Runoff prediction based on a VMD-LSTM model considering the decomposition error

For increasing the forecasting accuracy of runoff, a combined prediction model composed of the variational mode decomposition (VMD) and long short-term memory network (LSTM) is investigated in our manuscript. Firstly, data from the runoff is decomposed into three modal components via the VMD algorit...

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Bibliographic Details
Published in:Journal of physics. Conference series 2023-04, Vol.2491 (1), p.12017
Main Authors: Ma, Ya-Rong, Yang, Jing, Li, Hao, Liao, He, Feng, Yu-Xin
Format: Article
Language:English
Subjects:
Algorithms
Decomposition
Errors
Physics
Prediction models
Runoff
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Summary:For increasing the forecasting accuracy of runoff, a combined prediction model composed of the variational mode decomposition (VMD) and long short-term memory network (LSTM) is investigated in our manuscript. Firstly, data from the runoff is decomposed into three modal components via the VMD algorithm, for reducing the complexity of the original data. In addition, decomposition error is also been considered in this paper, and the hidden information in the series is extracted. Three subsequences and the decomposition error are handled by the LSTM method, respectively. Superimposing the prediction results, the prediction result of the runoff is thus derived. Experimental analysis is carried out, and a comparison of the VMD-LSTM model and other algorithmic indicates that the model constructed in our manuscript is more valid for predicting the runoff.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2491/1/012017