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Model uncertainty reduction for real-time flood control by means of a flexible data assimilation approach and reduced conceptual models

Abstract:

Recently, a combination of model pbkp_redictive 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 pbkp_redictions. Pbkp_rediction 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 pbkp_rediction scheme. The proposed approach largely compensates for the loss in control performance due to the hydrodynamic model uncertainty.