PREDICTION OF PM<sub>2.5</sub> CONCENTRATION BASED ON INFORMER

DONG Hao; SUN Lin; OUYANG Feng

doi:10.13205/j.hjgc.202206006

Volume 40 Issue 6

Sep. 2022

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DONG Hao, SUN Lin, OUYANG Feng. PREDICTION OF PM_2.5 CONCENTRATION BASED ON INFORMER[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 48-54,62. doi: 10.13205/j.hjgc.202206006

Citation:

DONG Hao, SUN Lin, OUYANG Feng. PREDICTION OF PM_2.5 CONCENTRATION BASED ON INFORMER[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 48-54,62. doi: 10.13205/j.hjgc.202206006

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PREDICTION OF PM_2.5 CONCENTRATION BASED ON INFORMER

doi: 10.13205/j.hjgc.202206006

DONG Hao¹,
SUN Lin²,
OUYANG Feng^{1
,
,}

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;
2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China

Received Date: 2021-11-09
Available Online: 2022-09-01
Publish Date: 2022-09-01

Abstract

Abstract

For improving the low accuracy of the existing models for time series prediction of PM_2.5 concentration,a Seq2Seq multi-step PM_2.5 concentration prediction model for single-site based on Informer was proposed.With a series of air pollutant data and meteorological data in the past,Informer could make a forecast for PM_2.5 concentration in the future.The constructed model extracted the information of the input sequence based on the probsparse self-attention mechanism,which could widely capture the long-range dependency of the input sequence and model the complex nonlinearity between features,to improve the prediction accuracy eventually.The hourly air pollutant data and meteorological data of Beijing from 2015 to 2019 were used for training,validation and testing.Compared with RNN,LSTM and other existing models,the MAE,RMSE and R² metrics of Informer were the best for the time series prediction of PM_2.5 concentration in the next 1 to 6 hours,and then a more accurate prediction was realized.
- PM_2.5 concentration prediction,
- machine learning,
- Informer,
- self-attention mechanism,
- time series

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References(27)

References

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