PM 2.5 Concentration 7-days Prediction in the Beijing-Tianjin-Hebei Region Using a Novel Stacking Framework DOI Creative Commons

Xintong Gao,

Xiaohong Wang,

Fuping Li

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract High-precision prediction of near-surface PM2.5 concentration is an significant theoretical prerequisite for effective monitoring and prevention air pollution, also provides guiding suggestions health risk control. In view the fact that control variables existing models are mostly dependent on influencing factors at near-surface, it often difficult to fully explore continuous spatio-temporal characteristics in PM2.5. this study, MODIS remote sensing-derived Aerosol Optical Depth (AOD) daily data, atmospheric environment ground station data meteorological introduced identify strong correlation factors. A highly robust seven-day model constructed based Stacking algorithm combined with various machine learning methods improve generalisation ability model; estimation integrated compared analyzed LSTM, RF KNN models. The results demonstrated basis RF-LSTM-Stacking exhibited a better fit, R², RMSE, MAE values 0.95, 7.74 µg/m³, 6.08 respectively. This approach improved accuracy by approximately 17% single model. Based was evident LSTM-RF model, fusion-based algorithm, significantly enhanced provided reference predicting early warning monitoring.

Language: Английский

PM 2.5 Concentration 7-days Prediction in the Beijing-Tianjin-Hebei Region Using a Novel Stacking Framework DOI Creative Commons

Xintong Gao,

Xiaohong Wang,

Fuping Li

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract High-precision prediction of near-surface PM2.5 concentration is an significant theoretical prerequisite for effective monitoring and prevention air pollution, also provides guiding suggestions health risk control. In view the fact that control variables existing models are mostly dependent on influencing factors at near-surface, it often difficult to fully explore continuous spatio-temporal characteristics in PM2.5. this study, MODIS remote sensing-derived Aerosol Optical Depth (AOD) daily data, atmospheric environment ground station data meteorological introduced identify strong correlation factors. A highly robust seven-day model constructed based Stacking algorithm combined with various machine learning methods improve generalisation ability model; estimation integrated compared analyzed LSTM, RF KNN models. The results demonstrated basis RF-LSTM-Stacking exhibited a better fit, R², RMSE, MAE values 0.95, 7.74 µg/m³, 6.08 respectively. This approach improved accuracy by approximately 17% single model. Based was evident LSTM-RF model, fusion-based algorithm, significantly enhanced provided reference predicting early warning monitoring.

Language: Английский

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