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A random forest model for inflow prediction at wastewater treatment plants

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Abstract

Influent flow of wastewater treatment plants (WWTPs) is a crucial variable for plant operation and management. In this study, a random forest (RF) model was applied for daily wastewater inflow prediction, and a new probabilistic prediction approach was, for the first time, applied for quantifying the uncertainties associated with wastewater inflow prediction. The RF model uses regression trees to capture the nonlinear relationship between wastewater inflow and various influencing factors, such as weather features and domestic water usage patterns. The proposed model was applied to the daily wastewater inflow prediction for two WWTPs (i.e., Humber and one confidential plant) in Ontario, Canada. For the confidential WWTP, the coefficient of determination (\(\varvec{R}^{2}\)) values for training and testing were 0.971 and 0.722, respectively. The \(\varvec{R}^{2}\) values at the Humber WWTP were 0.957 and 0.584 for training and testing, respectively. In comparison with other approaches such as the multilayer perceptron neural networks (MLP) models and autoregressive integrated moving average models, the results show that the RF model performs well on predicting inflow. In addition, probabilistic prediction of daily inflow was generated. For the Humber station, 93.56% of the total testing samples fall into its corresponding predicted interval. For the confidential plant, 78 observed values of the total 89 samples fall into its corresponding interval, accounting for 87.64% of the total testing samples. The results show that the probabilistic approach can provide robust decision support for the operation, management, and optimization of WWTPs.

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Acknowledgements

This study was supported by the Southern Ontario Water Consortium and the Natural Science and Engineering Research Council of Canada. The authors would like to thank the contributions of engineers at Hydromantis and the WWTPs for their suggestions and comments.

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Authors and Affiliations

  1. Department of Civil Engineering, McMaster University, Hamilton, ON, L8S 4L7, Canada

    Pengxiao Zhou, Zhong Li, Brian W. Baetz, Dain Na & Gavin Boyd

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

    Spencer Snowling

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  1. Pengxiao Zhou
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  2. Zhong Li
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  3. Spencer Snowling
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  4. Brian W. Baetz
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Correspondence to Zhong Li.

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Zhou, P., Li, Z., Snowling, S. et al. A random forest model for inflow prediction at wastewater treatment plants. Stoch Environ Res Risk Assess 33, 1781–1792 (2019). https://doi.org/10.1007/s00477-019-01732-9

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