Comparison of Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) Methods for Predicting Air Quality Using Python and KNIME
Abstract views: 211
DOI:
https://doi.org/10.36456/jstat.vol16.no1.a6633
Keywords:
Support Vector Machine, ARIMA, Air qualityAbstract
Air is the most important component for living things on earth. However, the changes that exist on earth cause problems, one of which is air pollution. Human activity is one of the causes of air pollution. This is what makes future air quality feasible to predict. To predict air quality, this research use the Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) methods. For SVM itself, it presents one of the methods of machine learning techniques. Meanwhile, ARIMA presents one of the methods of the statistical model. Using data from the Open Data Jakarta website regarding measurements of the Air Pollution Standard Index (ISPU) at five air quality monitoring stations (SPKU) in DKI Jakarta Province in 2021, an analysis was then carried out to compare the performance and accuracy of these two methods in predicting air quality. The results of this study indicate that between ARIMA and SVM testing, it can be said that SVM testing has higher accuracy results. This can be seen from the average accuracy results with several treatments.
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S. Chapman, J. E. M. Watson, A. Salazar, M. Thatcher, and C. A. McAlpine, “The impact of urbanization and climate change on urban temperatures: a systematic review,” Landsc Ecol, vol. 32, no. 10, pp. 1921–1935, Oct. 2017, doi: 10.1007/s10980-017-0561-4.
L. Manisalidis, E. Stavropoulou, A. Stavropoulos, and E. Bezirtzoglou, “Environmental and Health Impacts of Air Pollution: A Review,” Front Public Health, vol. 8, Feb. 2020, doi: 10.3389/fpubh.2020.00014.
X. Yi, J. Zhang, Z. Wang, T. Li, and Y. Zheng, “Deep Distributed Fusion Network for Air Quality Prediction,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA: ACM, Jul. 2018, pp. 965–973. doi: 10.1145/3219819.3219822.
A. M. Fiore et al., “Global air quality and climate,” Chem Soc Rev, vol. 41, no. 19, p. 6663, 2012, doi: 10.1039/c2cs35095e.
T. B. Sasongko, “Komparasi dan Analisis Kinerja Model Algoritma SVM dan PSO-SVM (Studi Kasus Klasifikasi Jalur Minat SMA),” Jurnal Teknik Informatika dan Sistem Informasi, vol. 2, no. 2, Aug. 2016, doi: 10.28932/jutisi.v2i2.476.
W. Lu et al., “Air pollutant parameter forecasting using support vector machines,” in Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), IEEE, 2002, pp. 630–635. doi: 10.1109/IJCNN.2002.1005545.
M. Dun, Z. Xu, Y. Chen, and L. Wu, “Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine,” Math Probl Eng, vol. 2020, pp. 1–13, May 2020, doi: 10.1155/2020/8914501.
A. Kaya, R. Ozturk, and C. Altin Gumussoy, “Usability Measurement of Mobile Applications with System Usability Scale (SUS),” 2019, pp. 389–400. doi: 10.1007/978-3-030-03317-0_32.
Gourav, J. K. Rekhi, P. Nagrath, and R. Jain, “Forecasting Air Quality of Delhi Using ARIMA Model,” 2020, pp. 315–325. doi: 10.1007/978-981-15-0372-6_25.
Y. Zhang, H. Yang, H. Cui, and Q. Chen, “Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, China,” Natural Resources Research, vol. 29, no. 2, pp. 1447–1464, Apr. 2020, doi: 10.1007/s11053-019-09512-6.
M. Awad and R. Khanna, Efficient Learning Machines. Berkeley, CA: Apress, 2015. doi: 10.1007/978-1-4302-5990-9.
KNIME Community Hub, “KNIME Base nodes,” KNIME. https://hub.knime.com/knime/extensions/org.knime.features.base/latest (accessed Jan. 01, 2023).
