Publication:
Evaluation of spatial interpolation methods and spatiotemporal modeling of rainfall distribution in Peninsular Malaysia

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dc.citedby6
dc.contributor.authorFung K.F.en_US
dc.contributor.authorChew K.S.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorTeo F.Y.en_US
dc.contributor.authorNg J.L.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57199218602en_US
dc.contributor.authorid57261663400en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid35249518400en_US
dc.contributor.authorid57192698412en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:38:10Z
dc.date.available2023-05-29T09:38:10Z
dc.date.issued2022
dc.descriptionAtmospheric thermodynamics; Interpolation; Inverse problems; Rain; Scales (weighing instruments); Geographical weighted regressions; Interpolation method; Inverse distance weighting; Rainfall distribution; Spatial interpolation; Spatial interpolation method; Spatio-temporal models; Spatiotemporal analysis; Climate changeen_US
dc.description.abstractSpatial interpolation is important for visualizing changes of parameters over space. Interpolation methods for the spatiotemporal analysis of rainfall pattern changes of Peninsular Malaysia due to climate change were evaluated. Inverse Distance Weighting (IDW) and Ordinary Kriging (OK), Geographical Weighted Regression (GWR) and Multi-scale Geographical Weighted Regression (MGWR) methods were used. Based on the statistic results of RMSE, MAE and R2, the MGWR was the best performing model. To investigate the spatial interpolations with the MGWR, the period considered was arbitrarily and conveniently divided into six 5-year sub-periods for spatiotemporal analysis. Monthly Rainfall, Number of Wet Days and Maximum Daily Rainfall were parameters identified for the analyses. The results showed that the Peninsular Malaysia generally receives relatively higher rainfall amounts and intensities over the northern part of the East Coast region, while lower values were commonly received at the Central region. Changes in spatial pattern were also observed in the maps generated for the onset and withdrawal months of both the Northeast Monsoons and the Southwest Monsoons. The sub-period analyses also showed that Peninsular Malaysia has gradual increase of rainfall intensity due to climate change and is susceptible and vulnerable to El Nino/La Nina events. Hence, in the future Peninsular Malaysia is likely to have increasing occurrence of extreme rainfall events. � 2021 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo101571
dc.identifier.doi10.1016/j.asej.2021.09.001
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85115112968
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85115112968&doi=10.1016%2fj.asej.2021.09.001&partnerID=40&md5=f27c992763be3fb1da21430aeca3a9ce
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26961
dc.identifier.volume13
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.titleEvaluation of spatial interpolation methods and spatiotemporal modeling of rainfall distribution in Peninsular Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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