Effective plant operation begins with understanding what is coming through the front gate. Water resource recovery facility (WRRF) operators have traditionally relied on demand patterns and local weather to estimate influent flows. This method limits users to short forecasting horizons, typically a few hours to one day. However, machine learning can significantly boost forecast accuracy and reliability, while extending predictions to a full week.
More precise predictions enable operators to make better decisions related to process control, chemical usage, staffing, maintenance planning, and wet weather management. To help operators to make the most of this opportunity, our team created a scalable, data-efficient forecasting tool that empowers utilities to reduce costs, mitigate operational risks, and strengthen regulatory compliance.
One key challenge we faced in this process was the sheer diversity of WRRF datasets. No two facilities share the same flow ranges, weather patterns, or data history, making one-size-fits-all modeling impractical. We developed adaptive preprocessing tailored to each dataset as well as a flexible ensemble learning framework that blends tree-based models with neural networks. This approach captures nonlinear and temporal patterns more effectively than individual models.
We tested the solution across 30 WRRFs, ranging from 0.5 to 150 million gallons per day (1 to 550 megalitres). The tool used two years of training data to deliver reliable seven-day forecasts with an average mean absolute percentage error (MAPE) below 11 percent, even during extreme weather. Continued testing across more facilities will allow us to strengthen scalability and prepare the model for deployment.
By knowing what to expect a week ahead, utilities and operators can better prepare for future conditions and continue to provide reliable service to their communities.
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