Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm

Nowadays, 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 p...

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Main Authors:	Rodi, N.S.Noor, Malek, M.A, Ismail, Amelia Ritahani
Format:	Article
Language:	English English
Published:	Science Publishing Corporation Publisher 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/70093/ http://irep.iium.edu.my/70093/ http://irep.iium.edu.my/70093/ http://irep.iium.edu.my/70093/7/70093%20Monthly%20Rainfall%20Prediction%20Model.pdf http://irep.iium.edu.my/70093/8/70093%20Monthly%20Rainfall%20Prediction%20Model%20SCOPUS.pdf

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recordtype	eprints
spelling	iium-700932019-07-12T08:39:08Z http://irep.iium.edu.my/70093/ Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm Rodi, N.S.Noor Malek, M.A Ismail, Amelia Ritahani QA75 Electronic computers. Computer science Nowadays, 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 colle cted data includes rainfall and other four (4) meteorological parameters from year 1988 to 2017 at four selected meteorological stations. The parameters usedinthisanalysisarehumidity,windspeed,temperatureandpressureatmonthlyinterval. Four(4)meteorologicalstationsinvolved 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. Science Publishing Corporation Publisher 2018 Article PeerReviewed application/pdf en http://irep.iium.edu.my/70093/7/70093%20Monthly%20Rainfall%20Prediction%20Model.pdf application/pdf en http://irep.iium.edu.my/70093/8/70093%20Monthly%20Rainfall%20Prediction%20Model%20SCOPUS.pdf Rodi, N.S.Noor and Malek, M.A and Ismail, Amelia Ritahani (2018) Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm. International Journal of Engineering & Technology, 7 (4.35 Special issue 35). pp. 182-185. ISSN 2227-524X https://www.sciencepubco.com/index.php/ijet/article/view/22358/10961 10.14419/ijet.v7i4.35.22358
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Rodi, N.S.Noor Malek, M.A Ismail, Amelia Ritahani Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm
description	Nowadays, 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 colle cted data includes rainfall and other four (4) meteorological parameters from year 1988 to 2017 at four selected meteorological stations. The parameters usedinthisanalysisarehumidity,windspeed,temperatureandpressureatmonthlyinterval. Four(4)meteorologicalstationsinvolved 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.
format	Article
author	Rodi, N.S.Noor Malek, M.A Ismail, Amelia Ritahani
author_facet	Rodi, N.S.Noor Malek, M.A Ismail, Amelia Ritahani
author_sort	Rodi, N.S.Noor
title	Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm
title_short	Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm
title_full	Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm
title_fullStr	Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm
title_full_unstemmed	Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm
title_sort	monthly rainfall prediction model of peninsular malaysia using clonal selection algorithm
publisher	Science Publishing Corporation Publisher
publishDate	2018
url	http://irep.iium.edu.my/70093/ http://irep.iium.edu.my/70093/ http://irep.iium.edu.my/70093/ http://irep.iium.edu.my/70093/7/70093%20Monthly%20Rainfall%20Prediction%20Model.pdf http://irep.iium.edu.my/70093/8/70093%20Monthly%20Rainfall%20Prediction%20Model%20SCOPUS.pdf
first_indexed	2023-09-18T21:39:31Z
last_indexed	2023-09-18T21:39:31Z
_version_	1777413045583085568

Monthly rainfall prediction model of Peninsular Malaysia using clonal selection algorithm

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