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

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.