Improved monthly runoff time series prediction using the CABES-LSTM mixture model based on CEEMDAN-VMD decomposition
Accurate runoff prediction is vital in efficiently managing water resources. In this paper, a hybrid prediction model combining complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, CABES, and long short-term memory network (CEEMDAN-VMD-CABES-LSTM) is pr...
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| 主要な著者: | , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
IWA Publishing
2024-01-01
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| シリーズ: | Journal of Hydroinformatics |
| 主題: | |
| オンライン･アクセス: | http://jhydro.iwaponline.com/content/26/1/255 |
| タグ: |
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