Forecasting Smog Clouds With Deep Learning
<|reference_start|>Forecasting Smog Clouds With Deep Learning: In this proof-of-concept study, we conduct multivariate timeseries forecasting for the concentrations of nitrogen dioxide (NO2), ozone (O3), and (fine) particulate matter (PM10 & PM2.5) with meteorological covariates between two locations using various deep learning models, with a focus on long short-term memory (LSTM) and gated recurrent unit (GRU) architectures. In particular, we propose an integrated, hierarchical model architecture inspired by air pollution dynamics and atmospheric science that employs multi-task learning and is benchmarked by unidirectional and fully-connected models. Results demonstrate that, above all, the hierarchical GRU proves itself as a competitive and efficient method for forecasting the concentration of smog-related pollutants.<|reference_end|>
@article{oldenburg2024forecasting,
    title={Forecasting Smog Clouds With Deep Learning},
    author={Valentijn Oldenburg, Juan Cardenas-Cartagena, Matias Valdenegro-Toro},
    journal={Oldenburg, V., Cardenas-Cartagena, J., & Valdenegro-Toro, M.
  (2024). Forecasting smog clouds with deep learning: A proof-of-concept. In
  ICML 2024 AI for Science Workshop. https://openreview.net/forum?id=UQa2PEVHMF},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02759},
    primaryClass={cs.LG}
}