Air Pollution Index Forecasting Model using Automated Machine Learning

Abstract

Malaysia's Air Pollution Index (API) has steadily climbed in recent decades, posing a severe environmental hazard. In this paper, authors used the Automated Machine Learning (AutoML) technique for, to analyse daily integer value time series data for API in Malaysia from January to December in 2019. The parameters of the models will be calculated using an error rated, and their model’s result will be compared to determine which model best fits the data. The results showed that the AutoML model was more accurate in predicting API values. The MSE value of the AutoML model and the RMSE were lower, indicating that the model had a lower error rate than traditional regression models. Furthermore, predicting ability will be evaluated using mean square error (MSE) and correlation coefficient as accuracy. The findings indicated that the AutoML model produced better results in MSE and better accuracy. The fundamental benefit of utilising the AutoML model is that it is readily automated, making it a valuable tool for academics and scientists. This model may also be used as a forecasting tool to enhance air quality in a certain location. Overall, the results indicate that the AutoML model is a viable and promising technique for predicting air pollution. The findings are critical for managing API outcomes in the future and adopting protective measures for air conservation.