Regional Spatiotemporal Collaborative Prediction Model for Air Quality

Numerous countries in the world pay close attention to the change of air quality and the control of air pollution. Air quality prediction has become a challenging issue owing to the complex characteristics with time-space nonlinearity and multi-dimensional feature interaction. This paper inventively proposes a three-dimensional presentation of the air quality data and establishes a deep model using spatiotemporal collaborative strategy to predict regional air quality, which can properly deal with multiple characteristics of air quality. Firstly, a theory of regional air quality prediction is given with reasonable analyses and scientific definitions, for systematically analyzing the closely connected areas and simultaneously predicting multiple locations. Secondly, a three-dimensional data structure called Relevance Data Cube is constructed utilizing a clustering algorithm, time sliding windows and correlation analysis of factors. This structure depicts the whole relationships among multiple dimensions of air quality data clearly and provides a basic analysis foundation for subsequent processing. Thirdly, based on the above theory and data structure, a prediction model is proposed to demonstrate the applicability of our work. Spatial relevance and factor influence are processed using dimension reduction technique provided by CNN, which performs automatic mining of spatial correlation and factors reaction of air quality. Besides, LSTM is combined with CNN to deal with the temporal dependency among the data dynamically. Finally, the proposed model is compared with other neural networks by a large number of experiments. The model is proved to better adapt to the characteristics and predict the air quality more accurately, which is more suitable and reliable for the field of air quality.