PREDICTION OF PM<sub>10</sub> CONCENTRATION BASED ON CEEMDAN-SE AND LSTM NEURAL NETWORK

LIANG Tao; XIE Gao-feng; MI Da-bin; JIANG Wen

doi:10.13205/j.hjgc.202002015

Volume 38 Issue 2

Feb. 2020

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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

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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

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

doi: 10.13205/j.hjgc.202002015

1. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China;
2. Jointo Energy Investment Co., Ltd, Hebei, Shijiazhuang 050001, China

Received Date: 2019-07-07

Abstract

Abstract

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.
- PM₁₀,
- concentration prediction,
- ensemble empirical mode decomposition,
- sample entropy,
- time series

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References(17)

References

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