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This research aims to improve PM2.5 predictions and assess uncertainty using a novel Bayesian LSTM model.

February 10, 2026Open Access

Flipout Bayesian LSTM with Residual Attention for Uncertainty-Aware PM2.5 Forecasting and Anomaly Detection

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Demonstrates improved PM2.5 forecasting and uncertainty estimation in urban areas, indicating enhanced public health safety.

This research aims to improve PM2.5 predictions and assess uncertainty using a novel Bayesian LSTM model.
Developed a flipout Bayesian LSTM with residual attention.
Utilized Bayesian flipout inference for uncertainty representation.
Implemented a calibration module to improve confidence intervals.
Conducted experiments using hourly PM2.5 data from multiple stations in Nanjing.
Achieved F1 score of 0.996 for exceedance warnings.
Obtained F1 score of 0.691 for anomaly detection.
Demonstrated improved accuracy and noise robustness compared to baseline models.

Li et al. (2026) studied this question.

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February 10, 2026Open Access

View Full Paper

Ask AI

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Authors

Demonstrates improved PM2.5 forecasting and uncertainty estimation in urban areas, indicating enhanced public health safety.

This research aims to improve PM2.5 predictions and assess uncertainty using a novel Bayesian LSTM model.
Developed a flipout Bayesian LSTM with residual attention.
Utilized Bayesian flipout inference for uncertainty representation.
Implemented a calibration module to improve confidence intervals.
Conducted experiments using hourly PM2.5 data from multiple stations in Nanjing.
Achieved F1 score of 0.996 for exceedance warnings.
Obtained F1 score of 0.691 for anomaly detection.
Demonstrated improved accuracy and noise robustness compared to baseline models.

Li et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark