Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD

Wen Chuan Wang, Dong Mei Xu, Kwok Wing Chau, Shouyu Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

207 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1377-1390
Number of pages14
JournalJournal of Hydroinformatics
Issue number4
Publication statusPublished - 1 Dec 2013


  • Annual rainfall-runoff
  • Artificial neural networks
  • Ensemble empirical mode decomposition
  • Forecasting
  • Particle swarm optimization
  • Support vector machine

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Atmospheric Science


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