摘要

Due to the nature of spatial diffusion, urban air pollution presents a high correlation with regional concentration feature. Therefore, how to use spatio-temporal related data from urban multiple air pollution detection sites to predict air pollution concentration of a special target location is an important research effort for solving the problem of uneven site distribution. Concerning the multi-dimensional impacts of the air pollutant factors' features and the influence of meteorological factors, we propose an air pollutant concentration prediction model, which uses multi-site spatial detection data within a region to predict the concentration of the target station in this paper. This model is able to learn dimensional correlation characteristics and spatial correlation characteristics from multi-site pollutant concentration and meteorological data in the urban area through the multi-layer convolutional neural network, and then analyze the time-series correlation characteristics of multi-site concentration by utilizing the multi-layer auto-encoder network based long short-term memory network. The experimental results show that our proposed model obtains better performances than traditional machine learning based models under a real-world dataset, and meanwhile, the generalization performance of the proposed model has been examined based on multiple urban air pollution database.

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