Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1275
Title: Rainfall-runoff correlation with particle swarm optimization algorithm
Authors: Chau, KW 
Keywords: Particle swarm optimization
Artificial neural networks
Siu Lek Yuen
Issue Date: 2004
Publisher: Springer Berlin / Heidelberg
Source: Lecture notes in computer science, 2004, v. 3174, p. 970-975 How to cite?
Abstract: A reliable correlation between rainfall-runoff enables the local authority to gain more amble time for formulation of appropriate decision making, issuance of an advanced flood forewarning, and execution of earlier evacuation measures. Since a variety of existing methods such as rainfall-runoff modeling or statistical techniques involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution, provided that their drawbacks can be overcome. Usual problems in the training with gradient algorithms are the slow convergence and easy entrapment in a local minimum. This paper presents a particle swarm optimization model for training perceptrons. It is applied to forecasting real-time runoffs in Siu Lek Yuen of Hong Kong with different lead times on the basis of the upstream gauging stations or at the specific station. It is demonstrated that the results are both more accurate and faster to attain, when compared with the benchmark backward propagation algorithm.
URI: http://hdl.handle.net/10397/1275
ISBN: 978-3-540-22843-1
DOI: 10.1007/b99834
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 SizeFormat 
LNCS5.pdfPre-published version132.25 kBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

22
Last Week
0
Last month
0
Citations as of May 2, 2016

WEB OF SCIENCETM
Citations

17
Last Week
0
Last month
0
Citations as of May 1, 2016

Page view(s)

361
Last Week
0
Last month
Checked on May 1, 2016

Download(s)

421
Checked on May 1, 2016

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.