Forecasting Smog Clouds With Deep Learning
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866914963630587904 |
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| author | Oldenburg, Valentijn Cardenas-Cartagena, Juan Valdenegro-Toro, Matias |
| author_facet | Oldenburg, Valentijn Cardenas-Cartagena, Juan Valdenegro-Toro, Matias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02759 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Forecasting Smog Clouds With Deep Learning Oldenburg, Valentijn Cardenas-Cartagena, Juan Valdenegro-Toro, Matias Machine 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. |
| title | Forecasting Smog Clouds With Deep Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.02759 |