Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network - Zhang, An; Chen, Sheng; Zhao, Fen; Dai, Xiao (Frontiers in Environmental Science, 2022)
Fine particulate matter (PM2.5) is one of the main challenges that affect air quality in the world. Predicting PM2.5 accurately plays a pivotal role in environmental management. 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 uesd 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 shows superiority of newly designed method.