1. [1] Wang, P., et al., A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Science of The Total Environment, 2015. 505(0): p. 1202-1212. [ DOI:10.1016/j.scitotenv.2014.10.078] 2. [2] Hasenfratz, D., et al., Participatory air pollution monitoring using smartphones. Mobile Sensing,2012 3. [3] [Zheng, Y., F. Liu, and H.-P. Hsieh. U-Air: When urban air quality inference meets big data. in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013. ACM. 4. [4] Sapankevych, N. and R. Sankar, Time Series Prediction Using Support Vector Machines: A Survey. IEEE Computational Intelligence Magazine, 2009. 4(2): p. 24-38. [ DOI:10.1109/MCI.2009.932254] 5. [5] Finardi, S., et al., A deterministic air quality forecasting system for Torino urban area, Italy. Environmental Modelling & Software, 2008. 23(3): p. 344-355. [ DOI:10.1016/j.envsoft.2007.04.001] 6. [6] Ranzato, L., et al., A comparison of methods for the assessment of odor impacts on air quality: Field inspection (VDI 3940) and the air dispersion model CALPUFF. Atmospheric Environment, 2012. 61: p. 570-579. [ DOI:10.1016/j.atmosenv.2012.08.009] 7. [7] Chaloulakou, A., M. Saisana, and N. Spyrellis, Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Science of the Total Environment, 2003. 313(1): p. 1-13. [ DOI:10.1016/S0048-9697(03)00335-8] 8. [8] Kumar, A. and P. Goyal, Forecasting of daily air quality index in Delhi. Science of the total environment, 2011. 409(24): p. 5517-5523. [ DOI:10.1016/j.scitotenv.2011.08.069] 9. [9] Chen, Y., et al., Ensemble and enhanced PM 10 concentration forecast model based on stepwise regression and wavelet analysis. Atmospheric Environment, 2013. 74: p. 34.359-7 10. [10] Dong, M., et al., PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining. Expert Systems with Applications, 2009. 36(5): p. 9046-9055. [ DOI:10.1016/j.eswa.2008.12.017] 11. [11] Elangasinghe, M.A., et al., Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering. Atmospheric Environment, 2014. 94(0): p. 106-116. [ DOI:10.1016/j.atmosenv.2014.04.051] 12. [12] [12] Wahid, H., et al., Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels. Applied Soft Computing, 2013. 13(10): p. 4087-4096. [ DOI:10.1016/j.asoc.2013.05.007] 13. [13] [13] Niska, H., et al., Evolving the neural network model for forecasting air pollution time series. Engineering Applications of Artificial Intelligence, 2004. 17(2): p. 159-167. [ DOI:10.1016/j.engappai.2004.02.002] 14. [14] [14] Singh, K.P., S. Gupta, and P. Rai, Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmospheric Environment, 2013. 80: p. 426-437. [ DOI:10.1016/j.atmosenv.2013.08.023] 15. [15] [15] García Nieto, P.J., et al., A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study. Applied Mathematics and Computation, 2013. 219(17): p. 8923-8937. [ DOI:10.1016/j.amc.2013.03.018] 16. [16] Ip, W., et al. Forecasting daily ambient air pollution based on least squares support vector machines. in Information and Automation (ICIA), 2010 IEEE International Conference on. 2010. IEEE. 17. [17] Reikard, G., Forecasting volcanic air pollution in Hawaii: Tests of time series models. Atmospheric Environment, 2012. 60: p. 593-600. [ DOI:10.1016/j.atmosenv.2012.06.040] 18. [18] Juhos, I., L. Makra, and B. Tóth, Forecasting of traffic origin NO and NO2 concentrations by Support Vector Machines and neural networks using Principal Component Analysis. Simulation Modelling Practice and Theory, 2008. 16(9): p. 1488-1502. [ DOI:10.1016/j.simpat.2008.08.006] 19. [19] Wang, W., C. Men, and W. Lu, Online prediction model based on support vector machine. Neurocomputing, 2008. 71(4): p. 550-558. [ DOI:10.1016/j.neucom.2007.07.020] 20. [20] Kurt, A. and A.B. Oktay, Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Systems with Applications, 2010. 37(12): p. 7986-7992. [ DOI:10.1016/j.eswa.2010.05.093] 21. [21] Mintz, D., Technical Assistance Document for the Reporting of Daily Air Quality-the Air Quality Index (AQI). 2012: US Environmental Protection Agency, Office of Air Quality Planning and Standards. 22. [22] Halek, F., A. Kavouci, and H. Montehaie, Role of motor-vehicles and trend of air borne particulate in the Great Tehran area, Iran. International journal of environmental health research, 2004. 14(4): p. 307-313. [ DOI:10.1080/09603120410001725649] 23. [23] Jenness, J., DEM surface tools v. 2.1. 254. Jenness Enterprises, Flagstaff, Arizona, USA.[Cited 5 Jan 2012.] Available from URL: http://www. jennessent. com/arcgis/surface_area. htm, 2010. 24. [24] Müller, K.-R., et al., Predicting time series with support vector machines, in Artificial Neural Networks—ICANN'97. 1, 997Springer. p. 999-1004. 25. [25] Thissen, U., et al., Using support vector machines for time series prediction. Chemometrics and intelligent laboratory systems, 2003. 69(1): p. 35-49. [ DOI:10.1016/S0169-7439(03)00111-4] 26. [26] Vapnik, V.N., Statistical learning theory. Vol. 2. 1998: Wiley New York. 27. [27] Burges, C.J., A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 1998. 2(2): p. 121-167. [ DOI:10.1023/A:1009715923555] 28. [28] Ertekin, S., et al. Learning on the border: active learning in imbalanced data classification. in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. 2007. ACM. [ DOI:10.1145/1321440.1321461] 29. [29] Laskov, P., et al., Incremental support vector learning: Analysis, implementation and applications. The Journal of Machine Learning Research, 2006:7 p. 1909-1936. 30. [30] Bordes, A., et al., Fast kernel classifiers with online and active learning. The Journal of Machine Learning Research, 2005. 6: p. 1579-1619. 31. [31] Bottou, L., Large-scale kernel machines. 2007: MIT Press. 32. [32] Yeganeh, B., et al., Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model. Atmospheric Environment, 2012. 55: p. 357-365. [ DOI:10.1016/j.atmosenv.2012.02.092] 33. [33] Hastie, T. and R. Tibshirani, Classification by pairwise coupling. The annals of statistics, 1998. 26(2:(p. 451-471.
|