Combining Model Predictive Control with a Reduced Genetic Algorithm for Real-Time Flood Control
Publication: Journal of Water Resources Planning and Management
Volume 144, Issue 2
Abstract
This paper presents a novel meta-heuristic approach for real-time flood control. The approach combines model predictive control (MPC) with a reduced genetic algorithm (RGA) to quickly find near-optimal solutions. The main control objective is to reduce the flood damage cost in an entire river basin. A fast conceptual model for the rivers and floodplains is used to compute the inundation levels and damages. The hydraulic component of the model is identified and calibrated to a full hydrodynamic model. The Demer basin, a flood-prone area in Belgium, is considered as a case study. Results show that the RGA converges faster to a near-optimal solution than a standard genetic algorithm. Furthermore, MPC-RGA outperforms the current programmable logic controller (PLC) based regulation by anticipating to rainfall forecasts.
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Acknowledgments
This research was supported by the Agency for Innovation by Science and Technology in Flanders. The authors would like to thank Innovyze for the InfoWorks RS software license, and the Flemish Environment Agency (VMM) for the data and InfoWorks RS model of the Demer basin. The International Marine and Dredging Consultants derived the damage-stage relations for the different floodplains along the basin, as part of a project by the Tijdelijke Handelsvereniging Antea Group, Fabricom, IMDC and IPCOS (THV AFII) for VMM.
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Published In
Journal of Water Resources Planning and Management
Volume 144 • Issue 2 • February 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Dec 7, 2016
Accepted: Jul 7, 2017
Published online: Nov 22, 2017
Published in print: Feb 1, 2018
Discussion open until: Apr 22, 2018
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