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Prediction of Rainfall Trends using Mahalanobis-Taguchi System

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dc.citedby0
dc.contributor.authorJamil M.A.M.en_US
dc.contributor.authorAbu M.Y.en_US
dc.contributor.authorZaini S.N.A.M.en_US
dc.contributor.authorAris N.H.en_US
dc.contributor.authorPinueh N.S.en_US
dc.contributor.authorJaafar N.N.en_US
dc.contributor.authorMuhammad W.Z.A.W.en_US
dc.contributor.authorRamlie F.en_US
dc.contributor.authorHarudin N.en_US
dc.contributor.authorSari E.en_US
dc.contributor.authorGhani N.A.A.A.en_US
dc.contributor.authorid59182931100en_US
dc.contributor.authorid55983627200en_US
dc.contributor.authorid57196441481en_US
dc.contributor.authorid58832351700en_US
dc.contributor.authorid58830536300en_US
dc.contributor.authorid56083925700en_US
dc.contributor.authorid55860800560en_US
dc.contributor.authorid55982859700en_US
dc.contributor.authorid56319654100en_US
dc.contributor.authorid55983050300en_US
dc.contributor.authorid57214749865en_US
dc.date.accessioned2025-03-03T07:43:13Z
dc.date.available2025-03-03T07:43:13Z
dc.date.issued2024
dc.description.abstractFull comprehension of precipitation patterns is crucially needed, especially in Pekan, a district in Pahang, Malaysia. The area is renowned for its elevated levels of precipitation, making it imperative to precisely categorize and enhance the analysis of rainfall patterns to facilitate effective resource allocation, agricultural productivity, and catastrophe readiness. The variability of rainfall patterns is contingent upon geographical location, necessitating the collection of a comprehensive data set that includes several characteristics that influence precipitation to make reliable predictions. Data were collected from the Vantage Pro2 weather station, which is located on the UMP Pekan campus. This study used the RT method to classify rainfall and T-Method 1 to determine the degree of contribution of each parameter. Significant parameters were validated using a data set from the same type of weather station but in a different district. The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. Finally, the validation with T mean-based error (Tmbe) using Mean Absolute Error (MAE) revealed a pattern of errors to provide insight to find the significant parameters of MTS. ? 2024 Published by IRCS-ITB.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.5614/j.eng.technol.sci.2024.56.2.9
dc.identifier.epage303
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85196657123
dc.identifier.spage287
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85196657123&doi=10.5614%2fj.eng.technol.sci.2024.56.2.9&partnerID=40&md5=bb7a819259e7d9d35bd2d342eacb440a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36583
dc.identifier.volume56
dc.pagecount16
dc.publisherInstitute for Research and Community Services, Institut Teknologi Bandungen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleJournal of Engineering and Technological Sciences
dc.subjectErrors
dc.subjectWeather information services
dc.subjectData set
dc.subjectElevated level
dc.subjectMahalanobis distances
dc.subjectMahalanobis-taguchi systems
dc.subjectMalaysia
dc.subjectOptimisations
dc.subjectPrecipitation patterns
dc.subjectRainfall patterns
dc.subjectRainfall trends
dc.subjectWeather stations
dc.subjectRain
dc.titlePrediction of Rainfall Trends using Mahalanobis-Taguchi Systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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