A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta

Abstract

Air pollution poses a significant challenge to public health and the global environment. The Industrial Revolution, advancing technology and society, led to elevated air pollution levels, contributing to acid rain, smog, ozone depletion, and global warming. Poor air quality increases risks of respiratory inflammation, tuberculosis, asthma, chronic obstructive pulmonary disease (COPD), pneumoconiosis, and lung cancer. In this context, developing reliable air pollution forecasting models is imperative for guiding effective mitigation strategies and policy interventions. This study presents a daily air pollution prediction model focusing on Jakarta's sulfur dioxide (SO₂) and carbon monoxide (CO) levels, leveraging a hybrid methodology that integrates Clustering Large Applications (CLARA) with the Fuzzy Time Series Markov Chain (FTSMC) approach. The analysis revealed five distinct clusters, with medoid selection refined iteratively to ensure stabilization. A 5 × 5 Markov transition probability matrix was subsequently constructed for modeling the data. Predicted values for SO₂ and CO in Jakarta using the CLARA-FTSMC hybrid method showed strong alignment with the actual data. Forecasting accuracy results for SO₂ and CO in Jakarta, based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), showed excellent performance, underscoring the efficacy of the CLARA-FTSMC hybrid approach in predicting air pollution levels.