A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta
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 respir...
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Elsevier
2025-06-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000500 |
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| author | Nurtiti Sunusi Ankaz As Sikib Sumanta Pasari |
| author_facet | Nurtiti Sunusi Ankaz As Sikib Sumanta Pasari |
| author_sort | Nurtiti Sunusi |
| collection | DOAJ |
| description | 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. • The CLARA-FTSMC hybrid method demonstrates high effectiveness in analyzing large datasets, addressing the limitations of previous hybrid clustering fuzzy time series methods. • The number of fuzzy time series partitions is optimally determined based on clustering results obtained through the gap statistic approach, ensuring robust partitioning. • The forecasting accuracy of the CLARA-FTSMC hybrid method, evaluated using MAE and RMSE, showed excellent performance in predicting daily air pollution levels of SO₂ and CO in Jakarta. |
| format | Article |
| id | doaj-art-147e0abb7b0b4629ba770b83130a7874 |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-147e0abb7b0b4629ba770b83130a78742025-02-09T05:00:31ZengElsevierMethodsX2215-01612025-06-0114103202A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in JakartaNurtiti Sunusi0Ankaz As Sikib1Sumanta Pasari2Stochastic Modeling Research Group, Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Jl. Perintis Kemerdekaan Km. 10, Makassar 90245, Indonesia; Corresponding author.Stochastic Modeling Research Group, Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Jl. Perintis Kemerdekaan Km. 10, Makassar 90245, IndonesiaDepartment of Mathematics, Birla Institute of Technology and Science, Pilani, IndiaAir 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. • The CLARA-FTSMC hybrid method demonstrates high effectiveness in analyzing large datasets, addressing the limitations of previous hybrid clustering fuzzy time series methods. • The number of fuzzy time series partitions is optimally determined based on clustering results obtained through the gap statistic approach, ensuring robust partitioning. • The forecasting accuracy of the CLARA-FTSMC hybrid method, evaluated using MAE and RMSE, showed excellent performance in predicting daily air pollution levels of SO₂ and CO in Jakarta.http://www.sciencedirect.com/science/article/pii/S2215016125000500Hybrid Clustering Large Applications and Fuzzy Time Series Markov Chain |
| spellingShingle | Nurtiti Sunusi Ankaz As Sikib Sumanta Pasari A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta MethodsX Hybrid Clustering Large Applications and Fuzzy Time Series Markov Chain |
| title | A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta |
| title_full | A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta |
| title_fullStr | A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta |
| title_full_unstemmed | A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta |
| title_short | A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta |
| title_sort | novel hybrid clara and fuzzy time series markov chain model for predicting air pollution in jakarta |
| topic | Hybrid Clustering Large Applications and Fuzzy Time Series Markov Chain |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125000500 |
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