EmissionNet: Air Quality Pollution Forecasting for Agriculture
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915420974350336 |
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| author | Saligram, Prady Bhathal, Tanvir |
| author_facet | Saligram, Prady Bhathal, Tanvir |
| contents | Air pollution from agricultural emissions is a significant yet often overlooked contributor to environmental and public health challenges. Traditional air quality forecasting models rely on physics-based approaches, which struggle to capture complex, nonlinear pollutant interactions. In this work, we explore forecasting N$_2$O agricultural emissions through evaluating popular architectures, and proposing two novel deep learning architectures, EmissionNet (ENV) and EmissionNet-Transformer (ENT). These models leverage convolutional and transformer-based architectures to extract spatial-temporal dependencies from high-resolution emissions data |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_05416 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EmissionNet: Air Quality Pollution Forecasting for Agriculture Saligram, Prady Bhathal, Tanvir Machine Learning Artificial Intelligence Air pollution from agricultural emissions is a significant yet often overlooked contributor to environmental and public health challenges. Traditional air quality forecasting models rely on physics-based approaches, which struggle to capture complex, nonlinear pollutant interactions. In this work, we explore forecasting N$_2$O agricultural emissions through evaluating popular architectures, and proposing two novel deep learning architectures, EmissionNet (ENV) and EmissionNet-Transformer (ENT). These models leverage convolutional and transformer-based architectures to extract spatial-temporal dependencies from high-resolution emissions data |
| title | EmissionNet: Air Quality Pollution Forecasting for Agriculture |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.05416 |