Open AccessEstimation of Missing Rainfall Data Using GEP: Case Study of Raja River, Alor Setar, Kedah
Author(s) -
Nor Zaimah Che Ghani,
Zorkeflee Abu Hasan,
Tze Liang Lau
Publication year - 2014
Publication title -
advances in artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 1687-7489
pISSN - 1687-7470
DOI - 10.1155/2014/716398
Subject(s) - gene expression programming , principal (computer security) , setar , hydrology (agriculture) , flood myth , genetic programming , computer science , water resources , environmental science , geography , engineering , ecology , time series , star model , geotechnical engineering , archaeology , machine learning , artificial intelligence , autoregressive integrated moving average , operating system , biology
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 R2 is 0.886
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