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LIANG Tao, XIE Gao-feng, MI Da-bin, JIANG Wen. PREDICTION OF PM10 CONCENTRATION BASED ON CEEMDAN-SE AND LSTM NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(2): 107-113. doi: 10.13205/j.hjgc.202002015
Citation: LIANG Tao, XIE Gao-feng, MI Da-bin, JIANG Wen. PREDICTION OF PM10 CONCENTRATION BASED ON CEEMDAN-SE AND LSTM NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(2): 107-113. doi: 10.13205/j.hjgc.202002015

PREDICTION OF PM10 CONCENTRATION BASED ON CEEMDAN-SE AND LSTM NEURAL NETWORK

doi: 10.13205/j.hjgc.202002015
  • Received Date: 2019-07-07
  • In view of the nonlinear and volatility characteristics of PM10 concentration time series, this paper presented a prediction model of PM10 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-sample entropy (SE)-long short-term memory (LSTM). The original PM10 concentration time series were decomposed into several sub-sequences with obvious complexity differences by CEEMDAN-SE. Then, an appropriate LSTM prediction model was built by adding meteorological parameters to each different sub-sequence. The final results were got by adding the prediction results. The data of four monitoring stations in Tangshan was used to implement simulation experiment, and the results confirmed that the proposed prediction model showed high prediction precision, and good universality, comparing with other prediction models.
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