Real-time early warning and the prediction of air pollutants for sustainable development in smart cities

Abstract

Air quality forecasting is vital for sustainable development in smart cities and is essential to protect public health through early warning against air pollutants. This article presents a novel ensemble deep learning method able to capture spatiotemporal features on short-term time intervals and extract high data abstraction levels. This method uses a hierarchical framework with a new activation function called HT-Tanh to improve the prediction of air pollutant emissions in real time. As such, a residual neural network (ResNet) is integrated with a convolutional neural network (CNN) to deeply extract the temporal and spatial features from pollutant and meteorological data. The model network encodes the fully connected layer to fine-tune the CNN output and decodes the temporal prediction relations based on gate recurrent unit (GRU) to obtain more accurate results. The reliability of the proposed method is verified through a case study from Shantou City, China. The model performance is compared with the recently developed and traditional deep learning models. Results reveal that the proposed method has good precision in the multiscale air quality predictions. Compared with CNN-GRU, convolutional long short-term memory, and CNN models, the forecasting results of the proposed method are significantly improved. The presented method in this article can support the early warning systems in multiple regions for air pollution modeling studies.