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.

本套件包含用于实现马德里市空气质量预测的补充辅助材料。其下设两个核心子目录：数据（Data）目录与代码（Code）目录。数据目录涵盖原始数据（Raw-Data）与处理后数据（Processed-Data）两项内容：原始数据包含2019年1月至6月、2020年1月至6月的空气质量、气象及交通数据，以及马德里市空气质量监测站、气象监测站与交通测点的位置信息；处理后数据为原始数据经对应工作流处理后，满足所提出预测方法实现要求的输出结果。代码目录包含原始数据处理（Process Raw Data）、第4章卷积长短期记忆网络（ConvLSTM）、第5章双向卷积长短期记忆网络（BiConvLSTM）以及第6章A3T_GCN相关代码，上述内容提供了构建并实现所提出预测方法的完整流程。