title: Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.
abstract: Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting medium and long-term runoff time series. First, the original runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and a residual series using EEMD technique for attaining deeper insight into the data characteristics. Then all IMF components and residue are predicted, respectively, through appropriate ANN models. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Two annual reservoir runoff time series from Biuliuhe and Mopanshan in China, are investigated using the developed model based on four performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting.
authors: School of Water conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450011, PR China. Electronic address: wangwen1621@163.com.
authors: Wen-chuan
authors: WC
authors: Wang
authors: Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China. Electronic address: cekwchau@polyu.edu.hk.
authors: Kwok-wing
authors: KW
authors: Chau
authors: School of Water conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450011, PR China. Electronic address: qiulin@ncwu.edu.cn.
authors: Lin
authors: L
authors: Qiu
authors: Lab of Water Disaster Management and Hydroinformatics, Sun Yat-sen University, Guangzhou 510275, PR China. Electronic address: eescyb@mail.sysu.edu.cn.
authors: Yang-bo
authors: YB
authors: Chen
mesh_terms: D002681:China; D005544:Forecasting / Q000379:methods*; D062070:Hydrology / Q000379:methods* / Q000706:statistics & numerical data / Q000639:trends; D015233:Models, Statistical; D016571:Neural Networks, Computer; D013997:Time Factors; D062066:Water Resources / Q000032:analysis* / Q000706:statistics & numerical data
keywords: Artificial neural network; Decomposition and ensemble; Ensemble empirical mode decomposition (EEMD); Hydrologic time series; Medium and long-term runoff forecasting