Big Data Research ›› 2018, Vol. 4 ›› Issue (6): 54-64.doi: 10.11959/j.issn.2096-0271.2018061

• STUDY • Previous Articles     Next Articles

Air quality estimation based on active learning and Kriging interpolation

Huijuan CHANG1,Zhiwen YU1,Zhiyong YU2,Qi AN1,Bin GUO1   

  1. 1 School of Computer Science,Northwestern Polytechnical University,Xi’an 710072,China
    2 College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108,China
  • Online:2018-11-15 Published:2019-01-11
  • Supported by:
    The National Science Fund for Distinguished Young Scholars;The National Key Research and Development Program of China;The National Natural Science Foundation of China

Abstract:

For the air quality monitoring station,it can only be deployed in a few locations and cannot obtain the air quality information of each location in the city.An air quality estimation algorithm based on active learning and Kriging interpolation was proposed.Firstly,Kriging interpolation was used as the basic air quality estimation algorithm.Secondly,combined with the idea of active learning,the position-first sampling with the highest confidence of the model was searched.Finally,an interpolation model based on active learning was established to select the least position-to-air quality.Sampling was performed to maximize the accuracy of air quality at other locations.The results show that the proposed algorithm can effectively improve the accuracy of air quality estimation,reduce the number of sampling stations and reduce the deployment cost.

Key words: Kriging interpolation, air quality index, active learning, spatial interpolation, air quality estimation

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