High-accuracy PM2.5 prediction via mutual information filtering and Bayesian-Optimized Spatio-Temporal Convolutional Networks
Abstract Air pollution, particularly fine particulate matter (PM2.5), poses severe threats to human health and ecological sustainability, rendering accurate prediction of PM2.5 concentrations imperative for proactive public health interventions and evidence-based policy-making. While deep learning m...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-08896-1 |
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