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Open AccessArticle

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

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School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
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Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
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College of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(20), 3788; https://doi.org/10.3390/ijerph16203788
Received: 23 July 2019 / Revised: 30 September 2019 / Accepted: 4 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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. View Full-Text
Keywords: time series prediction; unknown inference; atmospheric quality; neural network time series prediction; unknown inference; atmospheric quality; neural network
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Bai, Y.-T.; Wang, X.-Y.; Sun, Q.; Jin, X.-B.; Wang, X.-K.; Su, T.-L.; Kong, J.-L. Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network. Int. J. Environ. Res. Public Health 2019, 16, 3788.

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