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

• Published in: Journal of hydroinformatics Vol. 15; no. 4; pp. 1377 - 1390
• Main Authors: Wang, Wen-chuan, Xu, Dong-mei, Chau, Kwok-wing, Chen, Shouyu
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.10.2013
• Subjects: Annual; Annual rainfall; Annual runoff; Artificial neural networks; Computer simulation; Data analysis; Data processing; Decomposition; Earth sciences; Earth, ocean, space; Empirical analysis; Exact sciences and technology; Forecasting; Hydrology; Hydrology. Hydrogeology; Mathematical models; Methods; Neural networks; Particle swarm optimization; Rain; Rainfall-runoff relationships; Regression analysis; Resource management; Rivers; Runoff; Runoff forecasting; Support vector machines; Water resources; Water resources management; Watersheds; Huang Ho; Far East; Asia; China; models; efficiency; rainfall; neural networks; runoff; least-squares; rain water; digital simulation; drainage divide; drainage basins; correlation; optimization; water resource management; prediction; particles; performances; errors
• ISSN: 1464-7141; 1465-1734
• DOI: 10.2166/hydro.2013.134

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.