Forward Prediction of Runoff Data in Data-Scarce Basins with an Improved Ensemble Empirical Mode Decomposition (EEMD) Model

Forward Prediction of Runoff Data in Data-Scarce Basins with an Improved Ensemble Empirical Mode Decomposition (EEMD) Model

Data scarcity is a common problem in hydrological calculations that often makes water resources planning and engineering design challenging. Combining ensemble empirical mode decomposition (EEMD), a radial basis function (RBF) neural network, and an autoregression (AR) model, an improved EEMD predic...

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Bibliographic Details
Main Authors: Yinghao Yu, Hongbo Zhang, Vijay P. Singh
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Water
Subjects:
data scarce basins
runoff series
data forward prediction
ensemble empirical mode decomposition (EEMD)
stopping criteria
method of tracking energy differences (MTED)
Online Access:http://www.mdpi.com/2073-4441/10/4/388

Internet

http://www.mdpi.com/2073-4441/10/4/388

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