Publication:
Comparison of Rainfall Interpolation Methods in Langat River Basin

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dc.citedby5
dc.contributor.authorHassim M.en_US
dc.contributor.authorYuzir A.en_US
dc.contributor.authorRazali M.N.en_US
dc.contributor.authorRos F.C.en_US
dc.contributor.authorChow M.F.en_US
dc.contributor.authorOthman F.en_US
dc.contributor.authorid14625397700en_US
dc.contributor.authorid56703426300en_US
dc.contributor.authorid36440450000en_US
dc.contributor.authorid57222964772en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid36630785100en_US
dc.date.accessioned2023-05-29T08:08:49Z
dc.date.available2023-05-29T08:08:49Z
dc.date.issued2020
dc.descriptionDisasters; Errors; Inverse problems; Mean square error; Rain; Rain gages; Rivers; Watersheds; Disaster mitigation; Hydrology modeling; Interpolation method; Inverse distance weighting; Rainfall interpolation; Root mean square errors; Spatial interpolation; Statistical evaluation; Interpolationen_US
dc.description.abstractRainfall is an element of climate that can be measured by a rain gauge. The rain gauge was set up for every station predefined by the Department of Irrigation and Drainage (DID) Malaysia. One millimeter (mm) of rainfall means that within a square meter of a flat surface, water can be as high as one mm. In the hydrology model, the rainfall data is very important in order to predict the flood or assist in the disaster mitigation plan. In this case, the availability of complete rainfall data in a region is essential. By performing spatial interpolation, rainfall data can predict values from the empty data at each point. In this study, Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Simple Kriging (SK) and Kernel Smoothing (KS) method were considered in the rainfall interpolation for this area. Rainfall data at 20 points in Langat River Basin that obtained from DID Ampang for the period 2008-2017 were used as reference data. This study aimed to compare IDW, Kriging and Spline methods to obtain better interpolation methods. The interpolation is done by running a cross-validation using a geostatistical wizard in ArcGIS. The method effectiveness was evaluated by the calculation of mean error (ME), Root Mean Square Error (RMSE), Root mean Square Standardize Error (RMSSE) and Average Standard Error (ASE). For IDW method, only the ME and RMSE results are available. From the result, it can be seen that SK method outperforms the IDW, OK and KS method for these rainfall interpolations in Langat River Basin by showing better statistical evaluation. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12018
dc.identifier.doi10.1088/1755-1315/479/1/012018
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85089134891
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089134891&doi=10.1088%2f1755-1315%2f479%2f1%2f012018&partnerID=40&md5=f15ace00d5a03dfbf5a0fafcab13b852
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25387
dc.identifier.volume479
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.titleComparison of Rainfall Interpolation Methods in Langat River Basinen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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