A Survey On Air Quality Prediction Using Traditional Statistics Method
DOI:
https://doi.org/10.32628/CSEIT2063197Keywords:
Air pollution, RMSE value, gasesAbstract
Air pollution is the release of pollutants into the atmospheric air which are harmful to human health and the planet as a whole. Car emissions, dust, pollen, chemicals from factories and mold spores may be suspended as a particle. In this survey, the analyzes are made revolving on air quality prediction using the traditional statistics method. The prediction using air pollutants are PM2.5, PM10, NO2, NOx, NO, SO2, CO, O3 and meteorological parameters such as Absolute Temparathure(AT) and Relative Humidity(RH). In this comparison experiments, common predicted algorithms are Naive Method, Auto-Regressive Integrated Moving Average(ARIMA), Exponentially Weighted Moving Average(EWMA), Linear Regression(LR), LSTM model, Prophet Model are analyzed.
References
- D’Amato G. et al.,(2010). Urban Air Pollution and Climate Change as Environmental Risk Factors of Respiratory Allergy. J. InvestigAllergolClinImmunol, (20(2), 95-102)
- Zhu, J.; Zhang, R.; Fu, B.; Jin, R.(2015) Comparison of ARIMA model and exponential smoothing model on 2014 air quality index in Yanqing county, Beijing, China. Appl. Comput. Math 4, 456–461. https://doi.org/10.11648/j.acm.20150406.19
- Armstrong, J.S., F. Collopy, 1992. Error measures for generalizing about forecasting methods: empirical comparisons. International Journal of Forecasting 8, 69-80.
- Kadilar, G.Ö.; Kadilar, C(2017) Assessing air quality in Aksaray with time series analysis. In Proceedings of the AIP Conference Proceedings, Antalya, Turkey, 18–21 April 2017; AIP Publishing: Melville, NY, USA, Volume 1833, p. 020112.
- Fahrmeir L, Kneib T, Lang S(2009) Regression –Modelle, Methoden und Anwendungen. 2nd edition. Berlin, Heidelberg: Springer.
- Rybarczyk, Y.; Zalakeviciute(2016) R. Machine learning approach to forecasting urban pollution. In Proceedings of the 2016 IEEE Ecuador Technical Chapters Meeting (ETCM), Guayaquil, Ecuador, 12–14 October.; IEEE: Piscataway, NJ, USA; pp. 1–6.
- Giovanni Raimondo,; Alfonso Montuori,; Walter Moniaci,; Eros Pasero1,; and EsbenAlmkvist An Application of Machine Learning Methods to PM10 Level Medium-Term Prediction.
- Johnston, F.R.; Boyland, J.E(1999).; Meadows, M.; Shale, E. Some properties of a simple moving average when applied to forecasting a time series. J. Oper. Res. Soc. 50, 1267–1271. https://doi.org/10.1057/palgrave.jors.2600823
- Bo Chang, Minmin Chen, Eldad Haber, and Ed H. Chi(2019) Antisymmetric RNN: A dynamical system view on recurrent neural networks. In International Conference on Learning Representations, Feb 2019. https://arxiv.org/abs/1902.09689
- Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M. A., Guo, J., Li, P. & Riddell, A., ‘Stan(2017): A probabilistic programming language’, Journal of Statistical Software 76(1).
- Tsay, R.S., and Tiao, G.C. (1984), “Consistent Estimates of Auto-regressive Parameters and Extended Sample Auto-correlation Function for Stationary and Non-stationary ARMA models,” Journal of American Statistical Association, 79, 84-96.
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