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作者:

Bi, Jing (Bi, Jing.) | Chen, Zexian (Chen, Zexian.) | Yuan, Haitao (Yuan, Haitao.) | Lin, Yongze (Lin, Yongze.) | Qiao, Junfei (Qiao, Junfei.)

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EI Scopus

摘要:

Accurate prediction of water quality indicators can effectively prevent sudden water pollution events, and control pollution diffusion. Neural networks, e.g., long short-term memory (LSTM) and encoder-decoder network, have been widely used to predict time series data. However, as the water quality data increases, it becomes unstable and highly nonlinear. Accurate prediction of water quality becomes a big challenge. This work proposes a hybrid prediction method called VBAED to predict the water quality time series. VBAED combines Variational mode decomposition (VMD), Bidirectional input Attention mechanism, an Encoder with bidirectional LSTM (BiLSTM), and a Decoder with temporal attention mechanism and LSTM. Specifically, VBAED first adopts VMD to decompose the ground truth time series, and the decomposed results are used as the input along with other features. Then, a bidirectional input attention mechanism is adopted to add weights to input features from both directions. VBAED adopts BiLSTM as an encoder to extract hidden features from input features. Finally, the predicted result is obtained by an LSTM decoder with a temporal attention mechanism. Real-life data-based experiments demonstrate that VBAED obtains the best prediction results compared with other widely used methods. © 2022 IEEE.

关键词:

Water pollution Long short-term memory Water quality Decoding Signal encoding Variational mode decomposition Forecasting Time series

作者机构:

  • [ 1 ] [Bi, Jing]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 2 ] [Chen, Zexian]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 ] [Lin, Yongze]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: 2009-2014

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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