A Forecast-Informed Rule-Based Control Framework for Optimizing the Hydraulic Performance of Bioretention Systems

Abstract

Bioretention systems are widely implemented to mitigate urban runoff; however, conventional passive designs lack the ability to adapt discharge behavior to evolving rainfall conditions, often resulting in premature drainage and inefficient utilization of available storage. This study develops and evaluates a forecast-informed, rule-based control framework for optimizing the long-term hydraulic performance of bioretention systems. The proposed approach integrates continuous valve actuation with a kernel-based rainfall forecast indicator embedded directly within a process-based hydraulic model implemented in OpenHydroQual. Control parameters governing forecast interpretation and valve responsiveness are optimized offline using a genetic algorithm over multiyear simulations. System performance is evaluated using three complementary metrics: exceedance flow at a probability level of p = 0.001 , total discharged volume, and a residence-time objective, defined as the outlet load of a Laplace-transformed age variable. Simulation-based evaluation using a field-calibrated model of a dual bioretention system demonstrated that exceedance-only optimization reduces extreme discharge from 1.99 × 10 2 to 2.86 × 10 1    m 3 / day (approximately 86%) relative to passive operation. Multiobjective optimization revealed a well-defined Pareto trade-off between exceedance suppression and residence-time enhancement. Increasing the residence-time weight yielded a 52% reduction in the transformed-age load while increasing exceedance by less than 20% relative to the exceedance-only solution. Notably, moderate weighting achieved approximately 40% improvement in residence time with less than 3% increase in exceedance, indicating a region of hydraulically efficient operating conditions. Results demonstrate that forecast-informed rule-based control can substantially improve both extreme-flow mitigation and storage persistence without requiring online optimization or computationally intensive model predictive control. The proposed framework provides a transparent, computationally lightweight, and physically interpretable approach for enhancing the operational performance of existing bioretention infrastructure under long-term, variable hydrologic conditions.