The increasing access to non-expensive sensors, computing power, and more
accurate forecasting of storm events provides unique opportunities to shift
flood management practices from static approaches to an optimization-based
real-time control (RTC) of urban drainage systems. Recent studies have
addressed a plethora of strategies for flood control in stormwater reservoirs;
however, advanced control theoretic techniques are not yet fully investigated
and applied to these systems. In addition, there is an absence of a coupled
integrated control model for systems composed of watersheds, reservoirs, and
channels for flood mitigation.
To this end, we develop a novel state-space model of hydrologic and
hydrodynamic processes in reservoirs and one-dimensional channels. The model is
tested under different types of reservoir control strategies based on real-time
measurements (reactive control), and based on predictions of the future
behavior of the system (predictive control) using rainfall forecastings. We
apply the modeling approach in a system composed by a single watershed,
reservoir, and a channel connected in series, respectively, for the San Antonio
observed rainfall data. Results indicate that for flood mitigation, the
predictive control strategy outperforms the reactive controls not only when
applied for synthetic design storm events, but also for a continuous
simulation. Moreover, the predictive control strategy requires smaller valve
operations, while still guaranteeing efficient hydrological performance. From
the results, we recommend the use of the model predictive control strategy to
control stormwater systems due to the ability to handle different objective
functions, which can be altered according to rainfall forecasting and shift the
reservoir operation from flood-based control to strategies focused on
increasing detention times, depending on the forecasting