EmissionNet: Air Quality Pollution Forecasting for Agriculture
This work addresses air quality forecasting for agriculture, an incremental improvement focusing on a specific domain.
The paper tackles forecasting N2O agricultural emissions by proposing two novel deep learning architectures, EmissionNet and EmissionNet-Transformer, which leverage convolutional and transformer-based methods to capture spatial-temporal dependencies from high-resolution data.
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