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Recently, a combination of model predictive control and a reduced genetic algorithm (RGA-MPC) has shown to be an efficient control technique for real-time flood control, making use of fast conceptual river models. This technique was so far only tested under ideal circumstances of perfect model predictions. Prediction errors originating from hydrodynamic model mismatches, however, result in a deterioration of the real-time control performance. Therefore, this paper presents two extensions of the RGA-MPC technique. First, a new type of conceptual model is introduced to further increase the computational efficiency. This reduced conceptual model is specially tailored for real-time flood control applications by eliminating all unnecessary intermediate calculations to obtain the flood control objectives and by introducing a new transport element by means of flow matrices. Furthermore, the RGA-MPC technique is extended with a flexible data assimilation approach that analyzes the past observed errors and applies an appropriate error prediction scheme. The proposed approach largely compensates for the loss in control performance due to the hydrodynamic model uncertainty.

The vegetation along a river reach varies throughout a year. Seasonal vegetation affects the hydrodynamic behaviour of the river system. Accordingly, flood studies should take this temporal variation into account. This also applies to real-time flood forecasting and control. This paper studies the impact of seasonal vegetation when considering real-time flood control performance, based on a model predictive control (MPC) scheme. The scheme makes use of a conceptual river model to limit the computational times, as well as a reduced genetic algorithm (RGA) for the optimization of the flood control gates. The impact of seasonal vegetation on the conceptual model accuracy was analysed and a flexible data assimilation approach developed, to adjust the model predictions to different vegetation scenarios. This method can successfully improve the efficiency of a control strategy, by strongly predicting and reducing the impact of seasonal vegetation changes on river conditions.

Model predictive control (MPC) has shown to be an efficient technique for real-time flood control. The evaluation of the control performance is, however, typically restricted to a limited set of flood events. In this paper, the control performance is evaluated for a long-term time series of 116 years of meteorological data as well as after climate scenarios. Such an evaluation became feasible thanks to the use of a computationally efficient MPC approach based on a fast conceptual river model and an adapted genetic algorithm. The uncertainties related to the river model and the rainfall forecasts were accounted for. The influence of these uncertainties on the MPC control performance was, however, found to be limited after applying data assimilation. Comparing the proposed MPC approach to a standard programmable logic control (PLC)-based regulation shows thatdespite the presence of uncertainties-MPC outperforms the PLC-based approach because it strongly reduces the incurred damage cost, the flood risk, and the frequency of flooding. This is still the case after considering the climate scenarios.

Abstract. 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.