Artificial Neural Network Simulation of Combined Permeable Pavement and Earth Energy Systems Treating Storm Water
Publication: Journal of Environmental Engineering
Volume 138, Issue 4
Abstract
Artificial intelligence techniques, such as neural networks, are modeling tools that can be applied to analyze urban runoff quality issues. Artificial neural networks are frequently used to model various highly variable and nonlinear physical phenomena in the water and environmental engineering fields. The application of neural networks for analyzing the performance of combined permeable pavement and earth energy systems is timely and novel. This paper presents the application of back-propagation neural networks and the testing of the Levenberg-Marquardt, Quasi-Newton, and Bayesian Regularization algorithms. The neural networks were statistically assessed for their goodness of prediction with respect to the biochemical oxygen demand (BOD), ammonia-nitrogen, nitrate-nitrogen, and ortho-phosphate-phosphorus by numerical computation of the mean absolute error, root-mean-square error, mean absolute relative error, and the coefficient of correlation for the prediction compared with the corresponding measured data. The three neural network models were assessed for their efficiency in accurately simulating the effluent water quality parameters from various experimental pavement systems. The models predicted all key parameters with high correlation coefficients and low minimum statistical errors. The back-propagation and feed-forward neural network models performed optimally as pollutant removal predictors with regard to these two sustainable technologies.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors wish to thank Hanson Formpave, part of the Heidelberg Cement Group, for providing financial support for this research. Support provided by Stephen Coupe (Coventry University) and Piotr Grabowicki (Environment Agency) is acknowledged.
References
Abbott, C. L., and Comino-Mateos, L. (2003). “In-situ hydraulic performance of a permeable pavement sustainable urban drainage system.” Water Environ. J.WEJAAB, 17(3), 187–190.
Affandi, A., and Wantanabe, K. (2007). “Daily groundwater level fluctuation forecasting using soft computing technique.” Nat. Sci., 5(2), 1–10.NASOA5
American Public Health Association (APHA). (2005). Standard methods for the examination of water and wastewater, 21st Ed., American Public Health Association (APHA), American Water Works Association and Water and Environmental Federation, Washington, DC.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). “Artificial neural networks in hydrology I: Preliminary concepts.” J. Hydrol. Eng.JHYEFF, 5(2), 115–123.
Battiti, R. (1992). “First and second-order method for learning between steepest descent and Newton’s method.” Neural Comput.NEUCEB, 4(2), 141–166.
Brion, G.M., and Lingireddy, S. (1999). “A neural network approach to identifying non-point sources of microbial contamination.” Water Res.WATRAG, 33(14), 3099–3106.
Chaves, P., and Kojiri, T. (2007). “Conceptual fuzzy neural network model for water quality simulation.” Hydrol. ProcessesHYPRE3, 21(5), 634–646.
Coupe, S. J., Smith, H. G., Newman, A. P., and Puehmeier, T. (2003). “Biodegradation and microbial diversity within permeable pavements.” Eur. J. ProtistologyEJPREZ, 39(4), 495–498.
Demuth, H., and Beale, M. (2001). Neural network toolbox learning for use with MATLAB, The Math Work, Natick, MA.
Dutot, A., Rynkiewicz, J., Steiner, F. E., and Rude, J. (2007). “A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions.” Environ. Modell. SoftwareEMSOFT, 22(9), 1261–1269.
Esen, H., Inalli, M., Sengur, A., and Esen, M. (2008). “Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing.” Int. J. Therm. Sci.IJTSFZ, 47(4), 431–441.
European Commission Environment. (2010). Urban Waste Water Treatment Directive, available online: 〈http://ec.europa.eu/environment/water/water-urbanwaste/directiv.html〉, accessed on February 5th, 2010.
Haykin, S. S. (1999). Neural networks: A comprehensive foundation, Prentice Hall, Upper Saddle River, NJ.
Hsu, K. L., Gupta, H. V., and Sorroshian, S. (1995). “Aritificial neural network modeling of the rainfall-runoff process.” Water Resour. Res.WRERAQ, 31(10), 2517–2530.
Isa Mat, N. A., Hashim, F. R., Mei, F. W., Ramli, D. A., Omar Wan, W. M., and Zamli, K. Z. (2006). “Predicting quality of river's water based on algae composition using artificial neural network.” Proc., 4th Institute of Electrical and Electronics Engineers Int. Conf. on Industrial Informatics, Institute of Electrical and Electronics Engineers, New York, 1340–1345.
Lek, S., and Guégan, J. F. (1999). “Artificial neural networks as a tool in ecological modellling, an introduction.” Ecol. Model.ECMODT, 120(2–3), 65–73.
Lingireddy, S., and Brion, G. M. (2005). Artificial neural networks in water supply engineering, ASCE, Reston, VA.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications.” Environ. Modell. SoftwareEMSOFT, 15(1), 101–124.
Maier, H. R., and Dandy, G. C. (1996). “The use of artificial neural networks for the prediction of water quality parameters.” Water Resour. Res.WRERAQ, 32(4), 1013–1022.
Neelakantan, T. R., Brion, G. M., and Lingireddy, S. (2002). “Effectiveness of different artificial neural network training algorithms in predicting protozoa risks in surface waters.” J. Environ. Eng.JOEEDU, 128(6), 533–542.
Omer, A. M. (2008). “Ground-source heat pumps systems and applications.” Renew. Sustain. Energ. Rev.RSERFH, 12(2), 344–371.
Phetteplace, G. (2007). “Geothermal heat pumps.” J. Energy Eng.JLEED9, 133(1), 32–38.
Pigram, G. M., and MacDonald, T. R. (2001). “Use of neural network models to predict industrial bioreactor effluent.” Environ. Sci. Technol.ESTHAG, 35(1), 157–162.
Pratt, C. J. (1999). “Use of permeable reservoir pavement constructions for stormwater treatment and storage for re-use.” Water Sci. Technol.WSTED4, 39(5), 145–151.
Sanner, B., Karytsas, C., Mendrinos, D., and Rybach, L. (2003). “Current status of ground source heat pumps and underground thermal energy storage.” GeothermicsGTMCAT, 32(4–6), 579–588.
Schlink, U., (2003). “A rigorous inter-comparison of ground-level ozone predictions.” Atmos. Environ.AENVEQ, 37(23), 3237–3253.
Scholz, M., and Grabowiecki, P. (2007). “Review of permeable pavement systems.” Build. Environ., 42(11), 3830–3836.
Scholz, M., and Grabowiecki, P. (2009). “Combined permeable pavement and ground source heat pump systems to treat urban runoff.” J. Chem. Technol. Biotechnol.JCTBDC, 84(3), 405–413.
Strugholtz, S., Panglisch, S., Gebhardt, J., and Gimbel, R. (2008). “Neural networks and genetic algorithms in membrane technology modeling.” J. Wat. Supply Res. Technol. -AQUAAQUAAA, 57(1), 23–34.
Tota-Maharaj, K., Grabowiecki, P., and Scholz, M. (2009). “Energy and temperature performance analysis of geothermal (ground source) heat pumps integrated with permeable pavement systems for urban run-off reuse.” Int. J. Sustainab. Eng., 2(3), 201–213.
Tota-Maharaj, K., and Scholz, M. (2010). “Efficiency of permeable pavement systems for the removal of urban runoff pollutants under varying environmental conditions.” Environ. Progr. Sustainab. Energy,.
U.S. Environmental Protection Agency. (1999). “US Environmental Protection Agency guideline for developing an ozone forecasting program,” Office of Air Quality Planning and Standards, Research Triangle Park, Durham, NC.
Wang, D., and Lu, W. Z. (2006). “Forecasting of ozone level in the time series using MLP model with a novel hybrid training algorithm.” Atmos. Environ.AENVEQ, 40(5), 913–924.
Yabunaka, K., Hosomi, M., and Murakami, A. (1997). “Novel application of a back-propagation neural network model formulated to predict algal bloom.” Water Sci. Technol.WSTED4, 36(5), 89–97.
Yang, Z. P., Lu, W. X., Long, Y. Q., and Li, P. (2009). “Application and comparison of two prediction models for groundwater levels: A case study in Western Jilin Province, China.” J. Arid. Environ., 73(4–5), 487–497.
Yeon, I. S., Jun, K. W., and Lee, H. J. (2009). “The improvement of total organic carbon forecasting using neural network discharge model.” Environmental technologyENVTEV, 30(1), 45–55.
Yu, R. F., Kang, S. F., Liaw, S. L., and Chen, M. C. (2000). “Application of artificial neural network to control the coagulant dosing in water treatment plant.” Water Sci. Technol.WSTED4, 42(3–4), 403–408.
Zhang, Q., and Stanley, S. J. (1997). “Forecasting raw-water quality parameters for the north saskatchewan river by neural network modeling.” Water Res.WATRAG, 31(9), 2340–2350.
Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 138 • Issue 4 • April 2012
Pages: 499 - 509
Copyright
© 2012. American Society of Civil Engineers.
History
Received: May 31, 2010
Accepted: Sep 19, 2011
Published online: Sep 21, 2011
Published in print: Apr 1, 2012
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.