AN AIR QUALITY INDEX PREDICTION METHOD BASED ON INVERSE VARIANCE MULTI-MODEL FUSION

SUN Zhaoyun; DU Yaohui; PEI Lili; LIU Ying; WU Yulong

doi:10.13205/j.hjgc.202302026

Volume 41 Issue 2

Feb. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(2): 197-204

SUN Zhaoyun, DU Yaohui, PEI Lili, LIU Ying, WU Yulong. AN AIR QUALITY INDEX PREDICTION METHOD BASED ON INVERSE VARIANCE MULTI-MODEL FUSION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(2): 197-204. doi: 10.13205/j.hjgc.202302026

Citation:

SUN Zhaoyun, DU Yaohui, PEI Lili, LIU Ying, WU Yulong. AN AIR QUALITY INDEX PREDICTION METHOD BASED ON INVERSE VARIANCE MULTI-MODEL FUSION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(2): 197-204. doi: 10.13205/j.hjgc.202302026

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AN AIR QUALITY INDEX PREDICTION METHOD BASED ON INVERSE VARIANCE MULTI-MODEL FUSION

doi: 10.13205/j.hjgc.202302026

School of Information Engineering, Chang'an University, Xi'an 710064, China

Received Date: 2022-04-27
Available Online: 2023-05-25
Publish Date: 2023-02-01

Abstract

Abstract

The prediction of air quality is of great significance for formulating environmental governance policies. Aiming at the problems of instability and weak generalization ability of the single model method, a multi-model fusion prediction method of air quality index (AQI) based on the inverse variance weight distribution method and fusion of three single model methods was proposed. Firstly, taking Beijing as an example, the air quality index prediction dataset was constructed. Secondly, five models, LSTM, GRU, Bi-LSTM, ARIMA and MLR, were constructed to predict the dataset, and the prediction results of these models were compared. Finally, in the multi-model fusion method, the inverse variance method was used to calculate the weight of three monomer models with high prediction accuracy, and the inverse variance fusion prediction model was constructed according to the calculated weight. Compared with the three monomer models with higher prediction accuracy and the weighted average fusion prediction model, the prediction accuracy, R² of the inverse variance fusion prediction model for air quality index was improved by 3.9%, 3.4%, 1.6% and 0.5% respectively, reaching 0.933. The results showed that the proposed inverse variance fusion prediction model integrated the advantages of each monomer prediction model, which could improve the prediction accuracy of air quality index.
- air quality index prediction,
- model fusion,
- neural network,
- long-short-term memory neural network,
- inverse variance

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References(23)

References

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