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Water 2016, 8(9), 367; doi:10.3390/w8090367

A New Hybrid Forecasting Approach Applied to Hydrological Data: A Case Study on Precipitation in Northwestern China

1
School of Mathematics and Statistics, Gansu Key Laboratory of Applied Mathematics and Complex Systems, Lanzhou University, Lanzhou 730000, China
2
State Key Laboratory of Cryosphere Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Y. Jun Xu
Received: 30 May 2016 / Revised: 12 August 2016 / Accepted: 19 August 2016 / Published: 25 August 2016
(This article belongs to the Special Issue Tackling Complex Water Problems in China under Changing Environment)
View Full-Text   |   Download PDF [5553 KB, uploaded 25 August 2016]   |  

Abstract

Hydrogeological disasters occur frequently. Proposing an effective prediction method for hydrology data can play a guiding role in disaster prevention; however, due to the complexity and instability of hydrological data, this is difficult. This paper proposes a new hybrid forecasting model based on ensemble empirical mode decomposition (EEMD), radial basis function neural networks (RBFN), and support vector machine (SVM), this is the EEMD–RBFN–SVM method, which has achieved effective results in forecasting hydrologic data. The data were collected from the Yushu Tibetan Autonomous Region of the Qinghai Province. To validate the method, the proposed hybrid model was compared to the RBFN, EEMD–RBFN, and SAM–ESM–RBFN models, and the results show that the proposed hybrid model had a better generalization ability. View Full-Text
Keywords: ensemble empirical mode decomposition; radial basis function neural networks; support vector machine; hybrid approach; precipitation prediction ensemble empirical mode decomposition; radial basis function neural networks; support vector machine; hybrid approach; precipitation prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Jiao, G.; Guo, T.; Ding, Y. A New Hybrid Forecasting Approach Applied to Hydrological Data: A Case Study on Precipitation in Northwestern China. Water 2016, 8, 367.

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