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Daily prediction of total coliform concentrations using artificial neural networks

  • Environmental Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Current water quality monitoring systems depend on the analysis of in situ grab samples. This is both cost- and labor-intensive, which makes it difficult to conduct daily monitoring tests. One possible way of overcoming this problem is to use a modeling approach. This paper describes the use of an artificial neural network, Self-organizing Linear Output (SOLO), as a modeling approach to predict total coliform concentrations from rainfall and streamflow data. Six different input scenarios are tested to check the efficiency of the SOLO approach, and the results show that the prediction of total coliform concentrations is possible if rainfall events occur. However, poor estimation results are obtained when there is no rain. The model performance improves slightly during periods of no rain if streamflow data are incorporated into the input. However, the model requires more input variables for no-rain periods, because the streamflow data do not enable observed variations to be fully predicted.

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References

  • Aguilera, P. A., Frenich, A. G., Torres, J. A., Castro H., Martinez-Vidal, J. L., and Canton, M. (2001). “Application of the Kohonen Neural Network in coastal water management: Methodological development for the assessment and prediction of water quality.” Wat. Res., Vol. 35, No. 17, pp. 4053–4062, DOI: 10.1016/S0043-1354(01)00151-8.

    Article  Google Scholar 

  • Ambrose, R. B., Wool, T. A., and Barnwell, T. O. (2009). “Development of Water Quality Modeling in the United States.” Environ. Eng. Res. Vol. 14, No. 4, pp. 200–210, DOI: 10.4491/eer.2009.14.4.200.

    Article  Google Scholar 

  • Auer, M. T. and Niehaus, S. L. (1993). “Modeling fecal colifrom bacteria-I. Field and Laboratory determination of loss kinetics.” Wat. Res., Vol. 27, No. 4, pp. 693–701, DOI: 10.1016/0043-1354(93)90179-L.

    Article  Google Scholar 

  • Ban, Y. U., Woo, H. M., Min, H. K., and Baek, J. I. (2013). “Building an Integrated Governance Model and Finding Management Measures for Nonpoint Source Pollution in Watershed Management of Korea.” Environ. Eng. Res., Vol. 18, No. 3, pp. 199–208, DOI: 10.4491/eer.2013.18.3.199.

    Article  Google Scholar 

  • Boehm, A. B. (2003). “Model of microbial transport and inactivation in the surf zone and application to field measurements of total coliform in northern orange county, California.” Environ. Sci. Technol., Vol. 37, No. 24, pp. 5511–5517, DOI: 10.1021/es034321x.

    Article  Google Scholar 

  • Brion, G. M., Neelakantan, T. R., and Lingireddy, S. (2002). “A neuralnetwork-based classification scheme for sorting sources and ages of fecal contamination in water.” Wat. Res., Vol. 36, No. 15, pp. 3765–3774, DOI: 10.1016/S0043-1354(02)00091-X.

    Article  Google Scholar 

  • California Regional Water Quality Control Board, Santa Ana Region (1999). “Amendment to the basin Plan, Santa Ana Region, Chapter 5 -Implementation Plan. Discussion of Newport Bay Watershed.”

  • Canale, R. P., Auer, M. T., Owens, E. M., Heidtke, T. M., and Effler, S. W. (1993). “Modeling fecal coliform bacteria II; Model development and application.” Wat. Res., Vol. 27, No. 4, pp. 703–714, DOI: 10.1016/0043-1354(93)90180-P.

    Article  Google Scholar 

  • Cheroutre-Vialette, M. and Lebert, A. (2002). “Application of recurrent neural network to predict bacterial growth in dynamic conditions.” International Journal of Food Microbiology, Vol. 73, Nos. 2-3, pp. 107–118, DOI: 10.1016/S0168-1605(01)00642-0.

    Article  Google Scholar 

  • Ferrara, R. A. and Withkowski, P. (1983). “Stromwater quality characteristics in detention basins.” J. Environ. Eng., Vol. 109, No. 2, pp. 428–447, DOI: 10.1061/(ASCE)0733-9372(1983)109:2(428).

    Article  Google Scholar 

  • Ha, H. and Stenstrom, M. K. (2003). “Identification of land use with water quality data in stormwater using a neural network.” Wat. Res., Vol. 37, No. 17, pp. 4222–4230, DOI: 10.1016/S0043-1354(03)00344-0.

    Article  Google Scholar 

  • Hsu K.-L., Gupta H. V., Gao X., Sorooshian S., and Imam B. (2002). “Self-organizing Linear Output Map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis.” Wat. Res. Resear., Vol. 38, No. 12, pp. 1302–1319, DOI: 10.1029/2001WR000795.

    Google Scholar 

  • Kashefipour, S. M., Lin, B., and Falconer, R. A. (2005). “Neural networks for predicting seawater bacterial levels.” Water Management, Vol. 158, No. WM3, pp. 111–118.

    Google Scholar 

  • Kim, J. H. (2003). Fecal pollution in coastal waters: Sources, transport, and public notification, PhD Dissertation, University of California, Irvine, CA,USA.

  • Lee, J. H. W., Huang, Y., Dickman, M., and Jayawardena, A.W. (2003). “Neural network modelling of coastal algal blooms.” Ecological Modelling, Vol. 159, Nos. 2-3, pp. 179–201, DOI: 10.1016/S0304-3800(02)00281-8.

    Article  Google Scholar 

  • Lou, W. and Nakai, S. (2001). “Application of artificial neural networks for predicting the thermal inactivation of bacteria: a combined effect of temperature, pH and water activity.” Food Research International, Vol. 34, No. 7, pp. 573–579, DOI: 10.1016/S0963-9969(01)00074-6.

    Article  Google Scholar 

  • Noble, R. T., Moore, D. F., Leecaster, M. K., McGee, C. D., and Weisberg, S. B. (2003). “Comparison of total coliform, fecal coliform, and enterococcus bacterial indicator response for ocean recreational water quality testing.” Wat. Res., Vol. 37, No. 7, pp. 1637–1643, DOI: 10.1016/S0043-1354(02)00496-7.

    Article  Google Scholar 

  • Orange County (2004). “Interim water quality improvement package Plant BMP Agreement 01-227-55.” Aliso Beach Annual Summary, 1–3.

    Google Scholar 

  • Reeves, R. L., Grant, S. B., Mrse, R. D., Copil-Oancea, C. M., Sanders, B. F., and Boehm, A. B. (2004). “Scaling and management of fecal indicator bacteria in runoff from a coastal urban watershed in southern California.” Environ. Sci. Technol., Vol. 38, No. 9, pp. 2637–2648, DOI: 10.1021/es034797g.

    Article  Google Scholar 

  • Sperling, M. V. (1999). “Performance evaluation and mathematical modeling of colifrom die-off in tropical and subtropical waste stabilization ponds.” Wat. Res., Vol. 33, No. 6, pp. 1435–1448, DOI: 10.1016/S0043-1354(98)00331-5.

    Article  Google Scholar 

  • Steets, B. M. and Holden, P. A. (2003). “A mechanistic model of runoffassociated fecal coliform fate and transport through a coastal logoon.” Wat. Res., Vol. 37, No. 3, pp. 589–608, DOI: 10.1016/S0043-1354(02)00312-3.

    Article  Google Scholar 

  • Thoe, W. and Lee J. H. W. (2014). “Daily forecasting of hong kong beach water quality by multiple linear regression models.” J. Environ. Eng., Vol. 140, No. 2, pp. 04013007, DOI:10.1061/(ASCE)EE.1943-7870.0000800.

    Article  Google Scholar 

  • Wengao, L. and Shuyro, N. (2001). “Artificial neural network-based predictive model for bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products.” J. of Agricultural and Food Chemistry, Vol. 49, No. 4, pp. 1799–1804, DOI: 10.1021/jf000650m.

    Article  Google Scholar 

  • Whitlock, J. E., Jones, D. T., and Harwood, V. J. (2002). “Identification of sources of fecal coliforms in an urban watershed using antibiotic resistance analysis.” Wat. Res., Vol. 36, No. 17, pp. 4273–4282, DOI: 10.1016/S0043-1354(02)00139-2.

    Article  Google Scholar 

  • Wilkinson, J., Jenkins, A., Wyer, M., and Kay, D. (1995). “Modeling fecal coliform dynamics in streams and rivers.” Wat. Res., Vol. 29, No. 3, pp. 847–855, DOI: 10.1016/0043-1354(94)00211-O.

    Article  Google Scholar 

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Authors and Affiliations

  1. Dept. of Environmental Science, School of Environment, Keimyung University, Daegu, 42601, Korea

    Sung-Woo Choi

  2. Dept. of Global Environment, School of Environment, Keimyung University, Daegu, 42601, Korea

    Hun-Kyun Bae

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  1. Sung-Woo Choi
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  2. Hun-Kyun Bae
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Correspondence to Hun-Kyun Bae.

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Choi, SW., Bae, HK. Daily prediction of total coliform concentrations using artificial neural networks. KSCE J Civ Eng 22, 467–474 (2018). https://doi.org/10.1007/s12205-017-0739-y

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  • DOI: https://doi.org/10.1007/s12205-017-0739-y

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