Online scalable SVM ensemble learning method (OSSELM) for spatio-temporal air pollution analysis

@article{Ali2017OnlineSS,
  title={Online scalable SVM ensemble learning method (OSSELM) for spatio-temporal air pollution analysis},
  author={S. Ali and Simon Dacey},
  journal={International Journal of Data Mining & Knowledge Management Process},
  year={2017},
  volume={7},
  pages={21-38}
}
  • S. Ali, Simon Dacey
  • Published 2017
  • Computer Science
  • International Journal of Data Mining & Knowledge Management Process
Environmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem. The volume and complexity of the data have created the need to explore various machine learning models, however, those models have advantages and disadvantages when applied to regional air pollution analysis, furthermore, most environmental problems are global distribution problems. This research addressed spatio-temporal problem using decentralized computational technique named Online… Expand
1 Citations

References

SHOWING 1-10 OF 27 REFERENCES
SVM aggregation modelling for spatio-temporal air pollution analysis
  • 10
Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression
  • 11
Urban Air Pollution Monitoring System With Forecasting Models
  • 112
PM-25 forecasting use reconstruct phase space LS-SVM
  • Z. Li, J. Yang
  • Mathematics
  • 2010 The 2nd Conference on Environmental Science and Information Application Technology
  • 2010
  • 3
openair - An R package for air quality data analysis
  • 886
  • PDF
A Wireless Sensor Network Air Pollution Monitoring System
  • 281
  • PDF
...
1
2
3
...