, Vol. 1, Eds., D. E. Rumelhart, and J. L. McClelland, MIT
Press, Cambridge, MA. pp. 318-362, 1986.
[10] Wasserman, P.D., Advanced methods in neural computing, Van
Nostrand Reinhold, New York, 1993.
[11] M. A. Rao, J. Srinivas, Neural networks: algorithms and applications,
Pangbourne, Eng.; Alpha Science International, 2003.
[12] Fernando, A.K. and Kerr, T., "Runoff forecasting with Artificial Neural
Network Model", The 3rd Pacific Conference on stormwater and aquatic
resource protection, Auckland, New Zealand Water and Wastes
Association publication of conference proceedings in CD-ROM, 2003.
[13] H. K. Cigizoglu "Estimation, forecasting and extrapolation of river
flows by artificial neural networks" Hydrological Science, Journal, Vol.
48, No. 3, pp. 349-361, June 2003.
[14] M. Campolo, A. Soldati, P. Acdreussi, "Artificial Neural Network
approach to flood forecasting in the River Arno" Hydrological Sciences
Journal, 48(3), pp. 381-398, June 2003
[15] H. R. Maier and G.C. Dandy, "Determining inputs for neural network
models of multivariate time series", Microcomputers in Civil
Engineering, Vol. 12, pp. 353-368, 1997.
[16] S. Amari, N. Murata, K. R. Muller, M. Finke and H.H. Yang,
"Asymptotic statistical theory of overtratining and cross-validation"
IEEE Transactions on Neural Networks, Vol. 8, No. 5, pp. 985-996,
1997.
[17] W. Wang, P.H.A.J.M. Van Gelder and J.K. Vrijling, "Some issues about
the generalization of neural networks for time series prediction" in
Advances in Neural Networks, Proc. of the International Symposium on
Neural networks, August 2004, China, Ed. F. Yin, J. Wang, and C. Guo,
Springer, pp. 559-564, 2004
[18] L. Prechelt, 1998, Early stopping - but when? In G. B. Orr, K-R
Mueller, Eds. Neural networks: Tricks of the trade, Berlin, Springer, pp.
55-99, 1998..
[1] R. H. Dwight, J. C. Semenza, D. B. Baker and B. H. Olson, "Association
of urban runoff with coastal water quality in Orange County, California"
Water Environment Research, Water Environment Federation, pp. 82-
90, Jan/Feb 2002
[2] Water Environment Research Foundation, "WERF Project Investigates
Sanitary Sewer Overflow Prediction Technologies", Available
http://www.werf.org/press/fall99/page6.cfm.
[3] S. Birikundavyi, R. Labib, T. Trung and J. Rousselle, "Performance of
neural networks in daily streamflow forecasting, Journal of
Hydrological Engineering, Vol. 7, No. 5, pp. 392-398, 2002
[4] A. Jain and S. Srinivasulu, "Development of effective and efficient
rainfall-runoff models using integration of deterministic, real-coded
genetic algorithms and artificial neural networks techniques", Water
Resources Research, Vol. 40, doi:10.1029/2003 WR002355, (2004)
Americal Geophysical Union, 2004.
[5] D. A. K. Fernando and A.W. Jayawardena, "Runoff forecasting using
RBF networks with OLS Algorithm", Journal of Hydrologic
Engineering, ASCE Water resources Engineering division, Vol. 3, No.
3, pp. 203- 209, July 1998.
[6] M. Tomasino, D. Zunchettin and P. Traverso, "Long term forecasts of
River Po discharges based on predictable solar activity and a fuzzy
neural network model, Hydrological Sciences Journal, Vol. 49, No. 4,
pp. 673-684, August 2004.
[7] Y. Lin, Y. Wang, Y. Li, B. Zhang and G. Wu, "Earthquake prediction by
RBF neural network ensemble", Advances in Neural Networks, Proc. of
the International Symposium on Neural networks, China, Ed. F. Yin, J.
Wang, and C. Guo, pp. 962-969, Springer, August 2004.
[8] D. E. Rumelhart, R. Durbin, R. Golden, and Chauvin,
"Backpropagation: The Basic Theory, In Mathematical Perspectives on
Neural Networks", Eds. P. Smolensky, M. C.Mozer, and D. E.
Rumelhart, Lawrence Erlbaum Associates, Mahwah, NJ., pp. 533-566,
1996.
[9] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning internal
representations by error propagation, In Parallel Distributed
Processing, Vol. 1, Eds., D. E. Rumelhart, and J. L. McClelland, MIT
Press, Cambridge, MA. pp. 318-362, 1986.
[10] Wasserman, P.D., Advanced methods in neural computing, Van
Nostrand Reinhold, New York, 1993.
[11] M. A. Rao, J. Srinivas, Neural networks: algorithms and applications,
Pangbourne, Eng.; Alpha Science International, 2003.
[12] Fernando, A.K. and Kerr, T., "Runoff forecasting with Artificial Neural
Network Model", The 3rd Pacific Conference on stormwater and aquatic
resource protection, Auckland, New Zealand Water and Wastes
Association publication of conference proceedings in CD-ROM, 2003.
[13] H. K. Cigizoglu "Estimation, forecasting and extrapolation of river
flows by artificial neural networks" Hydrological Science, Journal, Vol.
48, No. 3, pp. 349-361, June 2003.
[14] M. Campolo, A. Soldati, P. Acdreussi, "Artificial Neural Network
approach to flood forecasting in the River Arno" Hydrological Sciences
Journal, 48(3), pp. 381-398, June 2003
[15] H. R. Maier and G.C. Dandy, "Determining inputs for neural network
models of multivariate time series", Microcomputers in Civil
Engineering, Vol. 12, pp. 353-368, 1997.
[16] S. Amari, N. Murata, K. R. Muller, M. Finke and H.H. Yang,
"Asymptotic statistical theory of overtratining and cross-validation"
IEEE Transactions on Neural Networks, Vol. 8, No. 5, pp. 985-996,
1997.
[17] W. Wang, P.H.A.J.M. Van Gelder and J.K. Vrijling, "Some issues about
the generalization of neural networks for time series prediction" in
Advances in Neural Networks, Proc. of the International Symposium on
Neural networks, August 2004, China, Ed. F. Yin, J. Wang, and C. Guo,
Springer, pp. 559-564, 2004
[18] L. Prechelt, 1998, Early stopping - but when? In G. B. Orr, K-R
Mueller, Eds. Neural networks: Tricks of the trade, Berlin, Springer, pp.
55-99, 1998..
@article{"International Journal of Architectural, Civil and Construction Sciences:52578", author = "Achela K. Fernando and Xiujuan Zhang and Peter F. Kinley", title = "Combined Sewer Overflow forecasting with Feed-forward Back-propagation Artificial Neural Network", abstract = "A feed-forward, back-propagation Artificial Neural
Network (ANN) model has been used to forecast the occurrences of
wastewater overflows in a combined sewerage reticulation system.
This approach was tested to evaluate its applicability as a method
alternative to the common practice of developing a complete
conceptual, mathematical hydrological-hydraulic model for the
sewerage system to enable such forecasts. The ANN approach
obviates the need for a-priori understanding and representation of the
underlying hydrological hydraulic phenomena in mathematical terms
but enables learning the characteristics of a sewer overflow from the
historical data.
The performance of the standard feed-forward, back-propagation
of error algorithm was enhanced by a modified data normalizing
technique that enabled the ANN model to extrapolate into the
territory that was unseen by the training data. The algorithm and the
data normalizing method are presented along with the ANN model
output results that indicate a good accuracy in the forecasted sewer
overflow rates. However, it was revealed that the accurate
forecasting of the overflow rates are heavily dependent on the
availability of a real-time flow monitoring at the overflow structure
to provide antecedent flow rate data. The ability of the ANN to
forecast the overflow rates without the antecedent flow rates (as is
the case with traditional conceptual reticulation models) was found to
be quite poor.", keywords = "Artificial Neural Networks, Back-propagationlearning, Combined sewer overflows, Forecasting.", volume = "1", number = "12", pages = "113-7", }
