Variational mode decomposition coupled LSTM with encoder-decoder framework: an efficient method for daily streamflow forecasting

Accurate daily streamflow forecasting is crucial for effective flood control and water management. Decomposition ensemble models have proven to be effective in daily streamflow forecasting. However, it is important to recognize that the performance of streamflow forecasting within the decomposition...

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Bibliographic Details
Published in:Earth science informatics Vol. 18; no. 1; p. 38
Main Authors: Liu, Jiadong, Xu, Teng, Lu, Chunhui, Yang, Jie, Xie, Yifan
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
Springer Nature B.V
Subjects:
Coupled modes
Daily forecasts
Decomposition
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Effectiveness
Encoders-Decoders
Flood control
Flood forecasting
Flood management
Forecasting
Forecasting models
Information Systems Applications (incl.Internet)
Ontology
Performance enhancement
River basins
Simulation and Modeling
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Stream discharge
Stream flow
Streamflow forecasting
Water management
Data leakage
SF scheme
Variational mode decomposition
Streamflow forecasting
Encoder-decoder framework
ISSN:1865-0473, 1865-0481
Online Access:Get full text
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Summary:Accurate daily streamflow forecasting is crucial for effective flood control and water management. Decomposition ensemble models have proven to be effective in daily streamflow forecasting. However, it is important to recognize that the performance of streamflow forecasting within the decomposition ensemble models is significantly influenced by the choice of forecast model used. Therefore, a thoughtful selection of streamflow forecast models can greatly enhance the performance of decomposition ensemble models. Nevertheless, simple forecast models, such as long short-term memory, are susceptible to gradient disappearance or explosion, resulting in suboptimal peak streamflow forecasting performance, especially when dealing with long streamflow series. This limitation ultimately hinders the predictive accuracy of the decomposition ensemble model. Furthermore, while single-model forecasting (SF) schemes have demonstrated effectiveness, there has been limited exploration of the potential for coupling VMD and encoder-decoder framework in mid- and long-term daily streamflow forecasting. To address these issues and further enhance the capability of common VMD-LSTM model in mid and long-term streamflow forecasting, we have developed a novel and efficient forecast model VMD-LSTM-ED by integrating VMD and LSTM with a robust encoder-decoder structure in the SF scheme. Our proposed method was tested in the Fish River Basin in Maine, USA. To demonstrate its superiority, we also compared VMD-LSTM-ED against LSTM-ED, VMD-LSTM, and LSTM models. The results indicate that the proposed VMD-LSTM-ED exhibits outstanding performance across all aspects, with its advantage becoming more prominent as lead times increase, particularly in peak streamflow forecasting. For example, compared to VMD-LSTM, the Nash-Sutcliffe Efficiency (NSE) of VMD-LSTM-ED increased by 2.91%, 8.45%, 20.2% and 51.2% respectively, at lead times of 1, 3, 5, and 7 days; thus significantly enhancing the long-term prediction ability of VMD-LSTM.
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ISSN:1865-0473
1865-0481
DOI:10.1007/s12145-024-01569-z