Prediction of Jakarta's Air Quality Using a Stacking Framework of CLSTM, CatBoost, SVR, and XGBoost

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

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Prediction of Jakarta Air Quality, PM 10, Stacking, Extreme Gradient Boosting (XGBoost)
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	26 Nov 2024 04:45
Last Modified:	26 Nov 2024 04:45
URI:	http://eprints.intimal.edu.my/id/eprint/2045

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