Estimation of Missing Rainfall Data in Pahang using Modified Spatial Interpolation Weighting Methods

In meteorological and hydrological researches, missing rainfall data has always been one of the most challenging problems which need to be faced by the researchers. The problems of missing rainfall data are due to the wrong technique used when measuring the rainfall, relocation of the rain station...

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Bibliographic Details
Main Authors: Roslinazairimah, Zakaria, Muhammad Az-Zuhri, Azman, Noor Fadhilah, Ahmad Radi
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/6637/1/fist-2014-zuhri-Estimation_of_Missing_Rainfall.pdf
http://umpir.ump.edu.my/id/eprint/6637/
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Summary:In meteorological and hydrological researches, missing rainfall data has always been one of the most challenging problems which need to be faced by the researchers. The problems of missing rainfall data are due to the wrong technique used when measuring the rainfall, relocation of the rain station and malfunctioned of instrument. Finding the suitable method to solve missing data problem is very critical before going to the next level of data analysis. Most researchers used the spatial interpolation method to estimate the missing rainfall data at a particular target station which is based on the available rainfall data at their neighboring stations. The spatial interpolation method is one of the traditional weighting factors which also consider the correlation between the stations. This study uses the modified of spatial interpolation weighting methods to estimate the missing rainfall data in Pahang and only assume that the particular target station has the missing value. A new modified method of normal ratio and inverse distance weighting with correlation is proposed by abbreviated by NRIDC. The performance of the modified spatial interpolation weighting methods used are assessed using the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R) for different percentage of missing values (5%-30%).