Air Quality Prediction Based on Quadratic Prediction Model

  • Zijun Luo College of Urban Railway Transportation,Shanghai University of Engineering Science
  • Rui Zeng College of Urban Railway Transportation,Shanghai University of Engineering Science
  • Pan Wang College of Urban Railway Transportation,Shanghai University of Engineering Science
Ariticle ID: 2669
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DOI: https://doi.org/10.18282/l-e.v10i5.2669
Keywords: Secondary Prediction Model, Genetic Algorithm, Air Quality Forecast

Abstract

In order to improve the performance of the prediction model of air quality prediction,a secondary prediction mathematical model is established in this paper.The first is to clean the data and find the potential model relationship between variables through data mining and correlation methods,so as to establish the limit learning machine model.The model needs to be able to explain the influence of meteorological index variables on pollutant concentration diffusion to a certain extent.Then,the EML model is optimized by genetic algorithm,rolling optimization and other methods to reduce noise and make the data as accurate as possible.

References

[1] Liu Qian,Li CE,Yang Feng,Liu Libo,Deng Zhen.Research and application of weighted limit learning machine in pedestrian detection[J].Computer engineering and design,2019,40(08):2366-2371

[2] He fahu,Liang Jiantao.Research on air quality prediction based on neural network[J].Modern computer,2021(18):64-67

[3] Wu Jianwei,Liu Fuyun,Li Qiao.Matlab genetic algorithm function GA optimization example[J].Mechanical engineering and automation,2017(02):61-63

[4] Xu Rui,Liang Xun,Qi Jinshan,Li Zhiyu,Zhang Shusen.Frontier progress and trend of extreme learning machine[J].Journal of computer science,2019,42(07):1640-1670

[5] Kuang Jia Qing.Classification and gene selection of breast cancer subtypes based on genetic algorithm and weighted extreme learning machine[D].Jilin University,2017.

Published
2022-03-13
Issue
Vol. 10 No. 5 (2021)

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