Evaluation of machine learning models for estimating pm2.5 concentrations across Malaysia

• Main Authors: Kamarul Zaman, N. A. F., Kanniah, K. D., Kaskaoutis, D. G., Latif, M. T.
• Format: Article
• Language: English
• Published: MDPI AG 2021
• Subjects: TH Building construction
• Online Access: http://eprints.utm.my/id/eprint/95447/1/NurulAmalinFatihah2021_EvaluationofMachineLearningModels.pdf; http://eprints.utm.my/id/eprint/95447/; http://dx.doi.org/10.3390/app11167326
• Institution: Universiti Teknologi Malaysia

Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression...