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Influent Forecasting for Wastewater Treatment Plants in North America

Author

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  • Gavin Boyd

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
    Joint first authors.)

  • Dain Na

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
    Joint first authors.)

  • Zhong Li

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada)

  • Spencer Snowling

    (Hydromantis Environmental Software Solutions, Inc., 407 King Street West, Hamilton, ON L8P 1B5, Canada)

  • Qianqian Zhang

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
    School of Management, Chengdu University of Information Technology, Chengdu 610225, China)

  • Pengxiao Zhou

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada)

Abstract

Autoregressive Integrated Moving Average (ARIMA) is a time series analysis model that can be dated back to 1955. It has been used in many different fields of study to analyze time series and forecast future data points; however, it has not been widely used to forecast daily wastewater influent flow. The objective of this study is to explore the possibility for wastewater treatment plants (WWTPs) to utilize ARIMA for daily influent flow forecasting. To pursue the objective confidently, five stations across North America are used to validate ARIMA’s performance. These stations include Woodward, Niagara, North Davis, and two confidential plants. The results demonstrate that ARIMA models can produce satisfactory daily influent flow forecasts. Considering the results of this study, ARIMA models could provide the operating engineers at both municipal and rural WWTPs with sufficient information to run the stations efficiently and thus, support wastewater management and planning at various levels within a watershed.

Suggested Citation

  • Gavin Boyd & Dain Na & Zhong Li & Spencer Snowling & Qianqian Zhang & Pengxiao Zhou, 2019. "Influent Forecasting for Wastewater Treatment Plants in North America," Sustainability, MDPI, vol. 11(6), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1764-:d:216611
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    References listed on IDEAS

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    1. Armstrong, J. Scott, 1989. "Combining forecasts: The end of the beginning or the beginning of the end?," International Journal of Forecasting, Elsevier, vol. 5(4), pages 585-588.
    2. Georgia Papacharalampous & Hristos Tyralis & Demetris Koutsoyiannis, 2018. "Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: a Multiple-Case Study from Greece," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5207-5239, December.
    3. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    4. Qiang Zhang & Ben-De Wang & Bin He & Yong Peng & Ming-Lei Ren, 2011. "Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2683-2703, September.
    5. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    6. Liu, Lon-Mu & Bhattacharyya, Siddhartha & Sclove, Stanley L. & Chen, Rong & Lattyak, William J., 2001. "Data mining on time series: an illustration using fast-food restaurant franchise data," Computational Statistics & Data Analysis, Elsevier, vol. 37(4), pages 455-476, October.
    7. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    8. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    9. Tjur, Tue, 2009. "Coefficients of Determination in Logistic Regression Models—A New Proposal: The Coefficient of Discrimination," The American Statistician, American Statistical Association, vol. 63(4), pages 366-372.
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