A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

@article{Zhou2014AHM,
  title={A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.},
  author={Qingping Zhou and Haiyan Jiang and JianZhou Wang and Jianling Zhou},
  journal={The Science of the total environment},
  year={2014},
  volume={496},
  pages={264-274}
}
Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural… CONTINUE READING

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