Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network
An Zhang, Sheng Chen, Fen Zhao, Dai Xiao
Abstract
In the context of low-carbon globalization, green development has become the common pursuit of all countries and the theme of China’s development in the new era. Fine particulate matter (PM2.5) is one of the main challenges affecting air quality, and how to accurately predict PM2.5 plays a pivotal role in environmental governance. However, traditional data-driven approaches and deep learning methods for prediction rarely consider spatiotemporal features. Furthermore, different regions always have various implicit or hidden states, which have rarely been considered in the off-the-shelf model. To solve these problems, this study proposed a novel Spatial-Temporal Matrix Factorization Generative Adversarial Network (ST MFGAN) to capture spatiotemporal correlations and overcome the regional diversity problem at the same time. Specifically, Generative Adversarial Network (GAN) composed of graph Convolutional Network (GCN) and Long-Short-Term Memory (LSTM) network is used to generate a large amount of reliable spatiotemporal data, and matrix factorization network is used to decompose the vector output by GAN into multiple sub-networks. PM2.5 are finally combined and jointly predicted by the fusion layer. Extensive experiments show the superiority of the newly designed method.