Prediction of Jakarta's Air Quality Using a Stacking Framework of CLSTM, CatBoost, SVR, and XGBoost
Air quality prediction, particularly in estimating PM10 particle concentration, is a significant challenge in major cities like Jakarta, which experience high levels of air pollution. This study aims to develop an air quality prediction model using an innovative stacking framework that combines s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2045/ http://eprints.intimal.edu.my/2045/1/jods2024_46.pdf http://eprints.intimal.edu.my/2045/2/586 |
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| author | Usman, Syapotro Silvia, Ratna M., Muflih Haldi, Budiman M. Rezqy, Noor Ridha Muhammad, Hamdani |
| author_facet | Usman, Syapotro Silvia, Ratna M., Muflih Haldi, Budiman M. Rezqy, Noor Ridha Muhammad, Hamdani |
| author_sort | Usman, Syapotro |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Air quality prediction, particularly in estimating PM10 particle concentration, is a
significant challenge in major cities like Jakarta, which experience high levels of air pollution.
This study aims to develop an air quality prediction model using an innovative stacking
framework that combines several machine learning algorithms, namely ConvLSTM, CatBoost,
SVR, and XGBoost. The methodology employed in this research is an experimental approach,
where each model is trained and tested individually before being integrated into the stacking
framework. The dataset used was sourced from the Kaggle platform, containing historical air
quality data in Jakarta. Performance evaluation was conducted by measuring the Root Mean
Squared Error (RMSE) for each model. The results of the study showed that the ConvLSTM
model produced an RMSE of 13.5168, CatBoost with an RMSE of 13.4113, and SVR with an
RMSE of 14.2725. To improve prediction accuracy, the researchers employed a stacking
approach of the four models (ConvLSTM, CatBoost, SVR, and XGBoost), which yielded a
much lower RMSE of 0.8093. Thus, this stacking framework has proven to significantly
enhance air quality prediction performance, particularly in predicting PM10 concentrations in
Jakarta. |
| first_indexed | 2025-11-14T11:58:34Z |
| format | Article |
| id | intimal-2045 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:34Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20452024-11-26T04:45:53Z http://eprints.intimal.edu.my/2045/ Prediction of Jakarta's Air Quality Using a Stacking Framework of CLSTM, CatBoost, SVR, and XGBoost Usman, Syapotro Silvia, Ratna M., Muflih Haldi, Budiman M. Rezqy, Noor Ridha Muhammad, Hamdani GE Environmental Sciences QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Air quality prediction, particularly in estimating PM10 particle concentration, is a significant challenge in major cities like Jakarta, which experience high levels of air pollution. This study aims to develop an air quality prediction model using an innovative stacking framework that combines several machine learning algorithms, namely ConvLSTM, CatBoost, SVR, and XGBoost. The methodology employed in this research is an experimental approach, where each model is trained and tested individually before being integrated into the stacking framework. The dataset used was sourced from the Kaggle platform, containing historical air quality data in Jakarta. Performance evaluation was conducted by measuring the Root Mean Squared Error (RMSE) for each model. The results of the study showed that the ConvLSTM model produced an RMSE of 13.5168, CatBoost with an RMSE of 13.4113, and SVR with an RMSE of 14.2725. To improve prediction accuracy, the researchers employed a stacking approach of the four models (ConvLSTM, CatBoost, SVR, and XGBoost), which yielded a much lower RMSE of 0.8093. Thus, this stacking framework has proven to significantly enhance air quality prediction performance, particularly in predicting PM10 concentrations in Jakarta. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2045/1/jods2024_46.pdf text en cc_by_4 http://eprints.intimal.edu.my/2045/2/586 Usman, Syapotro and Silvia, Ratna and M., Muflih and Haldi, Budiman and M. Rezqy, Noor Ridha and Muhammad, Hamdani (2024) Prediction of Jakarta's Air Quality Using a Stacking Framework of CLSTM, CatBoost, SVR, and XGBoost. Journal of Data Science, 2024 (46). pp. 1-6. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | GE Environmental Sciences QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Usman, Syapotro Silvia, Ratna M., Muflih Haldi, Budiman M. Rezqy, Noor Ridha Muhammad, Hamdani Prediction of Jakarta's Air Quality Using a Stacking Framework of CLSTM, CatBoost, SVR, and XGBoost |
| title | Prediction of Jakarta's Air Quality Using a Stacking Framework of
CLSTM, CatBoost, SVR, and XGBoost |
| title_full | Prediction of Jakarta's Air Quality Using a Stacking Framework of
CLSTM, CatBoost, SVR, and XGBoost |
| title_fullStr | Prediction of Jakarta's Air Quality Using a Stacking Framework of
CLSTM, CatBoost, SVR, and XGBoost |
| title_full_unstemmed | Prediction of Jakarta's Air Quality Using a Stacking Framework of
CLSTM, CatBoost, SVR, and XGBoost |
| title_short | Prediction of Jakarta's Air Quality Using a Stacking Framework of
CLSTM, CatBoost, SVR, and XGBoost |
| title_sort | prediction of jakarta's air quality using a stacking framework of
clstm, catboost, svr, and xgboost |
| topic | GE Environmental Sciences QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://eprints.intimal.edu.my/2045/ http://eprints.intimal.edu.my/2045/ http://eprints.intimal.edu.my/2045/1/jods2024_46.pdf http://eprints.intimal.edu.my/2045/2/586 |