Dealing with rainfall forecast uncertainties in real-time flood control along the Demer river
Hydraulics Division, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, 3001 Herverlee, Belgium
a Corresponding author: Evert.Vermuyten@kuleuven.be
Real-time Model Predictive Control (MPC) of hydraulic structures strongly reduces flood consequences under ideal circumstances. The performance of such flood control may, however, be significantly affected by uncertainties. This research quantifies the influence of rainfall forecast uncertainties and related uncertainties in the catchment rainfall-runoff discharges on the control performance for the Herk river case study in Belgium. To limit the model computational times, a fast conceptual model is applied. It is calibrated to a full hydrodynamic river model. A Reduced Genetic Algorithm is used as optimization method. Next to the analysis of the impact of the rainfall forecast uncertainties on the control performance, a Multiple Model Predictive Control (MMPC) approach is tested to reduce this impact. Results show that the deterministic MPC-RGA outperforms the MMPC and that it is inherently robust against rainfall forecast uncertainties due to its receding horizon strategy.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.