Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

• Main Authors: Hanoon, Marwah Sattar, Ahmed, Ali Najah, Zaini, Nur'atiah, Razzaq, Arif, Kumar, Pavitra, Sherif, Mohsen, Sefelnasr, Ahmed, Ahmed El-Shafie, Ahmed Hussein Kamel
• Format: Article
• Published: Nature Portfolio 2021
• Subjects: Q Science (General); T Technology (General)
• Online Access: http://eprints.um.edu.my/33889/

Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neura...