Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction

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• 作者：Qi Ouyang ; Wenxi Lu ; Xin Xin ; Yu Zhang ; Weiguo Cheng ; Ting Yu
• 关键词：Rainfall forecasting ; Support vector regression ; Ensemble empirical mode decomposition ; Phase ; space reconstruction
• 刊名：Water Resources Management
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：30
• 期：7
• 页码：2311-2325
• 全文大小：736 KB
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• 作者单位：Qi Ouyang (1) (2)
  Wenxi Lu (1) (2)
  Xin Xin (1) (2)
  Yu Zhang (1) (2)
  Weiguo Cheng (1) (2)
  Ting Yu (1) (2)
  
  1. Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China
  2. College of Environment and Resources, Jilin University, Changchun, 130021, China
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geotechnical Engineering
  Meteorology and Climatology
  Civil Engineering
  Environment
  
• 出版者：Springer Netherlands
• ISSN：1573-1650

文摘

Rainfall links atmospheric and surficial processes and is one of the most important hydrologic variables. We apply support vector regression (SVR), which has a high generalization capability, to construct a rainfall forecasting model. Before construction of the model, a self-adaptive data analysis methodology called ensemble empirical mode decomposition (EEMD) is used to preprocess a rainfall data series. In addition, the phase-space reconstruction method is implemented to design input vectors for the forecasting model. The proposed hybrid model is applied to forecast the monthly rainfall at a weather station in Changchun, China as a case study. To demonstrate the capacity of the proposed hybrid model, a typical three-layer feed-forward artificial neural network model, an auto-regressive integrated moving average model, and a support vector regression model are constructed. Predictive performance of the models is evaluated based on normalized mean squared error (NMSE), mean absolute percent error (MAPE), Nash–Sutcliffe efficiency (NSE), and the coefficient of correlation (CC). Results indicate that the proposed hybrid model has the lowest NMSE and MAPE values of 0.10 and 14.90, respectively, and the highest NSE and CC values of 0.91 and 0.83, respectively, during the validation period. We conclude that the proposed hybrid model is feasible for monthly rainfall forecast and is better than the models currently in common use.