ArticleInternational journal of environmental research and public health2019
Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series.Entropy (Basel, Switzerland) · 2022Article
- Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction.Computational intelligence and neuroscience · 2022Article
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
- Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy.Entropy (Basel, Switzerland) · 2021Article
- Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting.Computational intelligence and neuroscience · 2021Article
- Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model.Sensors (Basel, Switzerland) · 2020Article
- A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model.Sensors (Basel, Switzerland) · 2020Article
- Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis.International journal of environmental research and public health · 2020Article
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Authors and funding
7 authors.
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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.
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