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March 16, 2024Alexandria Engineering JournalOpen Access

A systematic survey of air quality prediction based on deep learning

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Authors

ZZZhen ZhangSZShiqing ZhangCCCaimei Chen

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Overview

Systematic review demonstrates the proficiency of deep learning in air quality prediction for early warning systems, highlighting advances in spatiotemporal modeling.

Key Points

  • Deep learning architectures successfully identify complex nonlinear patterns in air quality data, surpassing traditional techniques across diverse environmental datasets.
  • Systematic analysis of prediction methods highlights temporal modeling alongside spatiotemporal modeling and attention mechanisms as critical components for predictive accuracy.
  • Supports the deployment of advanced air quality prediction within public health early warning systems, while calling for further development of nascent deep learning applications.

Cite This Study

Zhang et al. (2024) studied this question.

synapsesocial.com/papers/68e73b88b6db6435876b4e6bhttps://doi.org/10.1016/j.aej.2024.03.031
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