Estimation of Missing Rainfall Data Using GEP: Case Study of Raja River, Alor Setar, Kedah. (8th September 2014)

• Record Type: Journal Article
• Authors: Che Ghani, Nor Zaimah; Abu Hasan, Zorkeflee; Tze Liang, Lau
• Other Names: Alimi Adel M. Academic Editor.
• Is Part Of: Advances in artificial intelligence. Volume 2014(2014)
• Publication Date: 2014-09-08
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1155/2014/716398
• Languages: English
• ISSNs: 1687-7470

Abstract

Abstract : Water resources and urban flood management require hydrologic and hydraulic modeling. However, incomplete precipitation data is often the issue during hydrological modeling exercise. In this study, gene expression programming (GEP) was utilised to correlate monthly precipitation data from a principal station with its neighbouring station located in Alor Setar, Kedah, Malaysia. GEP is an extension to genetic programming (GP), and can provide simple and efficient solution. The study illustrates the applications of GEP to determine the most suitable rainfall station to replace the principal rainfall station (station 6103047). This is to ensure that a reliable rainfall station can be made if the principal station malfunctioned. These were done by comparing principal station data with each individual neighbouring station. Result of the analysis reveals that the station 38 is the most compatible to the principal station where the value of R 2 is 0.886.