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.
Skip Nav Destination
Article navigation
Research Article|
April 24 2013
Improved annual rainfall-runoff forecasting using PSO–SVM model based on EEMD
Wen-chuan Wang;
1Department of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
E-mail: [email protected]
Search for other works by this author on:
Dong-mei Xu;
Dong-mei Xu
1Department of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
2Water Resources and Flood Control, Dalian University of Technology, Dalian 116085, China
Search for other works by this author on:
Kwok-wing Chau;
Kwok-wing Chau
3Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
Search for other works by this author on:
Shouyu Chen
Shouyu Chen
2Water Resources and Flood Control, Dalian University of Technology, Dalian 116085, China
Search for other works by this author on:
Journal of Hydroinformatics (2013) 15 (4): 1377–1390.
Article history
Received:
April 06 2012
Accepted:
March 13 2013
Citation
Wen-chuan Wang, Dong-mei Xu, Kwok-wing Chau, Shouyu Chen; Improved annual rainfall-runoff forecasting using PSO–SVM model based on EEMD. Journal of Hydroinformatics 1 October 2013; 15 (4): 1377–1390. doi: https://doi.org/10.2166/hydro.2013.134
Download citation file: