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Artificial intelligence model for water resources management

Tang, H. W., Lei, Y., Lin, BinLiang, Zhou, Y. L. and Gu, Z. H. 2010. Artificial intelligence model for water resources management. Proceedings of the ICE - Water Management 163 (4) , pp. 175-187. 10.1680/wama.2010.163.4.175

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Abstract

The channel network in Pudong New District, Shanghai, is very complex owing to the large area of its basin, its numerous sluice gates, complex influencing factors and some other management issues involving water delivery, flood prevention, floodwater drainage, navigation and saltwater intrusion. It is generally difficult to achieve efficient water resources management merely through manually operating the sluice gates. Therefore, an artificial intelligence modelling system for managing the water resources in the channel network of Pudong New District has been developed by combining hydrodynamic simulation with an artificial intelligence technique. The artificial neural network model is used to develop sluice gate operation procedures according to the water levels in both the outer and inner rivers. The hydrodynamic model is used to simulate the flow discharges and water levels based on the sluice gate operation procedures. This modelling system has been applied successfully to the water resources management of the Pudong channel network. The results indicate that the modelling system satisfactorily meets the demands for sluice gate operation and water resources optimisation management of the channel network and thus provides decision-making support for integrated management of water resources in this inland channel network.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TC Hydraulic engineering. Ocean engineering
Uncontrolled Keywords: hydraulics & hydrodynamics; hydrology & water resource; mathematical modelling
Additional Information: Pdf uploaded in accordance with publisher's policy at http://www.sherpa.ac.uk/romeo/issn/1741-7589/ (accessed 24/04/2014).
Publisher: ICE Institution of Civil Engineers
ISSN: 1741-7589
Date of First Compliant Deposit: 30 March 2016
Last Modified: 03 Mar 2020 02:42
URI: http://orca.cf.ac.uk/id/eprint/16349

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