EmissionNet: Air Quality Pollution Forecasting for Agriculture
Prady Saligram, Tanvir Bhathal

TL;DR
This paper introduces EmissionNet and EmissionNet-Transformer, two deep learning models that improve air quality pollution forecasting for agriculture by capturing complex spatial-temporal dependencies in emissions data.
Contribution
The paper proposes two novel deep learning architectures, EmissionNet and EmissionNet-Transformer, tailored for forecasting agricultural N₂O emissions, addressing limitations of traditional physics-based models.
Findings
EmissionNet outperforms baseline models in accuracy.
Transformer-based architecture captures complex dependencies effectively.
Models demonstrate potential for improved environmental monitoring.
Abstract
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 NO 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
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