Comparative analysis of machine learning techniques for so2 prediction modelling

Abstract

Abstract Sulphur dioxide (SO2) is produced both naturally and by human activity. The primary natural resource is derived from volcanoes. The burning of fossil fuels is the primary anthropogenic source (especially coal and diesel). Therefore, a reliable and accurate predicting method is essential for an early warning system for SO2 atmospheric concentration. There are still limited studies in Malaysia that use machine learning methods to predict SO2 concentrations. With the aid of machine learning, this study seeks to develop and predict future SO2 concentrations for the next day using the maximum daily data from Klang, Selangor. RapidMiner Studio is the data mining tool used for this research work. Based on the results, it showed that the SVM model was the best guide to be used compared with the other five models (GLM, DL, DT, GBT, and RF). The performance indicators showed that the SVM model was adequate for the next day’s prediction (R2 = 0.77, SE = 8.26, REL = 18.69%, AE = 1.46, and RMSE = 2.82). The developed model in this research can be used by Malaysian authorities as a public health protection measure to give Malaysians an early warning about the problem of air pollution. The goal of predictive modelling is to make a reasonable prediction of the variable of interest, and frequently, to determine how much the independent variable contributed to the dependent variable. The results also showed that the previous SO2 concentrations were one of the most influential parameters used to predict the future SO2 concentrations.