Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1271
Title: | River stage forecasting with particle swarm optimization | Authors: | Chau, KW | Issue Date: | 2004 | Source: | In B Orchard, C Yang, M Ali (Eds.), Innovations in applied artificial intelligence : 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004, Ottawa, Canada, May 17-20, 2004 : proceedings, p.1166-1173. Berlin ; Hong Kong: Springer-Verlag, 2004 | Abstract: | An accurate water stage prediction allows the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures when required. Existing methods including rainfall-runoff modeling or statistical techniques entail exogenous input together with a number of assumptions. The use of artificial neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with certain drawbacks, such as very slow convergence and easily getting stuck in a local minimum. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting real-time water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station. It is shown from the verification simulations that faster and more accurate results can be acquired. | Keywords: | Algorithms Forecasting Hydrology Multilayers Neural networks Optimization Rain Statistical methods |
Publisher: | Springer-Verlag | ISBN: | 978-3-540-22007-7 | DOI: | 10.1007/978-3-540-24677-0_119 | Rights: | © Springer-Verlag Berlin Heidelberg 2004. The original publication is available at http://www.springerlink.com. |
Appears in Collections: | Book Chapter |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
LNAI8.pdf | Pre-published version | 140.19 kB | Adobe PDF | View/Open |
Page views
229
Last Week
0
0
Last month
Citations as of Dec 22, 2024
Downloads
200
Citations as of Dec 22, 2024
SCOPUSTM
Citations
46
Last Week
1
1
Last month
0
0
Citations as of Dec 19, 2024
WEB OF SCIENCETM
Citations
41
Last Week
0
0
Last month
0
0
Citations as of Dec 19, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.