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http://dx.doi.org/10.9717/kmms.2013.16.12.1465

Air Pollutants Tracing Model using Perceptron Neural Network and Non-negative Least Square  

Yu, Suk-Hyun (Dept. of Information & Communications Engineering, Anyang Univ.)
Kwon, Hee-Yong (Dept. of Computer Science Engineering, Anyang Univ.)
Publication Information
Journal of Korea Multimedia Society / v.16, no.12, 2013 , pp. 1465-1474 More about this Journal
Abstract
In this paper, air pollutant tracing models using perceptron neural network(PNN) and non-negative least square(NNLS) are proposed. When the measured values of the air pollution and the contribution concentration of each source by chemical transport modeling are given, they estimate and trace the amount of the air pollutants emission from each source. Two kinds of emissions data are used in the experiments : CH4 and N2O of Geumgo-dong landfill greenhouse gas, and PM10 of 17 areas in Northeast Asia and eight regions of the Korean Peninsula. Emission values were calculated using pseudo inverse method, PNN and NNLS. Pseudo inverse method could be used for the model, but it may have negative emission values. In order to deal with the problem, we used the PNN and NNLS methods. As a result, the estimation using the NNLS is closer to the measured values than that using PNN. The proposed tracing models have better utilization and generalization than those of conventional pseudo inverse model. It could be used more efficiently for air quality management and air pollution reduction.
Keywords
Air Pollution Tracing Model; Perceptron; Non-negative Least Square;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Yong-Pyo Kim, "Air Pollution in Seoul Caused by Aerosols," Journal of Korean Society for Atmospheric Environment, Vol. 22, No. 5, pp. 535-553, 2006.   과학기술학회마을
2 IPCC, 1996 IPCC Guidelines for National Greenhouse Gas Inventories, 1996.
3 IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2006.
4 Suk-Hyun Yu, Air Quality Forecasting and Odor Source Tracking Method Using Neural Network and Nonnegative Least Square, A Doctoral Dissertation of Anyang University, 2011.
5 Youn-Seo Koo, Hui-Young Yun, Hee-Yong Kwon, and Suk-Hyun Yu, "A Development of PM10 Forecasting System," Journal of Korean Society for Atmospheric Environment, Vol. 26, No. 6, pp. 666-682, 2010.   과학기술학회마을   DOI   ScienceOn
6 Gyeonggi Institute of Health Environment, Evaluation Report of Air Pollution in Gyeonggi-do, 2008.
7 The Metropolis of Seoul, Characterization Research of PM10 using Batch Monitoring with Airborne PM10 in Seoul , 2010.
8 Yun-Seob Moon and Seong-Hwan Lee, "Estimation of Hourly Emission Flux of Asian Bust Using Empirical Formulas in the Source Area," Journal of Korean Society for Atmospheric Environment, Vol. 25, No. 6, pp. 539-549, 2009.   DOI   ScienceOn
9 D.K. Haneze, J.H. Seinfeld, and D.T. Shindell, "Inverse Modeling and Mapping us Air Quality Influence of Inorganic PM2.5 Precursor Emissions using the Adjoint of CEOS-Chem," Atmos. Chem. Phy., Vol. 9, No. 16, pp. 5877-5903, 2009.   DOI
10 M. Kopacz, D.J. Jacob, D.K. Hanze, and C.L. Heald, "Comparision of Adjoint and Analytical Bayesian Inversion Methods for Constraining Asian Sources of Carbon Monoxide Using Satellite(MOPITT) Measurements of CO Columns," Journal of Geophysical Research Atmospheres, Vol. 114, Issue D4, pp. 1-10, 2007.
11 David E. Rumelhart, James L. McClelland, and the PDP Research Group, Parallel Distributed Processing, MIT Press Publishers, Cambridge, Massachusetts., 1987.
12 Suk-Hyun Yu, Sang-Jin Park, Youn-Seo Koo, and Hee-Yong Kwon, "Odor Classification and Source Analysis using Pseudo Inverse," Journal of Korea Multimedia Society, Vol. 13, No. 8, pp. 1171-1182, 2010.   과학기술학회마을
13 C.L Lawson and R.J Hanson, Solving Least Squares Problems, Prentice-Hall Publishers, Englewood Cliffs, New Jersey, 1974.
14 D. Lee and H. Seung, "Learning the Parts of Objects by Non-negative Matrix Factorization," Nature, Vol. 401, No. 6755, pp. 788- 791, 1999.   DOI   ScienceOn
15 R.B. Bapat and T.E.S. Raghavan, Nonnegative Matrices and Applications, Cambridge University Press Publishers, Cambridge, UK., 1997.
16 A. Berman and R. Plemmons, Nonnegative Matrices in the Mathematical Sciences, Academic Press Publishers, New York., 1994.
17 Yong-Deok Kim and Seung-Jin Choi, "Nonnegative Tucker Decomposition," Journal of KI ISE: Computing Practices and Letters, Vol. 14, No. 3, pp. 296-300, 2008.   과학기술학회마을
18 N. Vo, B. Moran, and S. Challa, "Nonnegative Least Square Classifier for Face Recognition," Lecture Notes in Computer Science, Vol. 5553, pp. 449-456, 2009.
19 G. Bombara, M. Cococcioni, B. Lazzerini, and F. Marcelloni, "S-NNLS: An Efficient Nonnegative Least Squares Algorithm for Sequential Data," International J ournal for Numerical Methods in Biomedical Engineering, Vol. 27, No. 5, pp. 770-773, 2011.   DOI   ScienceOn
20 Wu Jiaying, J.A. van Aardt, and G.P. Asner, "A Comparison of Signal Deconvolution Algorithms Based on Small-Footprint LiDAR Waveform Simulation," IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 6, pp. 2402-2414, 2011.   DOI   ScienceOn
21 Youn-Seo Koo, Hee-Yong Kwon, Eun-Sung Son, Hyun-Jin Jin, Byoung-Won Jung, and Gwi-Wuk Heo, "Review of the Estimation Methodology of Methane Emission in a Landfill using Inverse Modeling Technique," Journal of Korean Society of Odor Research and Engineering, Vol. 12, No. 3, pp. 111-123. 2013.   DOI
22 Korea Environmental Industry&Technology Institute, Development of Technology for Improvement of Air Pollutant Amount Inventory and Reliability, 2012.