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Deep Multi-task Learning for Air Quality Prediction

  • Bin Wang
  • Zheng Yan
  • Jie Lu
  • Guangquan Zhang
  • Tianrui Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11305)

Abstract

Predicting the concentration of air pollution particles has been an important task of urban computing. Accurately measuring and estimating makes the citizen and governments can behave with suitable decisions. In order to predict the concentration of several air pollutants at multiple monitoring stations throughout the city region, we proposed a novel deep multi-task learning framework based on residual Gated Recurrent Unit (GRU). The experimental results on the real world data from London region substantiate that the proposed deep model has manifest superiority than shallow models and outperforms 9 baselines.

Keywords

Deep learning Recurrent neural networks Neural networks Air quality prediction Urban computing 

Notes

Acknowledgment

This work was supported by the Natural Science Foundation of China (No. 61773324), the Fundamental Research Funds for the Central Universities (No. 2682015QM02) and the Australian Research Council (No. DP150101645).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Bin Wang
    • 1
    • 2
  • Zheng Yan
    • 2
  • Jie Lu
    • 2
  • Guangquan Zhang
    • 2
  • Tianrui Li
    • 1
  1. 1.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduChina
  2. 2.Faculty of Engineering and Information TechnologyUniversity of Technology, SydneySydneyAustralia

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