Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction

Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction

Abstract To address the limitations of existing water quality prediction models in handling non-stationary data and capturing multi-scale features, this study proposes a hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposit...

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Bibliographic Details
Main Authors: Jie Long, Chong Lu, Yiming Lei, Zhong Yuan Chen, Yihan Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
Water quality monitoring
CEEMDAN
VMD
LSTM
FECA
Hybrid model
Online Access:https://doi.org/10.1038/s41598-025-96941-4
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https://doi.org/10.1038/s41598-025-96941-4

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