Browse > Article
http://dx.doi.org/10.9717/kmms.2017.20.8.1208

Machine Learning-based Estimation of the Concentration of Fine Particulate Matter Using Domain Adaptation Method  

Kang, Tae-Cheon (Dept. of Digital Media, Graduate School, Catholic University of Korea)
Kang, Hang-Bong (Dept. of Digital Media, Graduate School, Catholic University of Korea)
Publication Information
Journal of Korea Multimedia Society / v.20, no.8, 2017 , pp. 1208-1215 More about this Journal
Abstract
Recently, people's attention and worries about fine particulate matter have been increasing. Due to the construction and maintenance costs, there are insufficient air quality monitoring stations. As a result, people have limited information about the concentration of fine particulate matter, depending on the location. Studies have been undertaken to estimate the fine particle concentrations in areas without a measurement station. Yet there are limitations in that the estimate cannot take account of other factors that affect the concentration of fine particle. In order to solve these problems, we propose a framework for estimating the concentration of fine particulate matter of a specific area using meteorological data and traffic data. Since there are more grids without a monitor station than grids with a monitor station, we used a domain adversarial neural network based on the domain adaptation method. The features extracted from meteorological data and traffic data are learned in the network, and the air quality index of the corresponding area is then predicted by the generated model. Experimental results demonstrate that the proposed method performs better as the number of source data increases than the method using conditional random fields.
Keywords
Fine Particulate Matter; Domain Adaptation; Big Data; Domain Adversarial Neural Network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 M.K. Shin, C.D. Lee, H.S. Ha, C.S. Choe, and Y.H. Kim, "The Influence of Meteorological Factors on PM 10 Concentration in Incheon," Journal of Korean Society for Atmospheric Environment, Vol. 23, pp. 322-331, 2007.   DOI
2 Y. Zheng, F. Liu, and H. Hsieh, "U-Air: When Urban Air Quality Inference Meets Big Data," Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1436-1444, 2013.
3 Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, et al. "Forecasting Fine-grained Air Quality Based on Big Data," Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2267-2276, 2015.
4 J.Y. Zhu, C. Zhang, S. Zhi, V.O.K. Li, J. Han, and Y. Zheng, "P-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data," arXiv Preprint arXiv: 1610.07045, 2016.
5 J.H. Leem, S.T. Kim, and H.C. Kim, "Public-health Impact of Outdoor Air Pollution for 2 nd Air Pollution Management Policy in Seoul Metropolitan Area, Korea," Annals of Occupational and Environmental Medicine, Vol. 27, pp. 1-9, 2015.   DOI
6 H. Wang, X. Yuan, X. Wang, Y. Zhang, and Q. Dai, "Real-time Air Quality Estimation Based on Color Image Processing," Proceeding of Visual Communications and Image Processing Conference, pp. 326-329, 2014.
7 S. Vardoulakis, B.E.A. Fisher, K.Pericleous, and N.G. Flesca, "Modelling Air Quality in Street Canyons: A Review," Atmospheric Environment, Vol. 37, Issue 2, pp. 155-182, 2003.   DOI
8 M. Chen, Z. Xu, K. Weinberger, and F. Sha, "Marginalized Denoising Autoencoders for Domain Adaptation," arXiv Preprint arXiv: 1206.4683, 2012.
9 J. Blitzer, R. McDonald, and F. Pereira, "Domain Adaptation with Structural Correspondence Learning," Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 120-128, 2006.
10 P. Germain, A. Habrard, F. Lavlolette, and E. Morvant, "A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers," Proceeding of International Conference on Machine Learning, pp. 738-746, 2013.
11 Y. Ganin, H. Ajakan, H. Larochelle, F. Laviolette, and V. Lempitsky, "Domain-adversarial Training of Neural Networks," Journal of Machine Learning Research, Vol. 17, pp. 1-35, 2016.
12 D.M.S. Arsa, G. Jati, A.J. Mantau, and I. Wasito, "Dimensionality reduction using deep belief network in big data case study: Hyperspectral image classification," Big Data and Information Security International Workshop, pp. 71-76, 2016
13 J. Lafferty, A. McCallu, and F.C.N. Pereira, "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data," Proceedings of the 18th International Conference on Machine Learning, pp. 282-289, 2001.
14 Semi-supervised Learning Liter-ature Survey (2008), http://pages.cs.wisc.edu/-jerryzhu/pub/ssl_survey.pdf (accessed Jul., 15, 2017)
15 H.S. Bae, S.H. Yu, and H.Y. Kwon, "Fast Data Assimilation using Kernel Tridiagonal Sparse Matrix for Performance Improvement of Air Quality Forecasting," Journal of Korea Multimedia Society, Vol.20, No. 2, pp. 363-370, 2017   DOI
16 M. Baktashmotlagh, M.T. Harandi, B.C. Lovell, and M. Salzmann, "Unsupervised Domain Adaptation by Domain Invariant Projection," Proceedings of the IEEE International Conference on Computer Vision, pp. 769-776, 2013.