Data assimilation for supporting optimum control in large-scale river networks

We present a Nonlinear Model Predictive Control (NMPC) algorithm for real-time control of large-scale river networks in delta areas. The algorithm consists of an iterative, finite-horizon optimization of the system over a short-term control horizon. The underlying set of nonlinear internal process models represents relevant physical phenomena such as flow routing in the river network, and the dynamics of hydraulic structures. Data assimilation (DA) techniques turn out to be a key factor for the practical implementation of such schemes and may serve various purposes. First of all, DA contributes to the offline system identification of reduced internal models by parameter optimization. Secondly, we apply DA in an operational mode for model updating by adapting parameters, states, or outputs of the internal model for improving its lead time accuracy. The framework of DA and NMPC is applied on the control of a complex river network in the Dutch delta of Rhine River. We discuss the performance of a derivative-free optimization algorithm for calibrating the roughness coefficients of the underlying kinematic wave model and online parameter updating. Furthermore, we present the application of an Ensemble Kalman Filter (EKF) for updating model states as well as an output correction based on an AR(1) model. The contribution of these techniques in relation to the MPC performance is discussed in detail.