Revisiting urban air quality forecasting: a regression approach
Tóm tắt
We address air quality (AQ) forecasting as a regression problem employing computational intelligence (CI) methods for the Gdańsk Metropolitan Area (GMA) in Poland and the Thessaloniki Metropolitan Area (TMA) in Greece. Linear Regression as well as Artificial Neural Network models are developed, accompanied by Random Forest models, for five locations per study area and for a dataset of limited feature dimensionality. An ensemble approach is also used for generating and testing AQ forecasting models. Results indicate good model performance with a correlation coefficient between forecasts and measurements for the daily mean
$$\hbox {PM}_{10}$$
concentration one day in advance reaching 0.765 for one of the TMA locations and 0.64 for one of the GMA locations. Overall results suggest that the specific modelling approach can support the provision of air quality forecasts on the basis of limited feature space dimensionality and by employing simple linear regression models.
Tài liệu tham khảo
Karatzas, K., Katsifarakis, N., Orlowski, C. Sarzyński A.: Urban air quality forecasting: a regression and a classification approach. In: In Nguyen N.T. et al. (eds.): Intelligent information and database systems, \(9^{\text{th}}\) Asian Conference on Intelligent Information and Database Systems, Part II, Lecture Notes in Artificial Intelligence vol. 10192, pp. 1–10 (2017). https://doi.org/10.1007/978-3-319-54430-4_52
Riffat, S., Powell, R., Aydin, D.: Future cities and environmental sustainability. Future Cities Environ. 2, 1 (2016). https://doi.org/10.1186/s40984-016-0014-2
Webel, S.: Forecasting Software that’s a Breath of Fresh Air. Pictures of the Future Siemens Magazine, (2016) http://www.siemens.com/innovation/en/home/pictures-of-the-future/infrastructure-and-finance/smart-cities-air-pollution-forecasting-models.html. Accessed 18 Aug 2017
Dawe, S. Paradice, D.: A systems approach to smart city infrastructure: a small city perspective. In: Proceedings of the Thirty Seventh International Conference on Information Systems, Dublin, http://iot-smartcities.lero.ie/wp-content/uploads/2016/12/A-Systems-Approach-to-Smart-City-Infrastructure-A-Small-City-Perspective.pdf. Accessed 18 Aug 2017
Marinov, M.B., Topalov, I., Gieva, E., Nikolov, G.: Air quality monitoring in urban environments. In: 39th International Spring Seminar on Electronics Technology (ISSE), Pilsen, pp. 443–448. (2016). https://doi.org/10.1109/ISSE.2016.7563237
Bukoski, B., Taylor, E.M.: Air quality forecasting. Air quality management 129–138 (2014)
Kukkonen, J., Olsson, T., Schultz, D.M., Baklanov, A., Klein, T., Miranda, A.I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K.E.J., Karatzas, K., San José, R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., Eben, K.: A review of operational, regional-scale, chemical weather forecasting models in Europe. Atmos. Chem. Phys. 12, 1–87 (2012)
Karatzas, K., Kaltsatos, S.: Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece. Simul. Modelling Pract. Theory 15(10), 1310–1319 (2007)
EEA, 2016: Air quality in Europe—2016 report, European Environment Agency, https://doi.org/10.2800/80982. https://www.eea.europa.eu//publications/air-quality-in-europe-2016. Accessed 18 Aug 2017
Juda-Rezler, K., Trapp, W., Reizer, M.: Modelling the impact of climate changes on particulate matter levels over Poland. In: Steyn, D.G., Rao, S.T. (eds.) Air pollution modeling and its application XX, pp. 499–450 (2010)
Moussiopoulos, N., Vlachokostas, C., Tsilingiridis, G., Douros, I., Hourdakis, E., Naneris, C., Sidiropoulos, C.: Air quality status in Greater Thessaloniki Area and the emission reductions needed for attaining the EU air quality legislation. Sci. Total Environ. 407(4), 1268–1285 (2009)
Andrews, A.: The clean air handbook, a practical guideline to EU air quality law, https://www.clientearth.org/reports/20140515-clientearth-air-pollution-clean-air-handbook.pdf. Accessed 18 Aug 2017
WHO: Air Quality Guidelines, global update 2005, ISBN 92 890 2192 6 via http://www.euro.who.int. Accessed 18 Aug. 2017
Siwek, K., Osowski, S.: Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors. Eng. Appl. Artif. Intel. 25(6), 1246–1258 (2012)
Zhou, Q., Jiang, H., Wang, J., Zhou, J.: A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Sci. Total Environ. 496, 264–274 (2014)
Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di Tommaso, S., Colangeli, C., Rosatelli, G., Carlo, P.: Recursive neural network model for analysis and forecast of PM10 and PM2.5. atmospheric. Pollut. Res. 8(4), 652–659 (2017)
Khokhlov, V.N., Glushkov, A.V., Loboda, N.S., Bunyakova, Y.Y.: Short-range forecast of atmospheric pollutants using non-linear prediction method. Atmos. Environ. 42(31), 7284–7292 (2008)
Orłowski, C., Sarzyński, A.: A model for forecasting pm10 levels with the use of artificial neural networks. In: Information Systems Architecture and Technology—the use of IT Technologies to Support Organizational Management in Risky Environment, Wrocław (2014)
Orłowski, C., Sarzyński, A., Karatzas, K., Katsifarakis, N., Nazarko J.: Adaptation of an ANN-based air quality forecasting model to a new application area. In: Król D., Nguyen N., Shirai K. (eds) Advanced Topics in Intelligent Information and Database Systems 479-488 (2017)
Karatzas, K., Kaltsatos, S.: Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece. Simul. Model. Pract. Theory 15(10), 1310–1319 (2007)
Voukantsis, D., Karatzas, K., Kukkonen, J., Räsänen, T., Karppinen, A., Kolehmainen, M.: Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci. Total Environ. 409, 1266–1276 (2011)
Szczepaniak, K., Astel, A., Bode, P., Sârbu, C., Biziuk, M., Raińska, E., Gos, K.: Assessment of atmospheric inorganic pollution in the urban region of Gdańsk. J. Radioanal. Nuclear Chem. 270(1), 35–42 (2006)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. Proc. Fourteenth Int. Joint Conf. Artif. Intel. 2(12), 1137–1143 (1995)
EPA: Guidelines for developing an air quality (ozone and PM2.5) forecasting program, U.S. Environmental Protection Agency report EPA-456/R-03-002, https://www3.epa.gov/airnow/aq_forecasting_guidance-1016.pdf. Accessed 18 Aug 2017
Voukantsis, D., Niska, H., Karatzas, K., Riga, M., Damialis, A., Vokou, D.: Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece. Atmos. Environ. 44(39), 5101–5111 (2010)
Tzima, F., Mitkas, P., Voukantsis, D., Karatzas, K.: Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst. with Appl. 38(5), 5019–5027 (2011)