Deep Learning From Spatio-Temporal Data Using Orthogonal Regularizaion Residual CNN for Air Prediction

Air pollution is harmful to human health and restricts economic development, so predicting when and where air pollution will occur is a challenging and important issue, especially in fields of urban planning, factory production and human activities. In this paper, we propose a deep Spatio-Temporal Orthogonal Regularization Residual CNN (ST-OR-ResNet) for air prediction. Deep Convolutional Neural Network (CNN) is presented to capture the complex spatio-temporal relation of the dynamic biased meteorological data. Residual learning is designed to avoid unpredictable oscillations when training the network and verifying errors. For the issue of characteristic statistical migration and saddle point proliferation in deep network, the orthogonality regularizations are designed to stabilize the back-propagation errors, utilizing various advanced analytical tools such as restricted isometry property without extra hassle. We then benchmark their effects on public real-world datasets to demonstrate that ST-OR-ResNet has better predictive performance than the state-of-the-art methods.