Stormwater Symposium

Oral

397535 - A data-driven approach to model flows across combined sewer systems

Monday, June 4
4:00 PM - 5:30 PM
Location: Mirage Room
Co-Authors: Donald Scavia, Graham Sustainability Institute – University of Michigan; Branko Kerkez, Civil and Environmental Engineering – University of Michigan

We present a data-driven toolchain for the modeling of phosphorus loads across combined sewer systems, which fuses sensor measurements with the outputs of a large physical model. Rather than relying on a traditional recalibration of a physical model (e.g. SWMM), which requires tuning of the model’s structure and parameters, our approach instead learns the causal relationship between model outputs and sensor measurements. This allows new independent measurements to be rapidly ingested and forecasts to be made without requiring the full recalibration of the complex physical model. In this study, we evaluate a data-driven toolchain based on Directed Information (DI) and Boosted Regression Trees (BRT). A case study in a major Midwestern city is used to illustrate the performance of the approach in modeling flows and phosphorous loads across a large combined sewer system. An evaluation of the approach across this large sensor measurements set indicates a strong performance of this data-driven methodology to predict flows and phosphorous loads. We further discuss generalizability to this DI-BRT method, which is well suited for ingesting physical models whose representations of the target systems are decent, but whose parameters are too computationally expensive to calibrate using conventional approaches.

Yao Hu, PhD

Postdoctoral researcher
University of Michigan

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