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作者:

Zhang, Luyao (Zhang, Luyao.) | Bi, Jing (Bi, Jing.) | Yuan, Haitao (Yuan, Haitao.) | Zhang, Jun (Zhang, Jun.) | Qiao, Junfei (Qiao, Junfei.)

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EI Scopus

摘要:

Accurate and real-time prediction of water quality not only helps to assess the environmental quality of water, but also effectively prevents and controls water quality emergencies. In recent years, neural networks represented by Bidirectional Long Short-Term Memory (BiLSTM) and Encoder-Decoder (ED) frameworks have been shown to be suitable for prediction of time series data. However, traditional statistical methods cannot capture nonlinear characteristics of the water quality, and deep learning models often suffer from gradient disappearance and gradient explosion problems. This work proposes a hybrid water quality prediction method called VBEG, which combines V ariational Mode Decomposition (VMD), B iLSTM, an E D structure, and G enetic Simulated annealing-based particle swarm optimization (GSPSO). VBEG first adopts VMD to deal with nonlinear features in the original time series. Then, VBEG combines BiLSTM and the ED structure to capture bi-directional long-term correlations, and realize dimensionality reduction, respectively. Furthermore, VBEG adopts GSPSO to optimize its hyperparameters. Experimental results with real-life datasets demonstrate that the proposed VBEG outperforms two current state-of-the-art algorithms in terms of prediction accuracy. © 2022 IEEE.

关键词:

Forecasting Water quality Signal encoding Simulated annealing Mode decomposition Brain Long short-term memory Particle swarm optimization (PSO) Decoding Time series

作者机构:

  • [ 1 ] [Zhang, Luyao]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 2 ] [Bi, Jing]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 3 ] [Yuan, Haitao]Beihang University, School of Automation Science and Electrical Engineering, Beijing; 100191, China
  • [ 4 ] [Zhang, Jun]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 5 ] [Qiao, Junfei]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China

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ISSN: 1062-922X

年份: 2022

卷: 2022-October

页码: 1997-2002

语种: 英文

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SCOPUS被引频次: 3

ESI高被引论文在榜: 0 展开所有

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