Code and data for SF-VMD-LSTM model and other decomposition ensemble models

Published: 11 February 2020| Version 1 | DOI: 10.17632/bhjgdhgzjr.1
Ganggang Zuo,


This data repository contains code and data for research article, namely, “Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting”, which will be (or have already been) published in the Journal of Hydrology. The streamflow data sets (daily streamflow series (01/01/1967-31/12/2014) of Yangxian station (yx), Han River and Zhangjiashan station (zjs), Jing River, China) used to build the proposed model are in the “time_series” directory. The fundamental code for decomposing streamflow data, deciding input predictors and output target, generating machine learning samples, building long short-term memory (LSTM) models and evaluating the model performance are organized in “tools” directory. The execution code for forecasting different streamflow series (zjs and yx) using different decomposition algorithms (e.g., variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), discrete wavelet transform (DWT) or non-decomposition-based (orig)) are organized in “projects” directory (e.g., “zjs_vmd/projects/”). To reproduce the results of this paper, follow the instructions given in “”. Note that the same results demonstrated in this paper cannot be reproduced but similar results should be reproduced.



Xi'an University of Technology


Hydrology, Surface Water, Chaotic Signal, Forecasting