Dealing with rainfall forecast uncertainties in real-time flood control along the Demer river

Testo completo

(1)E3S Web of Conferences 7, 18023 (2016) FLOODrisk 2016 - 3rd European Conference on Flood Risk Management. DOI: 10.1051/ e3sconf/2016 0718023. Dealing with rainfall forecast uncertainties in real-time flood control along the Demer river 1,a. 1. 1. Evert Vermuyten , Pieter Meert , Vincent Wolfs and Patrick Willems. 1. 1. Hydraulics Division, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, 3001 Herverlee, Belgium. 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.. 1 Introduction In September 1998, the Demer basin in Belgium incurred a total damage loss of 16 million euros due to flooding [1]. It is no surprise that floods are the natural disasters with the highest economic damage costs. Moreover, the number of floods has strongly increased during the last decades due to two main driving forces: the increasing trend of extreme rainfall events by climate change [2-4] and rising urbanization [5]. The problem of floods will further enlarge in the future, due to these ongoing trends. Different strategies can be applied to reduce the flood hazard. Few examples are: source control measures such as installing infiltration facilities and detention tanks at large impervious surfaces, installing new retention basins, intelligent real-time control of hydraulic structures. The latter is the strategy studied in this research. It has the advantage to be applicable in densely populated areas lacking space to build new infrastructure by making optimal use of the existing infrastructure. Model Predictive Control (MPC), or Receding Horizon Control (RHC), was first used in the chemical process industry and is nowadays the approved method for intelligent control in different domains, including real-time flood control [6-8]. The nonlinear behaviour of river systems during flood events requires nonlinear river models, which turns the problem into a nonlinear programming problem. Such problems are more difficult to solve and can suffer from multiple local optima. Nonlinear MPC can be used to overcome this problem, but is computationally expensive and global optimality is not guaranteed [9-10]. Therefore, Breckpot [11] excluded a. the nonlinear characteristics from the river model to turn the problem into a Quadratic Programming problem. Nevertheless, calculation times were still very long, so this method was therefore only applied so far for small and simple river systems. Another solution is the combination of MPC and a heuristic approach. These heuristic approaches have gained much interest in recent years because of their ability to deal with nonlinearities, multi-objective analyses and uncertainties [12]. The MPC-GA technique is an example of such an approach whereby a Genetic Algorithm (GA) is used in combination with MPC. This technique has already been successfully applied for river flood control [13-15]. In these studies, conceptual models were used in a way complementary to the detailed full hydrodynamic models to limit the model computational times. The authors have further advanced this technique during recent years. Improvements are: consideration of larger and more complex river networks including floodplains, fast optimization methods that can handle large numbers of optimization variables and total economic flood damage as optimization objective. For that purpose, a Reduced Genetic Algorithm (RGA) was developed, based on the GA [16]. It was concluded that the MPC-RGA technique outperformed the current control strategy and significantly reduced flood consequences. This previous research, however, made the assumption that the model simulations and flood forecasts do not involve uncertainties. This is obviously never the case in practise. Uncertainties can have an important impact on the real-time flood control performance. Because it is expected that the most important source of uncertainty is the rainfall forecast. Corresponding author: Evert.Vermuyten@kuleuven.be. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)..

(2) E3S Web of Conferences 7, 18023 (2016) FLOODrisk 2016 - 3rd European Conference on Flood Risk Management. DOI: 10.1051/ e3sconf/2016 0718023.

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