Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9580
Title: Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD
Authors: Wang, WC
Xu, DM
Chau, KW 
Chen, S
Keywords: Annual rainfall-runoff
Artificial neural networks
Ensemble empirical mode decomposition
Forecasting
Particle swarm optimization
Support vector machine
Issue Date: 2013
Publisher: International Water Association Publishing
Source: Journal of hydroinformatics, 2013, v. 15, no. 4, p. 1377-1390 How to cite?
Journal: Journal of hydroinformatics 
Abstract: Rainfall-runoff simulation and prediction in watersheds is one of the most important tasks in water resources management. In this research, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vector machine (SVM). In addition, the particle swarm optimization (PSO) is used to determine free parameters of SVM. The study data from a large size catchment of the Yellow River in China are used to illustrate the performance of the proposed model. In order to measure the forecasting capability of the model, an ordinary least-squares (OLS) regression and a typical three-layer feed-forward artificial neural network (ANN) are employed as the benchmark model. The performance of the models was tested using the root mean squared error (RMSE), the average absolute relative error (AARE), the coefficient of correlation (R) and Nash- Sutcliffe efficiency (NSE). The PSO-SVM-EEMD model improved ANN model forecasting (65.99%) and OLS regression (64.40%), and reduced RMSE (67.7%) and AARE (65.38%) values. Improvements of the forecasting results regarding the R and NSE are 8.43%, 18.89% and 182.7%, 164.2%, respectively. Consequently, the presented methodology in this research can enhance significantly rainfall-runoff forecasting at the studied station.
URI: http://hdl.handle.net/10397/9580
ISSN: 1464-7141
DOI: 10.2166/hydro.2013.134
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

22
Last Week
0
Last month
1
Citations as of Jul 23, 2017

WEB OF SCIENCETM
Citations

19
Last Week
0
Last month
2
Citations as of Jul 15, 2017

Page view(s)

23
Last Week
1
Last month
Checked on Jul 9, 2017

Google ScholarTM

Check

Altmetric



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