AN AIR POLLUTANT CONCENTRATION PREDICTION MODEL BASED ON WAVELET TRANSFORM AND LSTM

HE Zhe-xiang; LI Lei

doi:10.13205/j.hjgc.202103016

Volume 39 Issue 3

Jul. 2021

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HE Zhe-xiang, LI Lei. AN AIR POLLUTANT CONCENTRATION PREDICTION MODEL BASED ON WAVELET TRANSFORM AND LSTM[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(3): 111-119. doi: 10.13205/j.hjgc.202103016

Citation:

HE Zhe-xiang, LI Lei. AN AIR POLLUTANT CONCENTRATION PREDICTION MODEL BASED ON WAVELET TRANSFORM AND LSTM[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(3): 111-119. doi: 10.13205/j.hjgc.202103016

HE Zhe-xiang, LI Lei. AN AIR POLLUTANT CONCENTRATION PREDICTION MODEL BASED ON WAVELET TRANSFORM AND LSTM[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(3): 111-119. doi: 10.13205/j.hjgc.202103016

Citation:

HE Zhe-xiang, LI Lei. AN AIR POLLUTANT CONCENTRATION PREDICTION MODEL BASED ON WAVELET TRANSFORM AND LSTM[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(3): 111-119. doi: 10.13205/j.hjgc.202103016

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AN AIR POLLUTANT CONCENTRATION PREDICTION MODEL BASED ON WAVELET TRANSFORM AND LSTM

doi: 10.13205/j.hjgc.202103016

HE Zhe-xiang^,,
LI Lei

Department of Environment Engineering, School of Metallurgy and Environment, Central South University, Changsha 410083, China

Received Date: 2020-01-20
Available Online: 2021-07-19

Abstract

Abstract

To solve the problem that current atmospheric pollutant prediction research has low accuracy of prediction and only pays attention to single pollutant type, a long short-term memory network atmospheric pollutant prediction model based on wavelet transform was proposed, to predict daily average PM_2.5, PM₁₀, SO₂, NO₂ and O₃ concentration of the next day. First, the high-dimensional data was converted into low-dimensional data by wavelet decomposition, and subsequently, the long short-term memory network prediction model was established for low-dimensional data. Finally, the decomposition sequence was reconstructed into the pollutant time series by wavelet reconstruction. Based on the data collected from 10 national control stations in Changsha from 2015 to 2018, the model was verified. The results showed that for the prediction of atmospheric pollutants of the next day, compared with the LSTM, MLR, WT-MLR, the root mean square error and absolute mean error of WT-LSTM model decreased by 50%, and the accuracy of the pollution level predictions of the five air pollutants were all above 80%.
- air quality index,
- machine learning,
- wavelet transform,
- air pollutant prediction

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References(23)

References

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