Research on Prediction of Air Quality Index Based on NARX and SVM

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In view of the problem of increasingly serious air pollution nowadays, this paper adopts Nonlinear Auto Regressive models with Exogenous Inputs (NARX) and Support Vector Machine (SVM) for regression and prediction on the Air Quality Index (AQI). The results show that the mean squared error of train set, validation set and test set and correlation coefficient of train set, validation set and test set in NARX model are 0.0029, 0.0064, 0.0072, 0.9865, 0.9731, 0.9701, respectively. Meanwhile, the results in SVM model are 0.0119, 0.0347, 0.0159, 0.9083, 0.9612, 0.8891, respectively. The above data prove the advantage of the NARX method in the prediction of the AQI.

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3580-3584

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August 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Kunwar P. Singh, Shikha Gupta, Premanjali Rai, Identifying pollution sources and predicting urban air quality using ensemble learning methods[J]. Atmospheric Environment, 2013, 80, 426-437.

DOI: 10.1016/j.atmosenv.2013.08.023

Google Scholar

[2] Wang Xiaochuan, Shi Feng, YuLei, li Yang. The Analysis of 43 Cases in MATLAB Neural Network [M]. Beijing: Beijing University of Aeronautics and Astronautics Press, (2013).

Google Scholar

[3] Zhang Guoliang, Zhang Zhijie, Du Gongjin etc. The Study of the impact of the accelerometer model based on NARX neural network [J]. Journal of missiles and guided journal, 2008, 28 (3) : 284-286.

Google Scholar

[4] Qin Huichao, Bai Yanping. The Classification of Passenger Car Based on SVM [J]. Journal of mathematics practice and understanding. And 2012 (18), 190-194.

Google Scholar

[5] Ambient air quality index (AQI) technical regulations (trial): the state environmental protection standards of the People's Republic of China, HJ. 633-(2012).

Google Scholar

[6] Yan Yan, Zhang Yunpeng, Li Kaiyue, Yang Guangmei. The Environmental Air Quality Prediction in Xi'an Based on the BP Neural Network [J]. Journal of electronic design engineering. 2013, 21 (21), 54-57.

Google Scholar

[7] Gao Huixuan. Application of Multivariate Statistical Analysis [M]. Beijing: Peking University press, (2005).

Google Scholar

[8] Cortes C, Vapnik V. Support-Vector network[J]. Machine Learning, 1995, 20, 273-297.

Google Scholar