Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1195
Title: | A split-step particle swarm optimization algorithm in river stage forecasting | Authors: | Chau, KW | Issue Date: | 30-Nov-2007 | Source: | Journal of hydrology, 30 Nov. 2007, v. 346, no. 3-4, p. 131-135 | Abstract: | An accurate forecast of river stage is very significant so that there is ample time for the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures as required. Since a variety of existing process-based hydrological models involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution. In this paper, a split-step particle swarm optimization (PSO) model is developed and applied to train multi-layer perceptrons for forecasting real-time water levels at Fo Tan in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging station (Tin Sum) or at Fo Tan. This paradigm is able to combine the advantages of global search capability of PSO algorithm in the first step and local fast convergence of Levenberg–Marquardt algorithm in the second step. The results demonstrate that it is able to attain a higher accuracy in a much shorter time when compared with the benchmarking backward propagation algorithm as well as the standard PSO algorithm. | Keywords: | River stage forecasting Split-step Particle swarm optimization Levenberg-Marquardt algorithm Artificial neural networks |
Publisher: | Elsevier | Journal: | Journal of hydrology | ISSN: | 0022-1694 | DOI: | 10.1016/j.jhydrol.2007.09.004 | Rights: | Journal of Hydrology © 2007 Elsevier B.V. The journal web site is located at http://www.sciencedirect.com. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
JH4.pdf | Pre-published version | 108.05 kB | Adobe PDF | View/Open |
Page views
136
Last Week
0
0
Last month
Citations as of Apr 21, 2024
Downloads
330
Citations as of Apr 21, 2024
SCOPUSTM
Citations
111
Last Week
2
2
Last month
1
1
Citations as of Apr 19, 2024
WEB OF SCIENCETM
Citations
101
Last Week
1
1
Last month
0
0
Citations as of Apr 25, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.