A set of deep learning algorithms for air quality prediction applications

This paper presents a set of machine learning algorithms, including grid-based (Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) algorithms to predict air quality. The methods were implemented on a spatiotemporal combination of air quality, meteorological and traffic data of the city of Madrid. The two methods are exposed to be reused for prediction in other scenarios and different air quality phenomena.