Publication:
Monthly rainfall prediction model of Peninsular Malaysia Using Clonal Selection Algorithm

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dc.citedby3
dc.contributor.authorRodi N.S.N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorIsmail A.R.en_US
dc.contributor.authorid57205233472en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid36995749000en_US
dc.date.accessioned2023-05-29T06:54:40Z
dc.date.available2023-05-29T06:54:40Z
dc.date.issued2018
dc.description.abstractNowadays, various algorithms inspired by natural processes have been extensively applied in solving engineering problems. This study proposed Artificial Immune Systems (AIS), a computational approach inspired by the processes of human immune system, as an algorithm to predict future rainfall. This proposed algorithm is another alternative technique as compared to the commonly used Statistical, Stochastic and Artificial Neural Network techniques traditionally use in Hydrology. Rainfall prediction is pertinent in order to solve many hydrological problems. The proposed Clonal Selection Algorithm (CSA) is one of the main algorithms in AIS, which inspired on Clonal selection theory in the immune system of human body that includes selection, hyper mutation, and receptor editing processes. This study proposed algorithm is utilised to predict future monthly rainfall in Peninsular Malaysia. The collected data includes rainfall and other four (4) meteorological parameters from year 1988 to 2017 at four selected meteorological stations. The parameters used in this analysis are humidity, wind speed, temperature and pressure at monthly interval. Four (4) meteorological stations involved are Chuping (north), Subang Jaya(west), Senai (south) and Kota Bharu (west) represented peninsular Malaysia. Based on the results at testing stage, it is found that the trend and peaks of the hydrographs from generated data are approximately similar to the actual historical data. The highest similarity percentage obtained is 91%. The high values of similarity percentage obtained between simulated and actual rainfall data in this study, reinforced the hypothesis that CSA is suitable to be used for prediction of continuous time series data such as monthly rainfall data which highly variable in nature. As a conclusion, the results showed that the proposed Clonal Selection Algorithm is acceptable and stable at all stations. � 2018 Authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14419/ijet.v7i4.35.22358
dc.identifier.epage185
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85059237648
dc.identifier.spage182
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85059237648&doi=10.14419%2fijet.v7i4.35.22358&partnerID=40&md5=e50f33ac8114c0972903c1bf32501c23
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24038
dc.identifier.volume7
dc.publisherScience Publishing Corporation Incen_US
dc.relation.ispartofAll Open Access, Bronze, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Engineering and Technology(UAE)
dc.titleMonthly rainfall prediction model of Peninsular Malaysia Using Clonal Selection Algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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