期刊
JOURNAL OF HYDROINFORMATICS
卷 15, 期 4, 页码 1377-1390出版社
IWA PUBLISHING
DOI: 10.2166/hydro.2013.134
关键词
annual rainfall-runoff; artificial neural networks; ensemble empirical mode decomposition; forecasting; particle swarm optimization; support vector machine
类别
资金
- Key Project of Science Technique Research of Henan Educational Committee [12A570005]
- foundation for University Backbone Teacher of Henan Province [2012GGJS-099]
- Central Research Grant of Hong Kong Polytechnic University [4-ZZAD]
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.
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