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...

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Main Authors: Usman, Syapotro, Silvia, Ratna, M., Muflih, Haldi, Budiman, M. Rezqy, Noor Ridha, Muhammad, Hamdani
Format: Article
Language:English
English
Published: INTI International University 2024
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Online Access:http://eprints.intimal.edu.my/2045/1/jods2024_46.pdf
http://eprints.intimal.edu.my/2045/2/586
http://eprints.intimal.edu.my/2045/
http://ipublishing.intimal.edu.my/jods.html
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spelling my-inti-eprints.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
institution INTI International University
building INTI Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider INTI International University
content_source INTI Institutional Repository
url_provider http://eprints.intimal.edu.my
language English
English
topic GE Environmental Sciences
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
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
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.
format Article
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
title 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_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_sort prediction of jakarta's air quality using a stacking framework of clstm, catboost, svr, and xgboost
publisher INTI International University
publishDate 2024
url http://eprints.intimal.edu.my/2045/1/jods2024_46.pdf
http://eprints.intimal.edu.my/2045/2/586
http://eprints.intimal.edu.my/2045/
http://ipublishing.intimal.edu.my/jods.html
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score 13.222552