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

• 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
• Subjects: GE Environmental Sciences; QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General)
• 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

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...