Water Resources Management

, Volume 32, Issue 2, pp 659–674 | Cite as

Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods



In this paper, two novel methods, echo state networks (ESN) and multi-gene genetic programming (MGGP), are proposed for forecasting monthly rainfall. Support vector regression (SVR) was taken as a reference to compare with these methods. To improve the accuracy of predictions, data preprocessing methods were adopted to decompose the raw rainfall data into subseries. Here, wavelet transform (WT), singular spectrum analysis (SSA) and ensemble empirical mode decomposition (EEMD) were applied as data preprocessing methods, and the performances of these methods were compared. Predictive performance of the models was evaluated based on multiple criteria. The results indicate that ESN is the most favorable method among the three evaluated, which makes it a promising alternative method for forecasting monthly rainfall. Although the performances of MGGP and SVR are less favorable, they are nevertheless good forecasting methods. Furthermore, in most cases, MGGP is inferior to SVR in monthly rainfall forecasting. WT and SSA are both favorable data preprocessing methods. WT is preferable for short-term forecasting, whereas SSA is excellent for long-term forecasting. However, EEMD tends to show inferior performance in monthly rainfall forecasting.


Ensemble empirical mode decomposition Multi-gene genetic programming Singular spectrum analysis Support vector regression Wavelet transform 



This work was supported by the National Natural Science Foundation of China (No. 41372237 and 41502221). The authors thank the editor and anonymous reviewers for their comments and suggestions.


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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Key Laboratory of Groundwater Resources and Environment, Ministry of EducationJilin UniversityChangchunChina
  2. 2.College of Environment and ResourcesJilin UniversityChangchunChina

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