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Data and Knowledge: An Interdisciplinary Approach for Air Quality Forecast

  • Cheng FengEmail author
  • Wendong Wang
  • Ye Tian
  • Xiangyang Gong
  • Xirong Que
Conference paper
  • 851 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11775)

Abstract

Air pollution has become a critical problem in rapidly developing countries. Prior domain knowledge combined with data mining offers new ideas for air quality prediction. In this paper, we propose an interdisciplinary approach for air quality forecast based on data mining and air mass trajectory analysis. The prediction model is composed of a temporal predictor based on local factors, a spatial predictor based on geographical factors, an air mass predictor tracking air pollutants transport corridors and an aggregator for final prediction. Experimental results based on real world data show that the cross-domain data mining method can significantly improve the prediction accuracy compared with other baselines, especially in the period of severe pollution.

Keywords

Air quality prediction Machine learning Data and knowledge Interdisciplinary approach 

Notes

Acknowledgement

This work was supported by National Natural Science Foundation of China (Grant No.61602051).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Cheng Feng
    • 1
    Email author
  • Wendong Wang
    • 1
  • Ye Tian
    • 1
  • Xiangyang Gong
    • 1
  • Xirong Que
    • 1
  1. 1.State Key Lab of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

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