Neural Networks and Fuzzy Systems in Model Based Control of the Overwaard Polder
Publication: Journal of Water Resources Planning and Management
Volume 131, Issue 2
Abstract
Recent developments in the field of computational intelligence (CI) techniques are helping to solve various problems of water resources modeling and management. These techniques have also shown their potential as an alternative approach to conventional controllers. In this paper, artificial neural networks (ANN) and fuzzy systems (FS) are shown to be efficient alternatives to using optimal control algorithms in real-time control of the polder water system of Overwaard in The Netherlands. The relation between the optimal decision or action and the influencing parameters are learned by ANN and FS and then used to derive the decisions and control actions in real-time. It was possible to reproduce the centralized behavior (in terms of water levels and corresponding discharges) of optimal control action by using easily measurable local information. Moreover, it is demonstrated that model simulation with external intelligent controllers is ten times faster than that with the optimal control.
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Acknowledgment
Part of this work was performed in the framework of the project “Data Mining, Knowledge Discovery, and Data-Driven Modeling” of the Delft Cluster research program supported by the Dutch government and the project SNN funded by the Dutch Foundation for Applied Research in Water Management (STOWA).
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 131 • Issue 2 • March 2005
Pages: 135 - 145
Copyright
© 2005 ASCE.
History
Received: Oct 24, 2003
Accepted: Jun 9, 2004
Published online: Mar 1, 2005
Published in print: Mar 2005
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