Physical- emipirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia

Simple item page
dc.contributor.authorSahar Hadi Pour
dc.contributor.authorAhmad Khairi Abd Wahab
dc.contributor.authorShamsuddin Shahid
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-20T03:08:30Z
dc.date.available2024-05-20T03:08:30Z
dc.date.issued2020
dc.description.abstractReliable 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.
dc.identifier.citationPour, 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.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/1364
dc.language.isoen
dc.publisherElsevier
dc.publisher.alternativeAtmospheric Research
dc.subjectExtreme rainfall
dc.subjectClimate forecasting
dc.subjectPhysical-empirical model
dc.subjectMachine learning algorithm
dc.subjectRecursive feature elimination
dc.titlePhysical- emipirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia
dc.typeJournal

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
8_Physical-_emipirical_models_for_prediction_of_seasonal_rainfall_extremes_of_Peninsular_Malaysia.pdf
Size:
3.94 MB
Format:
Adobe Portable Document Format
Download

Collections

Hydrology