Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network

Yu-ting Bai, Xiao-yi Wang, Qian Sun, Xue-bo Jin, Xiao-kai Wang, Ting-li Su and Jian-lei Kong
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Yu-ting Bai: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Xiao-yi Wang: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Qian Sun: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Xue-bo Jin: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Xiao-kai Wang: College of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China
Ting-li Su: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Jian-lei Kong: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China

IJERPH, 2019, vol. 16, issue 20, 1-15

Abstract: The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of “Circumjacent Monitoring-Blind Area Inference”. In the fusion network, the nonlinear autoregressive network was set up for the time series prediction of circumjacent points, and the full connection layer was built for the nonlinear relation fitting of multiple points. Secondly, the physical structure and learning method was studied for the sub-elements in the fusion network. Thirdly, the spatio-temporal prediction algorithm was proposed based on the network for the blind area monitoring problem. Finally, the experiment was conducted with the practical monitoring data in an industrial park in Hebei Province, China. The results show that the solution is feasible for the blind area analysis in the view of spatial and temporal dimensions.

Keywords: time series prediction; unknown inference; atmospheric quality; neural network (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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