NARX Neural Network for Imputation of Missing Data in Air Pollution Datasets

2020 ◽  
pp. 226-240
Author(s):  
Miguel Calle ◽  
Marcos Orellana ◽  
Patricia Ortega-Chasi
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Missing Data ◽  
Narx Neural Network

scholarly journals Evaluating Visibility Range on Air Pollution using NARX Neural Network

2021 ◽  
Vol 9 (2) ◽  
pp. 540-547
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Air Quality ◽  
Air Pollutant ◽  
Restriction Factors ◽  
Network Efficiency ◽  
Independent Variables ◽  
Visibility Range ◽  
Least Area ◽  
Narx Neural Network

Evaluating air visibility range is considered as one of the apparent criteria of air quality. Haze air as a conclusion of air pollution causes unpleasant breathing, psychological effects, and visibility restriction. In this study, NARX neural network applied to determine air visibility restriction factors. Data of air quality control stations of Baghshomal, Rastebazar, and Abresan in Tabriz City, Iran used which include PM2.5, PM10, NO2, SO2, O3, and CO for the duration of four years from 2013 to 2017 that considered as independent variables. NARX neural network created to find each pollutant relation to visibility restriction and networks used for simulation to analysis network results in conspectuses condition. The results showed that PM10 pollutant has the most influence on-air visibility with R=0.9 in the train, R=0.728 in the test, and R=0.75 in validation process. Also error results of the PM10 obtained as MSE=0.054. Moreover, simulation results demonstrated the least area integral between curves according to ascending order for six pollutant factors and verified PM10 accuracy in NARX network simulation. The total result as study conclusion verified NARX neural network efficiency to evaluate air visibility range while using air pollutant parameters.

Research on air pollution system based on neural network

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Image Features ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
System Model ◽  
Process Data ◽  
Deep Convolutional Neural Networks ◽  
Air Pollution Monitoring ◽  
Neural Network Algorithm

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.

scholarly journals Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

2010 ◽  
Vol 28 (2) ◽  
pp. 381-393 ◽  
Author(s):  
L. Cai ◽  
S. Y. Ma ◽  
Y. L. Zhou
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Correlation Coefficient ◽  
Characteristic Time ◽  
Ring Current ◽  
Wind Data ◽  
Distinct Advantage ◽  
Elman Network ◽  
H Index ◽  
Narx Neural Network

Abstract. Similar to the Dst index, the SYM-H index may also serve as an indicator of magnetic storm intensity, but having distinct advantage of higher time-resolution. In this study the NARX neural network has been used for the first time to predict SYM-H index from solar wind (SW) and IMF parameters. In total 73 time intervals of great storm events with IMF/SW data available from ACE satellite during 1998 to 2006 are used to establish the ANN model. Out of them, 67 are used to train the network and the other 6 samples for test. Additionally, the NARX prediction model is also validated using IMF/SW data from WIND satellite for 7 great storms during 1995–1997 and 2005, as well as for the July 2000 Bastille day storm and November 2001 superstorm using Geotail and OMNI data at 1 AU, respectively. Five interplanetary parameters of IMF Bz, By and total B components along with proton density and velocity of solar wind are used as the original external inputs of the neural network to predict the SYM-H index about one hour ahead. For the 6 test storms registered by ACE including two super-storms of min. SYM-H<−200 nT, the correlation coefficient between observed and NARX network predicted SYM-H is 0.95 as a whole, even as high as 0.95 and 0.98 with average relative variance of 13.2% and 7.4%, respectively, for the two super-storms. The prediction for the 7 storms with WIND data is also satisfactory, showing averaged correlation coefficient about 0.91 and RMSE of 14.2 nT. The newly developed NARX model shows much better capability than Elman network for SYM-H prediction, which can partly be attributed to a key feedback to the input layer from the output neuron with a suitable length (about 120 min). This feedback means that nearly real information of the ring current status is effectively directed to take part in the prediction of SYM-H index by ANN. The proper history length of the output-feedback may mainly reflect on average the characteristic time of ring current decay which involves various decay mechanisms with ion lifetimes from tens of minutes to tens of hours. The Elman network makes feedback from hidden layer to input only one step, which is of 5 min for SYM-H index in this work and thus insufficient to catch the characteristic time length.

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