Influent Forecasting for Wastewater Treatment Plants in North America

Gavin Boyd, Dain Na, Zhong Li, Spencer Snowling, Qianqian Zhang and Pengxiao Zhou
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Gavin Boyd: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
Dain Na: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
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
Pengxiao Zhou: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada

Sustainability, 2019, vol. 11, issue 6, 1-14

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.

Keywords: ARIMA; time series analysis; wastewater treatment; inflow forecasting; North America (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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