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

• Published in: International journal of environmental research and public health Vol. 16; no. 20; p. 3788
• Main Authors: Bai, Yu-ting, Wang, Xiao-yi, Sun, Qian, Jin, Xue-bo, Wang, Xiao-kai, Su, Ting-li, Kong, Jian-lei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09.10.2019; MDPI
• Subjects: Air Pollution; Algorithms; Artificial intelligence; Atmosphere; China; Deep learning; Environmental Monitoring - methods; Factories; Industry; Machine learning; Models, Theoretical; Neural networks; Neural Networks, Computer; Quality; Sensors; Spatial analysis; Statistical methods; Stochastic models; Time series; China; time series prediction; unknown inference; neural network; atmospheric quality
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph16203788

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.