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Particulate Matter 2.5 concentration prediction system based on uncertainty analysis and multi-model integration.
Yamei Chen1, Jianzhou Wang1, Runze Li1
1Institute of Systems Engineering, Macau University of Science and Technology, 999078, Macao.
The Science of the Total Environment
|December 10, 2024
Summary
This study introduces an advanced air pollution early warning system to combat health threats. The novel framework improves prediction accuracy and quantifies uncertainty for reliable particulate matter (PM2.5) forecasting.
Area of Science:
- Environmental Science
- Public Health
- Data Science
Background:
- Air pollution poses significant global public health risks.
- Existing early warning systems struggle with data noise and uncertainty estimation.
- Accurate prediction of pollutant concentrations is crucial for mitigation and sustainable development.
Purpose of the Study:
- To develop a robust air pollution concentration early warning system.
- To address limitations in existing models regarding data noise and uncertainty.
- To provide reliable PM2.5 concentration predictions for decision-makers.
Main Methods:
- A novel prediction framework combining decomposition strategy and meta-heuristic optimization.
- Data pre-processing including horizontal de-noising and vertical granulation.
- Integration of deterministic prediction with uncertainty analysis using interval probability.
Main Results:
- The proposed system significantly reduces data complexity through de-noising and granulation.
- Optimization algorithms enhance prediction accuracy and model generalization.
- Uncertainty in PM2.5 prediction is successfully quantified.
Conclusions:
- The integrated system offers a scientific and reliable approach to air pollution forecasting.
- Demonstrated improvements in prediction accuracy (68.12% reduction in APE, 68.88% reduction in RMSE) compared to benchmark models.
- Provides crucial technical support for effective air pollution control strategies.