Code and data for "Two-stage Variational Mode Decomposition and Support Vector Regression for Streamflow Forecasting"

Published: 20 August 2020| Version 4 | DOI: 10.17632/ybfvpgvvsj.4
Ganggang Zuo


This data repository contains code and data for the research article “Two-stage Variational Mode Decomposition and Support Vector Regression for Streamflow Forecasting”, which is currently under review for the journal Hydrology and Earth System Sciences (HESS). The underlying data of this study is the monthly runoff data sets (from Jan 1953 to Dec 2018) of Huaxian, Xianyang, and Zhangjiashan stations, Wei River, China, which is organized in "time_series" directory. The unit of measurement is 10^8m^3/s. The fundamental code for decomposing runoff data, deciding input predictors and output target, generating machine learning samples, building Autoregressive moving average (ARIMA), support vector regression (SVR), Backpropagation neural network (BPNN), and Long short-term memory (LSTM) models, and evaluating the model performance is organized in the “tools” directory. The execution code for forecasting different runoff series using different decomposition algorithms (e.g., variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), discrete wavelet transform (DWT), Singular spectrum analysis (SSA) or non-decomposition-based (Orig)) are organized in “projects” directory (e.g., “huaxian_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.


Steps to reproduce

See readme.


Xi'an University of Technology


Signal Processing, Surface Water, Basin Hydrology