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

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.

[1]  Hong Zhang,et al.  A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting , 2017 .

[2]  Liang-Cheng Chang,et al.  Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management , 2012, Water Resources Management.

[3]  Xiao Qin Shang,et al.  Modification and Application of Gaussian Plume Model for an Industrial Transfer Park , 2013 .

[4]  Peter Kjeldsen,et al.  Relating landfill gas emissions to atmospheric pressure using numerical modelling and state-space analysis , 2003, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[5]  Wang MingYuan,et al.  Construction of air quality evaluation system based on FCM algorithm and BP neural network. , 2018 .

[6]  Chao-Ming Huang,et al.  Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting , 1995 .

[7]  Xiaoying Cao,et al.  Dispersion Coefficients for Gaussian Puff Models , 2011 .

[8]  Jürgen Schmidhuber,et al.  Multi-dimensional Recurrent Neural Networks , 2007, ICANN.

[9]  Qi Li,et al.  Research on Applied-Information Technology with PM2.5 Generation and Evolution Model Based on BP Neural Network , 2014 .

[10]  Nilesh K. Deshmukh,et al.  Autoregressive integrated moving average time series model for forecasting air pollution in Nanded city, Maharashtra, India , 2018, Modeling Earth Systems and Environment.

[11]  B. Nelson,et al.  Statistical methodology: V. Time series analysis using autoregressive integrated moving average (ARIMA) models. , 1998, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[12]  E. García-Gonzalo,et al.  Air Quality Modeling Using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain) , 2018, Environmental Modeling & Assessment.

[13]  S. Shimpalee,et al.  Investigation of gas diffusion media inside PEMFC using CFD modeling , 2006 .

[14]  Guilherme De A. Barreto,et al.  Long-term time series prediction with the NARX network: An empirical evaluation , 2008, Neurocomputing.

[15]  T. Overcamp An Exact Solution for the Ground-level Gamma Dose Rate from a Spherical Gaussian Puff , 2016, Health physics.

[16]  Jianqiang Li,et al.  Forecasting PM2.5 Concentration Using Spatio-Temporal Extreme Learning Machine , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

[17]  Baihua Xiao,et al.  Short-term cloud coverage prediction using the ARIMA time series model , 2018 .

[18]  Alexander V. Favorov,et al.  Hidden Markov Models for Evolution and Comparative Genomics Analysis , 2013, PloS one.

[19]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[20]  Yan Ying Xing Approach on Pollution Gases Diffusion Path of Small Spacing Tunnel Entrance Based on CFD , 2014 .

[21]  Zhisong Pan,et al.  时间序列预测方法综述 (Review of Time Series Prediction Methods) , 2019, 计算机科学.

[22]  Jiti Gao,et al.  Specification testing in nonlinear and nonstationary time series autoregression , 2009, 0911.3736.

[23]  Qinghua Liu,et al.  Fusing Moving Average Model and Stationary Wavelet Decomposition for Automatic Incident Detection: Case Study of Tokyo Expressway , 2014 .

[24]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[25]  Matthew M Botvinick,et al.  Short-term memory for serial order: a recurrent neural network model. , 2006, Psychological review.

[26]  Branko Ristic,et al.  Evaluation of Bayesian source estimation methods with Prairie Grass observations and Gaussian plume model: A comparison of likelihood functions and distance measures , 2017 .