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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.
Gavin Boyd; Dain Na; Zhong Li; Spencer Snowling; Qianqian Zhang; Pengxiao Zhou. Influent Forecasting for Wastewater Treatment Plants in North America. Sustainability 2019, 11, 1764 .
AMA StyleGavin Boyd, Dain Na, Zhong Li, Spencer Snowling, Qianqian Zhang, Pengxiao Zhou. Influent Forecasting for Wastewater Treatment Plants in North America. Sustainability. 2019; 11 (6):1764.
Chicago/Turabian StyleGavin Boyd; Dain Na; Zhong Li; Spencer Snowling; Qianqian Zhang; Pengxiao Zhou. 2019. "Influent Forecasting for Wastewater Treatment Plants in North America." Sustainability 11, no. 6: 1764.