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In order to ensure safety against high sea water levels, in many low-lying countries, water levels are maintained at certain safety levels, and dikes have been built, while large control structures have been installed that can also be adjusted dynamically after they have been constructed. Currently, these control structures are often operated purely locally, without coordination of actions being taken at different locations. Automatically coordinating these actions is difficult, as open water systems are complex, hybrid dynamical systems, in the sense that continuous dynamics (e.g. the evolution of the water levels) appear mixed with discrete events (e.g. the opening or closing of barriers). In low lands, this complexity is increased further due to bi-directional water flows resulting from backwater effects and interconnectivity of flows in different parts of river deltas. In this paper, we propose a model predictive control (MPC) approach that is aimed at automatically coordinating the actions of control structures. The hybrid dynamical nature of the water system is explicitly taken into account, in order to relieve the computational complexity involved in solving the MPC problem, we propose TIO-MPC, where TIO stands for time-instant optimization. Using this approach, the original MPC optimization problem that uses both continuous and integer variables is transformed into a problem involving only continuous variables. Simulation studies of current and future situations are used to illustrate the behavior of the proposed scheme. [ABSTRACT FROM AUTHOR]

In many process-based models, parameters have to be estimated from data. It is important to obtain not only the optimum value of the parameters, but also to assess the uncertainty in the parameters and, hence, in the models' output. In this paper, the Bayesian Monte Carlo technique known as Generalised Likelihood Uncertainty Estimation (GLUE) is used to evaluate the parameter-induced predictive uncertainty of a three-parameter model that predicts alongshore currents over a nearshore barred profile. GLUE performs a fully random sampling of feasible-parameters space, assigning non-zero likelihoods to those model simulations that outperform a user-defined threshold. Based on data gathered at six cross-shore position across an inner bar at Egmond aan Zee, The Netherlands, non-zero likelihoods were found for a rather wide range of parameter values, largely induced by an interdependence between two parameters that affect the width of current jets across the bar. The width of the 95% uncertainty interval was found empirically to increase linearly with the predicted magnitude of the alongshore current, from about 0.02–0.06 m/s when the current magnitude is near zero to about 0.2 m/s when it is near its maximum of about 1.1 m/s. These widths are approximately equal to a rough estimate of the errors in the data. In many cases the 95% uncertainty interval brackets the observations, although there are also various instances where this is not the case and apparently model structural errors dominate over parameter-induced errors. Model non-linearity and parameter interdependence cause the marginal parameter posterior distributions to differ remarkably from those obtained from traditional first-order approximations. [ABSTRACT FROM AUTHOR]