Prediction of Carbon Monoxide Concentration with Variation of Support Vector Regression Kernel Parameter Value

Halawa Ernwati; Yazid Bindar; Acep Purqon; Wahyu Srigutomo

doi:10.5614/j.math.fund.sci.2022.54.1.3

Authors

Halawa Ernwati Physics of Earth and Complex System, Physics Department, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jl. Ganesa 10 Bandung 40132, Indonesia
Yazid Bindar Department of Chemical Engineering, Faculty of Industrial Technology, Institut Teknologi Bandung, Jl. Ganesa 10, Bandung 40132, Indonesia
Acep Purqon Physics of Earth and Complex System, Physics Department, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jl. Ganesa 10 Bandung 40132, Indonesia
Wahyu Srigutomo Physics of Earth and Complex System, Physics Department, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jl. Ganesa 10 Bandung 40132, Indonesia

DOI:

https://doi.org/10.5614/j.math.fund.sci.2022.54.1.3

Keywords:

air pollution, carbon monoxide, kernel, prediction, support vector regression

Abstract

Human and industrial activities produce air pollutants that can cause a decline in air quality. In urban areas, transportation activities are the main source of air pollution. One of the emitted air pollutants produced by transportation is carbon monoxide (CO). The understanding of CO concentration is crucial since its overabundance beyond a certain limit will have a negative impact on human health and the environment. In this study, the support vector regression (SVR) method was used to predict CO concentration. The purpose of this study was to predict the hourly CO concentration in the Ujung Berung district, Bandung City, West Java, Indonesia with optimal prediction accuracy. An experiment was carried out by modeling the CO concentration with varying kernel parameter values to obtain accurate prediction results. The suitability of the values between error (?), a trade-off constant (C), and variation mismatch (?) is vital to obtain optimal prediction results. The results showed that the best prediction accuracy value was 97.68% with kernel parameter values ? = 0.02, ? = 30, and C = 0.006. These results may lead to proper decision making on environmental issues and can improve air pollution control strategies.

References

Karagulian, F., Belis, C.A., Dora, C.F.C., Prs-Ust, A.M., Bonjour, S., Adair-Rohani, H. & Amann, M., Contributions to Cities Ambient Particulate Matter (PM): A Systematic Review of Local Source Contributions at Global Level, Atmospheric Environment, 120, pp. 475-483, 2015.

European Environment Agency, Explaining Road Transport Emissions Non-Technical Guide, 2016.

Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M. & Pereira, M.C., Multiple Linear Regression and Artificial Neural Networks Based on Principal Components to Predict Ozone Concentrations, Environ. Modelling & Software, 22, pp. 97-103, 2007.

Sousa, S.I.V., Pires, J.C.M., Martins, F.G., Pereira, M.C. & Alvim-Ferraz, M.C.M., Potentialities of Quantile Regression to Predict Ozone Concentrations, Environmetrics, 20(2), pp. 147-158, 2009.

Za?uska M. & G?adyszewska-Fiedoruk K., Regression Model of PM2.5 Concentration in a Single-Family House, Sustainability, 12, p. 5952, 2020.

Markakis, K., Valari, M., Perrussel, O., Sanchez, O. & Honore, C., Climate Forced Air-Quality Modeling at Urban Scale: Sensitivity to Model Resolution, Emissions and Meteorology, Atmos. Chem. Phys., 15, pp. 4767-4821, 2015.

Mehrani, M.J., Filvantorkaman, M. & Markade, F.A., Using Box Model to Estimate the Impact of Air Pollution from an Industrial Plant, MOJ Civil Engineering, 2(4), pp. 124?127, 2017.

Rafati, L., Ehrampousha, M.H., Talebib, A., Mokhtaria, M., Pisheha, Z. K. & Dehghana, H. R., Modelling the Formation of Ozone in the Air by Using Adaptive Neuro-Fuzzy Inference System (ANFIS) (Case Study: City of Yazd, Iran), Desert, 19(2), pp. 131-135, 2014.

Elangasinghe, M.A., Singhal, N., Dirks, K.N. & Salmond, J.A., Development of an ANN-Based Air Pollution Forecasting System with Explicit Knowledge Through Sensitivity Analysis, Atmospheric Pollution Research, 5, pp. 696-708, 2014.

Halawa, E., Zaharo, A., Fitrasari, D. & Purqon, A., Prediction of Carbon Monoxide Concentration Using Artificial Neural Network Method (ANN), Seminar Kontribusi Fisika ITB, pp. 140-144, 2015.

Shahrainy, H.T., Shahsavani, D., Sargazi, S. & Habibi-Nokhanda, M., Evaluation of MARS for the Spatial Distribution Modeling of Carbon Monoxide in An Urban Area, Atmospheric Pollution Research, 6, pp. 581-588, 2015.

Nunnari, G., Dorling, S., Schlink, U., Cawley, G., Foxall, R. & Chatterton, T., Modelling SO2 Concentration at a Point with Statistical Approaches, Environmental Modelling & Software, 19(10), pp. 887-905, 2004.

Moazami, S., Noori, R., Amiri, B. J., Yeganeh, B., Partani, S., & Safavi, S., Reliable Prediction of Carbon Monoxide Using Developed Support Vector Machine, Atmospheric Pollution Research, 7, pp. 412-418, 2016.

Smola, A. J., & Scholkopf, B., A Tutorial on Support Vector Regression, Statistics and Computing, 14, pp. 199-222, 2004.

Lu, W.-Z. & Wang, W-J., Potential Assessment of the Support Vector Machine Method in Forecasting Ambient Air Pollutant Trends, Chemosphere, 59(5), pp. 693-701, 2005.

Osowski, S. & Garanty, K., Forecasting of the Daily Meteorological Pollution Using Wavelets and Support Vector Machine, Engineering Applications of Artificial Intelligence, 20(6), pp. 745-755, 2007.

Wang, W., Men, C. & Lu, W., Online Prediction Model Based on Support Vector Machine, Neurocomputing, 71(4-6), pp. 550-558, 2008.

Luna, A.S., Paredes, M.L.L., de Oliveira, G.C.G. & Corr, S.M., Prediction of Ozone Concentration in Tropospheric Levels Using Artificial Neural Networks and Support Vector Machine at Rio De Janeiro, Brazil, Atmospheric Environment, 98, pp. 98-104, 2014.

Leong, W.C., Kelani, R.O. & Ahmad, Z., Prediction of Air Pollution Index (API) Using Support Vector Machine (SVM), Journal of Environmental Chemical Engineering, 2019.

Ortiz-Garc, E.G., Salcedo-Sanz, S., Perez-Bellido, A.M., Portilla-Figueras, J.A., & Prieto, L., Prediction of Hourly O3 Concentrations Using Support Vector Regression Algorithms, Atmospheric Environment, 44, pp. 4481-4488, 2010.

Castelli, M., Clemente, F.M., Popovi?, A. & Silva, S., A Machine Learning Approach to Predict Air Quality in California, Complexity, 2020.

Akbarzadeh, A., Vesali Naseh, M. R. & Nodefarahani, M., Carbon Monoxide Prediction in the Atmosphere of Tehran Using Developed Support Vector Machine, Pollution, 6(1), pp. 43-57, 2020.

Brereton, R.G. & Lloyd, G.R., Support Vector Machines for Classification and Regression, The Analyst: Tutorial Review, 2009.

Environmental Management Agency of Bandung City, Bandung City Environmental Management Agency Strategic Plan 2013-2018, 2018.

Basak, D., Pal, S., & Patranabis, D.C., Support Vector

Regression, Neural Information Processing ? Letter and Reviews. 11(10), pp. 203-224, Sept. 2019.

Li, X., Lord, D., Zhang, Y. & Xie, Y., Predicting Motor Vehicle Crashes Using Support Vector Machine Models, Accident Analysis and Prevention, 40(4), pp. 1611-1618, 2008.

Noori, R., Abdoli, M.A., Ghasrodashti, A.A. & Jalili Ghazizade, M. Prediction of Municipal Solid Waste Generation with Combination of Support Vector Machine and Principal Component Analysis: A Case Study of Mashhad, Environmental Progress & Sustainable Energy, 28(2), pp. 249-258, 2009.

Bank Central Asia Tbk Stock Price Today ? BBCA Live Ticker, Investing. com, https://id.investing.com/equities/bnk-central-as, (November 26th, 2021).

Central Statistics Agency of Bandung City, Ujung Berung District in Numbers 2020, 2020.