A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms ar...

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Bibliographic Details
Main Authors: Xike Zhang, Qiuwen Zhang, Gui Zhang, Zhiping Nie, Zifan Gui, Huafei Que
Format: Article
Language:English
Published: MDPI AG 2018-05-01
Series:International Journal of Environmental Research and Public Health
Subjects:
daily land surface temperature
forecasting
data-driven
hybrid model
Ensemble Empirical Mode Decomposition (EEMD)
Long Short-Term Memory (LSTM)
Neural Network (NN)
Dongting Lake basin
Online Access:http://www.mdpi.com/1660-4601/15/5/1032

Internet

http://www.mdpi.com/1660-4601/15/5/1032

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