Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1198
Title: Comparison of several flood forecasting models in Yangtze River
Authors: Chau, KW 
Wu, CL
Li, YS 
Keywords: Floods
Forecasting
Models
Neural networks
Algorithms
Fuzzy sets
China
Rivers
Issue Date: Nov-2005
Publisher: American Society of Civil Engineers (ASCE)
Source: Journal of hydrologic engineering, ASCE, 2005, v. 10, no. 6, p. 485-491 How to cite?
Journal: Journal of hydrologic engineering 
Abstract: In a flood-prone region, quick and accurate flood forecasting is imperative. It can extend the lead time for issuing disaster warnings and allow sufficient time for habitants in hazardous areas to take appropriate action, such as evacuation. In this paper, two hybrid models based on recent artificial intelligence technology, namely, the genetic algorithm-based artificial neural network (ANN-GA) and the adaptive-network-based fuzzy inference system (ANFIS), are employed for flood forecasting in a channel reach of the Yangtze River in China. An empirical linear regression model is used as the benchmark for comparison of their performances. Water levels at a downstream station, Han-Kou, are forecasted by using known water levels at the upstream station, Luo-Shan. When cautious treatment is made to avoid overfitting, both hybrid algorithms produce better accuracy in performance than the linear regression model. The ANFIS model is found to be optimal, but it entails a large number of parameters. The performance of the ANN-GA model is also good, yet it requires longer computation time and additional modeling parameters.
URI: http://hdl.handle.net/10397/1198
ISSN: 1084-0699
DOI: 10.1061/(ASCE)1084-0699(2005)10:6(485)
Rights: Journal of Hydrologic Engineering © 2005 ASCE. The published version in ASCE's Engineering Database is located at: http://cedb.asce.org/cgi/WWWdisplay.cgi?0529091.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
JHydro.pdfPre-published version281.31 kBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

178
Last Week
0
Last month
3
Citations as of Jun 3, 2016

WEB OF SCIENCETM
Citations

167
Last Week
1
Last month
4
Citations as of Aug 25, 2016

Page view(s)

605
Last Week
0
Last month
Checked on Aug 28, 2016

Download(s)

1,188
Checked on Aug 28, 2016

Google ScholarTM

Check

Altmetric



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