Sahar Hadi PourAhmad Khairi Abd WahabShamsuddin Shahid2024-05-202024-05-202020Pour, S. H., Abd Wahab, A. K., & Shahid, S. (2020). Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia. Atmospheric Research, 233, 104720.https://repoemc.ukm.my/handle/123456789/1364Reliable prediction of rainfall extremes is vital for disaster management, particularly in the context of increasing rainfall extremes due to global climate change. Physical-empirical models have been developed in this study using three widely used Machine Learning (ML) methods namely, Support Vector Machines (SVM), Random Forests (RF), Bayesian Artificial Neural Networks (BANN) for the prediction of rainfall and rainfall related extremes during Northeast Monsoon (NEM) in Peninsular Malaysia from synoptic predictors.enExtreme rainfallClimate forecastingPhysical-empirical modelMachine learning algorithmRecursive feature eliminationJournal