Prediction of Daily Air Pollutants Concentration and Air Pollutant Index Using Machine Learning Approach

Abstract

The major air pollutants in Malaysia that contribute to air pollution are carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter. Predicting the air pollutants concentration can help the government to monitor air quality and provide awareness to the public. Therefore, this study aims to overcome the problem by predicting the air pollutants concentration for the next day. This study focuses on an industrial, the Petaling Jaya monitoring station in Selangor. The data is obtained from the Department of Environment, which contains the dataset from 2004 to 2018. Subsequently, this study is conducted to construct predictive modeling that can predict the air pollutants concentrations for the next day using a tree-based approach. From the comparison of the three models, a random forest is a best-proposed model. The results of PM10 concentration prediction for the random forest is the best performance which is shown by RMSE (15.7611–19.0153), NAE (0.6508–0.8216), and R2 (0.346–0.5911). For SO2, the RMSE was 0.0016–0.0017, the NAE was 0.7056–0.8052, and the R2 was 0.3219–0.4676. The RMSE (0.0062–0.0075), the NAE (0.7892–0.9591), and the R2 (0.0814–0.3609) for NO2. The RMSE (0.3438–0.3975), NAE (0.7387–0.9015), and R2 (0.2005–0.4399) for CO were all within acceptable limits. For O3, the RMSE was 0.0051–0.0057, the NAE was 0.8386–0.9263, and the R2 was 0.1379–0.2953. The API calculation results indicate that PM10 is a significant pollutant in representing the API.

Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence

“…Integrating two model algorithms with decision trees and a supervised method is used to aggregate the final output prediction. The GB uses the loss function for the converging output to minimize the loss using less complex decision trees [47]. Gradient boosting iteratively adds weak learners to the ensemble, each weak learner attempting to outperform the prior weak learners.…”