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

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 (ɛ), a trade-off constant (C), and variation mismatch (γ) is vital to obtain optimal prediction results. The results showed that the best prediction accuracy value was 97.68% with kernel parameter values ɛ = 0.02, γ = 30, and C = 0.006. These results may lead to proper decision making on environmental issues and can improve air pollution control strategies.