A Review of the Application of Machine Learning and Geospatial Analysis Methods in Air Pollution Prediction

Zhalehdoost, Alireza; Taleai, Mohammad

doi:10.22059/poll.2022.336044.1300

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	A Review of the Application of Machine Learning and Geospatial Analysis Methods in Air Pollution Prediction
Pollution
دوره 8، شماره 3، مرداد 2022، صفحه 904-933 اصل مقاله (1.45 M)
نوع مقاله: Review Paper
شناسه دیجیتال (DOI): 10.22059/poll.2022.336044.1300
نویسندگان
Alireza Zhalehdoost^* ¹؛ Mohammad Taleai²
¹GIS Department, Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, P.O.Box 16315-1355, Tehran, Iran
²School of Built Environment, Faculty of the Arts, Design & Architecture, University of New South Wales (UNSW).P.O.Box 259, Sydney, Australia
چکیده
During the past years, air quality has become an important global issue, due to its impact on people's lives and the environment, and has caused severe problems for humans. As a prevention to effectively control air pollution, forecasting models have been developed as a base for decision-makers and urban managers during the past decades. In general, these methods can be divided into three classes: statistical methods, machine learning methods and hybrid methods. This study's primary intent is to supply an overview of air pollution prediction techniques in urban areas and their advantages and disadvantages. A comparison has also been made between the methods in terms of error assessment and the use of geospatial information systems (GIS). In addition, several approaches were applied to actual data, and the findings were compared to those acquired from previous published literatures. The results showed that forecasting using machine learning and hybrid methods has provided better results. It has also been demonstrated that GIS can improve the results of the forecasting methods.
کلیدواژه‌ها
Air pollution forecast؛ Statistical methods؛ Neural Network؛ Machine learning؛ GIS

مراجع
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A Review of the Application of Machine Learning and Geospatial Analysis Methods in Air Pollution Prediction

Abdullah, S., Ismail, M. and Fong, S. (2017). Multiple linear regression (MLR) models for long term PM10 concentration forecasting during different monsoon seasons. Journal of Sustainability Science and Management, 12(1), 60-69. http://jbsd.umt.edu.my/wp-content/uploads/sites/51/2017/06/7-web.pdf

Aditya, C., Deshmukh, C. R., Nayana, D. and Vidyavastu, P. G. (2018). Detection and prediction of air pollution using machine learning models. International Journal of Engineering Trends and Technology (IJETT), 59(4). https://doi.org/10.14445/22315381/IJETT-V59P238

An overview of the analyzing patterns toolset. From ESRI. Website at:https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/an-overview-of-the-analyzing-patterns-toolset.htm

Arslan, O. and Akyürek, Ö. (2018). Spatial modelling of air pollution from pm10 and so2 concentrations during winter season in marmara region (2013-2014). International Journal of Environment and Geoinformatics, 5(1), 1-16. https://doi.org/10.30897/ijegeo.412391

Arthur C. Stern, editors. The Effects of Air Pollution; Third edition. Academic Press: New York, NY, USA.1977.

Asadolah-Fardi, G. and Zangoi, H. (2017). PM10 AIR POLLUTION IN MASHAD CITY USING ARTIFICIAL NEURAL NETWORK AND MAKOV CHAIN MODEL. Journal of Applied researches in Geographical Sciences.; 17(47):39-59. https://www.sid.ir/en/journal/ViewPaper.aspx?ID=608455

Asghari, M. and Nematzadeh, H. (2016). Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network. Journal of AI and Data Mining, 4(1), 49-54. https://doi.org/10.5829/IDOSI.JAIDM.2016.04.01.06

Ayturan, Y. A., Ayturan, Z. C. and Altun, H. O. (2018). Air pollution modelling with deep learning: a review. International Journal of Environmental Pollution and Environmental Modelling, 1(3), 58-62. https://api.semanticscholar.org/CorpusID:201904082

Bai, L., Wang, J., Ma, X. and Lu, H. (2018). Air pollution forecasts: An overview. International journal of environmental research and public health, 15(4), 780. https://doi.org/10.3390/ijerph15040780

Broomhead, D. and Lowe, D. (1988). Multivariable functional interpolation and adaptive networks, complex systems, vol. 2. https://sci2s.ugr.es/keel/pdf/algorithm/articulo/1988-Broomhead-CS.pdf

Cabaneros, S. M., Calautit, J. K. and Hughes, B. R. (2019). A review of artificial neural network models for ambient air pollution prediction. Environmental Modelling & Software, 119, 285-304. https://doi.org/10.1016/j.envsoft.2019.06.014

Cai, M., Yin, Y. and Xie, M. (2009). Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transportation Research Part D: Transport and Environment, 14(1), 32-41. https://doi.org/10.1016/j.trd.2008.10.004

Chaloulakou, A., Grivas, G. and Spyrellis, N. (2003). Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment. Journal of the Air & Waste Management Association, 53(10), 1183-1190. https://doi.org/10.1080/10473289.2003.10466276

Chelani, A. B., Gajghate, D. and Hasan, M. (2002). Prediction of ambient PM10 and toxic metals using artificial neural networks. Journal of the Air & Waste Management Association, 52(7), 805-810. https://doi.org/10.1080/10473289.2002.10470827

Copeland, M. (2016). What’s the Difference between Artificial Intelligence, Machine Learning, and Deep Learning? https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligencemachine-learning-deep-learning-ai/, retrieval date: 24.04.2018.

Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018

Cortina–Januchs, M. G., Quintanilla–Dominguez, J., Vega–Corona, A. and Andina, D. (2015). Development of a model for forecasting of PM10 concentrations in Salamanca, Mexico. Atmospheric Pollution Research, 6(4), 626-634. https://doi.org/10.5094/APR.2015.071

Djemai, S., Brahmi, B. and Bibi, M. O. (2016). A primal–dual method for SVM training. Neurocomputing, 211, 34-40.

Feng, Y., Zhang, W., Sun, D. and Zhang, L. (2011). Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification. Atmospheric Environment, 45(11), 1979-1985. https://doi.org/10.1016/j.atmosenv.2011.01.022

Gardner, M. and Dorling, S. (2000). Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmospheric Environment, 34(1), 21-34. https://doi.org/10.1016/S1352-2310(99)00359-3

Ghadi, M. E., Qaderi, F. and Babanezhad, E. (2019). Prediction of mortality resulted from NO 2 concentration in Tehran by Air Q+ software and artificial neural network. International Journal of Environmental Science and Technology, 16(3), 1351-1368. https://doi.org/10.1007/s13762-018-1818-4

Graupe, D. (2007). Principles of Artificial Neural Networks: World Scientific. https://doi.org/10.1142/8868

Grivas, G. and Chaloulakou, A. (2006). Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment, 40(7), 1216-1229. https://doi.org/10.1016/j.atmosenv.2005.10.036

Haykin, S. (1998). Neural Networks: A Comprehensive Foundation (2nd Edition): Prentice Hall. https://dl.acm.org/doi/book/10.5555/1213811

Hiregoudar S. (2020, August 5). Ways to Evaluate Regression Models. Website at: https://towardsdatascience.com/ways-to-evaluate-regression-models-77a3ff45ba70

Jenkin, M. E. and Clemitshaw, K. C. (2000). Ozone and other secondary photochemical pollutants: chemical processes governing their formation in the planetary boundary layer. Atmospheric Environment, 34(16), 2499-2527. https://doi.org/10.1016/S1352-2310 (99)00478-1

Kröse, B., Krose, B., van der Smagt, P. and Smagt, P. (1993). An introduction to neural networks. http://citeseerx.ist.psu.edu/viewdoc/similar?doi=10.1.1.18.493&type=cc

Kumar, A. and Goyal, P. (2011). Forecasting of air quality in Delhi using principal component regression technique. Atmospheric Pollution Research, 2(4), 436-444. https://doi.org/10.5094/APR.2011.050

Leong, W., Kelani, R. and Ahmad, Z. (2020). Prediction of air pollution index (API) using support vector machine (SVM). Journal of Environmental Chemical Engineering, 8(3), 103208. https://doi.org/10.1016/j.jece.2019.103208

Lu, W.-Z., Wang, W.-J., Wang, X.-K., Yan, S.-H. and Lam, J. C. (2004). Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong. Environmental Research, 96(1), 79-87. https://doi.org/10.1016/j.envres.2003.11.003

McKendry, I. G. (2002). Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2. 5) forecasting. Journal of the Air & Waste Management Association, 52(9), 1096-1101. https://doi.org/10.1080/10473289.2002.10470836

Mishra, D. and Goyal, P. (2015). Development of artificial intelligence based NO2 forecasting models at Taj Mahal, Agra. Atmospheric Pollution Research, 6(1), 99-106. https://doi.org/10.5094/APR.2015.012

Molina-Gómez, N., Díaz-Arévalo, J. and López-Jiménez, P. A. (2020). Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology, 1-18. https://doi.org/10.1007/s13762-020-02896-6

Nejadkoorki, F. and Baroutian, S. (2012). Forecasting extreme PM10 concentrations using artificial neural networks. https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=370188

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