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\begin{document}

\title{Model predictive control of stormwater basins coupled with real-time
data assimilation enhances flood and pollution control under uncertainty}



\author[1]{Jeil Oh}%
\author[1]{Matthew Bartos}%
\affil[1]{The University of Texas at Austin}%


\vspace{-1em}



  
  \date{June 05, 2024}


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\begin{abstract}
\emph{}Smart stormwater systems equipped with real-time controls are
transforming urban drainage management by enhancing the flood control
and water treatment potential of previously static infrastructure.
Real-time control of detention basins, for instance, has been shown to
improve contaminant removal by increasing hydraulic retention times
while also reducing downstream flood risk. However, to date, few studies
have explored optimal real-time control strategies for achieving both
water quality and flood control targets. This study advances a new
model-predictive control (MPC) algorithm for stormwater detention ponds
that determines the outlet valve control schedule needed to maximize
pollutant removal and minimize flooding using forecasts of the incoming
pollutograph and hydrograph. We illustrate that, compared to rule-based
controls, MPC more effectively prevents overflows, reduces peak
discharges, improves water quality, and adapts to changing hydrologic
inputs. Moreover, when paired with an online data assimilation scheme
based on Extended Kalman Filtering (EKF), we find that MPC is robust to
uncertainty in both pollutograph forecasts and water quality
measurements. By providing an integrated control strategy that optimizes
both water quality and quantity goals while remaining robust to
uncertainty in hydrologic and pollutant dynamics, our study paves the
way for real-world smart stormwater systems that will achieve improved
flood and nonpoint source pollution management.%
\end{abstract}%



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