Modeling Fecal Indicator Bacteria in Urban Waterways Using Artificial Neural Networks
Publication: Journal of Environmental Engineering
Volume 144, Issue 6
Abstract
Fecal indicator bacteria (FIB) are used as proxies to measure the microbial water quality of aquatic ecosystems. Methods of modeling FIB have evolved in order to provide accurate and timely prediction to inform decisions by governing authorities to prevent risks to public health. A predictive model to forecast the FIB concentrations of an urban waterway, the Chicago River, utilizing the artificial neural network (ANN) method was developed. To address tuning of hyperparameters of the ANN model, an exhaustive testing was performed to select optimal hyperparameters. The root-mean-square propagation (RMSprop) optimizer performed better than the stochastic gradient descent (SGD) and adaptive moment estimation (Adam) optimizers in this study. Eight input variables were eventually selected from 10 initially proposed variables: water temperature; turbidity; daily, 2-day, and 7-day cumulative rainfall; river flow discharge; distance from the upstream water reclamation plant; and number of upstream combined sewer outfalls. Water reclamation plants and combined sewer overflows were found to be critical contributors of microbial pollution in this urban waterway and should be considered in the ANN model. The developed model has an accuracy of 86.5% to predict whether fecal coliform concentration is above or below a regulatory threshold.
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Acknowledgments
Paul Entwistle was supported through the NSF-funded REU program under Grant No. EEC-1559989. The authors would like to thank Dr. Lauren Sassoubre for her helpful discussion and suggestion. Reviewers are also acknowledged for improving the quality of the manuscript.
References
Alp, E., and Melching, C. S. (2009). “Evaluation of the duration of storm effects on in-stream water quality.” J. Water Resour. Plann. Manage., 107–116.
Atakulreka, A., and Sutivong, D. (2007). “Avoiding local minima in feedforward neural networks by simultaneous learning.” Australasian Joint Conf. on Artificial Intelligence, Springer, New York, 100–109.
Bardenet, R., Brendel, M., Kégl, B., and Sebag, M. (2013). “Collaborative hyperparameter tuning.” Proc., 30th Int. Conf. on Machine Learning, Vol. 28, Atlanta, 199–207.
Bengio, Y. (2012). “Practical recommendations for gradient-based training of deep architectures.” Neural networks: Tricks of the trade, Springer, New York, 437–478.
Bowie, G. L., et al. (1985). “Rates, constants, and kinetics formulations in surface water quality modeling.” EPA/600/3-85/040, U.S. Environmental Protection Agency, Washington, DC.
Brion, G. M., and Lingireddy, S. (2003). “Artificial neural network modelling: A summary of successful applications relative to microbial water quality.” Water Sci. Technol., 47(3), 235–240.
Byappanahalli, M. N., Whitman, R. L., Shively, D. A., Przybyla-Kelly, K., and Lukasik, A. M. (2010). “Distribution of Escherichia coli and Enterococci in water, sediments, and bank soils along North Shore channel between Bridge Street and Wilson Avenue, Metropolitan Water Reclamation District of Greater Chicago.” U.S. Geological Survey Great Lakes Science Center, Porter, IN.
Chandramouli, V., Brion, G., Neelakantan, T., and Lingireddy, S. (2007). “Backfilling missing microbial concentrations in a riverine database using artificial neural networks.” Water Res., 41(1), 217–227.
Chen, X. Y., and Chau, K. W. (2016). “A hybrid double feedforward neural network for suspended sediment load estimation.” Water Resour. Manage., 30(7), 2179–2194.
Chollet, F. (2015). “Keras: GitHub repository.” ⟨https://github.com/keras-team/keras⟩.
Clevert, D. A., Unterthiner, T., and Hochreiter, S. (2015). “Fast and accurate deep network learning by exponential linear units (ELUs).” ArXiv e-prints arXiv: 1511.07289.
de Brauwere, A., Ouattara, N. K., and Servais, P. (2014). “Modeling fecal indicator bacteria concentrations in natural surface waters: A review.” Crit. Rev. Environ. Sci. Technol., 44(21), 2380–2453.
Dorevitch, S., et al. (2011). “A comparison of rapid and conventional measures of indicator bacteria as predictors of waterborne protozoan pathogen presence and density.” J. Environ. Monit., 13(9), 2427.
Eleria, A., and Vogel, R. M. (2005). “Predicting fecal coliform bacteria levels in the Charles River, Massachusetts, USA.” J. Am. Water Resour. Assoc., 41(5), 1195–1209.
Ferguson, C. M., Charles, K., and Deere, D. A. (2009). “Quantification of microbial sources in drinking-water catchments.” Crit. Rev. Environ. Sci. Technol., 39(1), 1–40.
Glorot, X., and Bengio, Y. (2010). “Understanding the difficulty of training deep feedforward neural networks.” Proc., 13th Int. Conf. on Artificial Intelligence and Statistics, Sardinia, Italy, 249–256.
Gurney, K. (1997). An introduction to neural networks, CRC Press, Boca Raton, FL.
Haykin, S. (1994). Neural networks: A comprehensive foundation, Prentice Hall PTR, Upper Saddle River, NJ.
He, L.-M. L., and He, Z.-L. (2008). “Water quality prediction of marine recreational beaches receiving watershed baseflow and stormwater runoff in southern California, USA.” Water Res., 42(10–11), 2563–2573.
Hornik, K., Stinchcombe, M., and White, H. (1989). “Multilayer feedforward networks are universal approximators.” Neural Networks, 2(5), 359–366.
Illinois State Water Survey. (2018). “Cook county precipitation network daily data.” Champaign, IL.
IPCB (Illinois Pollution Control Board). (2013). “Water use designations and site-specific water quality standards.”, Springfield, IL.
Ishii, S., Ksoll, W. B., Hicks, R. E., and Sadowsky, M. J. (2006). “Presence and growth of naturalized Escherichia coli in temperate soils from Lake Superior watersheds.” Appl. Environ. Microbiol., 72(1), 612–621.
Jagupilla, S. C. K., Vaccari, D. A., and Hires, R. I. (2010). “Multivariate polynomial time-series models and importance ratios to qualify fecal coliform sources.” J. Environ. Eng., 657–665.
Jones, R. M., Liu, L., and Dorevitch, S. (2013). “Hydrometeorological variables predict fecal indicator bacteria densities in freshwater: Data-driven methods for variable selection.” Environ. Monit. Assess., 185(3), 2355–2366.
Karlik, B., and Olgac, A. V. (2011). “Performance analysis of various activation functions in generalized MLP architectures of neural networks.” Int. J. Artif. Intell. Expert Syst., 1(4), 111–122.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems, MIT Press, Cambridge, MA, 1097–1105.
Lanyon, R. (2012). Building the canal to save Chicago, Xlibris Corp., Chicago.
Larsen, T. A., Hoffmann, S., Luthi, C., Truffer, B., and Maurer, M. (2016). “Emerging solutions to the water challenges of an urbanizing world.” Science, 352(6288), 928–933.
LeCun, Y., et al. (1989). “Backpropagation applied to handwritten zip code recognition.” Neural Comput., 1(4), 541–551.
LeCun, Y. A., Bottou, L., Orr, G. B., and Müller, K.-R. (2012). “Efficient backprop.” Neural networks: Tricks of the trade, Springer, New York, 9–48.
Manache, G., Melching, C. S., and Lanyon, R. (2007). “Calibration of a continuous simulation fecal coliform model based on historical data analysis.” J. Environ. Eng., 681–691.
Mas, D. M. L., and Ahlfeld, D. P. (2007). “Comparing artificial neural networks and regression models for predicting faecal coliform concentrations.” Hydrol. Sci. J., 52(4), 713–731.
McCulloch, W. S., and Pitts, W. (1943). “A logical calculus of the ideas immanent in nervous activity.” Bull. Math. Biophys., 5(4), 115–133.
Melching, C. S., Liang, J., Fleer, L., and Wethington, D. (2015). “Modeling the water quality impacts of the separation of the Great Lakes and Mississippi River basins for invasive species control.” J. Great Lakes Res., 41(1), 87–98.
MWRDGC (Metropolitan Water Reclamation District of Greater Chicago). (2016). “2015 annual summary report water quality within the waterway system of the Metropolitan Water Reclamation District of Greater Chicago.”, Chicago.
MWRDGC (Metropolitan Water Reclamation District of Greater Chicago). (2018). “Chicago area waterways monitoring (AWQM).” Chicago.
Nabavi-Pelesaraei, A., Bayat, R., Hosseinzadeh-Bandbafha, H., Afrasyabi, H., and Chau, K.-W. (2017). “Modeling of energy consumption and environmental life cycle assessment for incineration and landfill systems of municipal solid waste management: A case study in Tehran metropolis of Iran.” J. Cleaner Prod., 148(Apr), 427–440.
Nair, V., and Hinton, G. E. (2010). “Rectified linear units improve restricted Boltzmann machines.” Proc., 27th Int. Conf. on Machine Learning (ICML-10), International Machine Learning Society, Princeton, NJ, 807–814.
Nieh, C., Dorevitch, S., Liu, L. C., and Jones, R. M. (2014). “Evaluation of imputation methods for microbial surface water quality studies.” Environ. Sci. Processes Impacts, 16(5), 1145–1153.
Olyaie, E., Banejad, H., Chau, K.-W., and Melesse, A. M. (2015). “A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: A case study in united states.” Environ. Monit. Assess., 187(4), 189.
Ortega, C., Solo-Gabriele, H. M., Abdelzaher, A., Wright, M., Deng, Y., and Stark, L. M. (2009). “Correlations between microbial indicators, pathogens, and environmental factors in a subtropical estuary.” Mar. Pollut. Bull., 58(9), 1374–1381.
Paliwal, M., and Kumar, U. A. (2009). “Neural networks and statistical techniques: A review of applications.” Expert Syst. Appl., 36(1), 2–17.
Quijano, J. C., Zhu, Z., Morales, V., Landry, B. J., and Garcia, M. H. (2017). “Three-dimensional model to capture the fate and transport of combined sewer overflow discharges: A case study in the Chicago area waterway system.” Sci. Total Environ., 576(Jan), 362–373.
Rijal, G., et al. (2009). “Dry and wet weather microbial characterization of the Chicago area waterway system.” Water Sci. Technol., 60(7), 1847.
Rijal, G., et al. (2011). “Microbial risk assessment for recreational use of the Chicago area waterway system.” J. Water Health, 9(1), 169.
Ruder, S. (2016). “An overview of gradient descent optimization algorithms.” ArXiv e-prints arXiv: 1609.04747.
Sinha, S., Liu, X., and Garcia, M. H. (2012). “Three-dimensional hydrodynamic modeling of the Chicago River, Illinois.” Environ. Fluid Mech., 12(5), 471–494.
Sinha, S., Liu, X., and Garcia, M. H. (2013). “A three-dimensional water quality model of Chicago area waterway system (CAWS).” Environ. Model. Assess., 18(5), 567–592.
Templar, A., Schofield, D. M., and Nesbeth, D. N. (2017). “Measuring E. coli and bacteriophage DNA in cell sonicates to evaluate the CAL1 reaction as a synthetic biology standard for qPCR.” Biomol. Detect. Quantif., 11(Mar), 21–30.
Thirumalaiah, K., and Deo, M. (1998). “River stage forecasting using artificial neural networks.” J. Hydrol. Eng., 26–32.
Thoe, W., Gold, M., Griesbach, A., Grimmer, M., Taggart, M., and Boehm, A. (2014). “Predicting water quality at Santa Monica Beach: Evaluation of five different models for public notification of unsafe swimming conditions.” Water Res., 67(Dec), 105–117.
Thoe, W., Wong, S., Choi, K., and Lee, J. (2012). “Daily prediction of marine beach water quality in Hong Kong.” J. Hydro-environ. Res., 6(3), 164–180.
Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2012). “Auto-WEKA: Automated selection and hyper-parameter optimization of classification algorithms.” ArXiv e-prints arXiv: 1208.3719v1.
Thupaki, P., Phanikumar, M. S., Beletsky, D., Schwab, D. J., Nevers, M. B., and Whitman, R. L. (2010). “Budget analysis of Escherichia coli at a southern Lake Michigan beach.” Environ. Sci. Technol., 44(3), 1010–1016.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall-runoff modeling using artificial neural networks.” J. Hydrol. Eng., 232–239.
Tornevi, A., Bergstedt, O., and Forsberg, B. (2014). “Precipitation effects on microbial pollution in a river: Lag structures and seasonal effect modification.” PLoS One, 9(5), e98546.
Tufail, M., Ormsbee, L., and Teegavarapu, R. (2008). “Artificial intelligence-based inductive models for prediction and classification of fecal coliform in surface waters.” J. Environ. Eng., 789–799.
United Nations. (2014). “World urbanization prospects: The 2014 revision, highlights. United Nations Department of Economic and Social Affairs.” Population Division, United Nations, New York.
USACE (U.S. Army Corps of Engineers). (2014). “The GLMRIS report: Great Lakes and Mississippi River interbasin study.” Washington, DC.
USEPA (U.S. Environmental Protection Agency). (1986). “Ambient water quality criteria for bacteria—1986.”, Washington, DC.
USEPA (U.S. Environmental Protection Agency). (2012). “Recreational water quality criteria.”, Washington, DC.
USGS (U.S. Geological Survey). (2018). “National water information system.” Reston, VA.
Waterman, D. M., Waratuke, A. R., Motta, D., Cataño-Lopera, Y. A., Zhang, H., and García, M. H. (2011). “In situ characterization of resuspended-sediment oxygen demand in Bubbly Creek, Chicago, Illinois.” J. Environ. Eng., 717–730.
Westcott, N. E. (2015). “Continued operation of a 25-raingage network for collection, reduction and analysis of precipitation data for Lake Michigan diversion accounting, water year 2014.”, Illinois State Water Survey, Champaign, IL.
Williams, D., and Hinton, G. (1986). “Learning representations by back-propagating errors.” Nature, 323(6088), 533–536.
Wu, C. L., Chau, K. W., and Fan, C. (2010). “Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques.” J. Hydrol., 389(1), 146–167.
Wu, W., Dandy, G. C., and Maier, H. R. (2014). “Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling.” Environ. Modell. Software, 54(Apr), 108–127.
Zhu, Z., Morales, V., and Garcia, M. H. (2017a). “Impact of combined sewer overflow on urban river hydrodynamic modelling: A case study of the Chicago waterway.” Urban Water J., 14(9), 984–989.
Zhu, Z., Motta, D., Jackson, P. R., and Garcia, M. H. (2017b). “Numerical modeling of simultaneous tracer release and piscicide treatment for invasive species control in the Chicago Sanitary and Ship Canal, Chicago, Illinois.” Environ. Fluid Mech., 17(2), 211–229.
Zhu, Z., Oberg, N., Morales, V. M., Quijano, J. C., Landry, B. J., and Garcia, M. H. (2016). “Integrated urban hydrologic and hydraulic modelling in Chicago, Illinois.” Environ. Modell. Software, 77(Mar), 63–70.
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Published In
Journal of Environmental Engineering
Volume 144 • Issue 6 • June 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Aug 16, 2017
Accepted: Nov 29, 2017
Published online: Apr 6, 2018
Published in print: Jun 1, 2018
Discussion open until: Sep 6, 2018
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