Supplementary Materials for 'Spatiotemporal Prediction of Air Quality Using Machine Learning Techniques'

This package includes supplementary materials used to implement air quality prediction in the city of Madrid. It consists of two main subdirectories: Data and Code. The Data directory contains Raw-Data (air quality, meteorological and traffic data from the period of January-June 2019 and January-June 2020, and the location of air quality and meteorological monitoring stations and traffic measurement points of the city of Madrid) and Processed-Data (the output after raws data has gone through the workflow to meet the requirements corresponding to the implementation of the proposed forecasting approaches). The Code directory contains Process Raw Data, Chapter4-ConvLSTM, Chapter5-BiConvLSTM, and Chapter6-A3T_GCN, which provides the procedure for constructing and implementing the proposed approaches.