Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting-Reference-Cited by-同舟云学术

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Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting

Published:2024-06 Issue: Volume:636 Page:131275
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Fang Jinjie,Yang Linshan^ORCID,Wen Xiaohu,Yu Haijiao,Li Weide^ORCID,Adamowski Jan F.^ORCID,Barzegar Rahim

Publisher

Elsevier BV

Reference122 articles.

1. Daily streamflow prediction using optimally pruned extreme learning machine;Adnan;J. Hydrol.,2019

2. Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches;Ahmadi;Appl. Water Sci.,2023

3. New double decomposition deep learning methods for river water level forecasting;Ahmed;Sci. Total Environ.,2022

4. Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran;Akbarian;J. Hydrol.,2023

5. Deep residual network in network;Alaeddine;Comput. Intell. Neurosci.,2021

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1. Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models;Engineering Applications of Computational Fluid Mechanics;2024-08-13

2. A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models;Journal of Hydrology;2024-08

3. Guidance on the construction and selection of relatively simple to complex data-driven models for multi-task streamflow forecasting;Stochastic Environmental Research and Risk Assessment;2024-07-17

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