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IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Air Pollution Prediction Using Machine Learning Supervised Learning Approach

[Full Text]



Madhuri VM, Samyama Gunjal GH, Savitha Kamalapurkar



Air Pollution, Decision Tree, Linear Regression, Machine Learning, Random Forest, Supervised Learning, SVM.



Due to human activities, industrialization and urbanization air is getting polluted. The major air pollutants are CO, NO, C6H6,etc. The concentration of air pollutants in ambient air is governed by the meteorological parameters such as atmospheric wind speed, wind direction, relative humidity, and temperature. Earlier techniques such as Probability, Statistics etc. were used to predict the quality of air, but those methods are very complex to predict, the Machine Learning (ML) is the better approach to predict the air quality. With the need to predict air relative humidity by considering various parameters such as CO, Tin oxide, nonmetallic hydrocarbons, Benzene, Titanium, NO, Tungsten, Indium oxide, Temperature etc, approach uses Linear Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest Method (RF) to predict the Relative humidity of air and uses Root Mean Square Error to predict the accuracy.



[1] Verma, Ishan, Rahul Ahuja, HardikMeisheri, andLipikaDey. ”Air pollutant severity rediction using Bi-directional LSTM Network.” In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 651-654. IEEE, 2018.
[2] Figures Zhang, Chao, Baoxian Liu, Junchi Yan, Jinghai Yan, Lingjun Li, Dawei Zhang, XiaoguangRui, and RongfangBie. ”Hybrid Measurement of Air Quality as a 5 Fig. 8. RH w.r.t tin oxide Fig. 9. RH w.r.t C6H6 Mobile Service: An Image Based Approach.” In 2017 IEEE International Conference on Web Services (ICWS), pp. 853- 856. IEEE,2017.
[3] Yang, Ruijun, Feng Yan, and Nan Zhao. ”Urban air quality based on Bayesian network.” In 2017 IEEE 9th Fig. 10. RH w.r.t NO Fig. 11. RH w.r.t NO2 International Conference on Communication Softwareand Networks (ICCSN), pp. 1003-1006. IEEE,2017.
[4] Ayele, TemeseganWalelign, and RutvikMehta.”Air pollution monitoring and prediction using IoT.” In 2018 Second International Conference on Inventive Communication 6 Fig. 12. RH w.r.t Temperature Fig. 13. RH w.r.t CO and Computational Technologies (ICICCT), pp. 1741-1745. IEEE,2018.
[5] Djebbri, Nadjet, and MouniraRouainia. ”Artificial neural networksbased air pollution monitoring inindustrial sites.” In 2017 International Conference on Engineering and Technology (ICET), pp. 1-5. IEEE,2017.
[6] Kumar, Dinesh. ”Evolving Differential evolution method with random forest for prediction of Air Pollution.” Procedia computer science 132 (2018): 824-833.
[7] Jiang, Ningbo, and Matthew L. Riley. ”Exploring the utility of the random forest method for forecasting ozone pollution in SYDNEY.” Journal of Environment Protection and Sustainable Development 1.5 (2015): 245-254.
[8] Svetnik, Vladimir, et al. ”Random forest: a classification and regression tool for compound classification and QSAR modeling.” Journal of chemical information and computer sciences 43.6 (2003): 1947-1958.
[9] Biau, GA˜ Srard. ”Analysis of a random forest model.” ˇJournal of Machine Learning Research 13.Apr (2012): 1063- 1095.
[10] Biau, Gerard, and ErwanScornet. ”A random forest ´ guided tour.” Test 25.2 (2016): 197-227.
[11] Grimm, Rosina, et al. ”Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using Random Forests analysis.” Geoderma 146.1- 2 (2008): 102-113.
[12] Strobl, Carolin, et al. ”Conditional variable importance for random forests.” BMC bioinformatics 9.1 (2008): 307.
[13] Svetnik, Vladimir, et al. ”Random forest: a classification and regression tool for compound classification and QSAR modeling.” Journal of chemical information and computer sciences 43.6 (2003): 1947-1958.
[14] Verikas, Antanas, AdasGelzinis, and MarijaBacauskiene. ”Mining data with random forests: A survey and results of new tests.” Pattern recognition 44.2 (2011): 330-349.
[15] Ramasamy Jayamurugan,1 B. Kumaravel,1 S. Palanivelraja,1 and M.P.Chockalingam2 International Journal of Atmospheric Sciences Volume 2013, Article ID 264046, 7 pages http://dx.doi.org/10.1155/2013/264046