Estimation of missing rainfall data in Pahang using modified spatial interpolation weighting methods

Abstract

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%).

Cross Assessment of Twenty-One Different Methods for Missing Precipitation Data Estimation

“…The modified NR method mainly reflects the positive spatial correlation between the target station and the nearby stations. The following formula can be used to compute the missing rainfall data using the NRID method [50]: A combination of the NR method and the ID method improves the results of both methods in terms of filling in missing data [2]. The ONRID method is a combination of the modified old normal ratio method and the ID method [50].…”

“…The following formula can be used to compute the missing rainfall data using the NRID method [50]: A combination of the NR method and the ID method improves the results of both methods in terms of filling in missing data [2]. The ONRID method is a combination of the modified old normal ratio method and the ID method [50]. The missing data are estimated using the following equation [2,50]: 2015) first proposed this method, known as NRIDC, as being a combination of three methods: the NR, IDW, and CCW methods [50].…”

“…The ONRID method is a combination of the modified old normal ratio method and the ID method [50]. The missing data are estimated using the following equation [2,50]: 2015) first proposed this method, known as NRIDC, as being a combination of three methods: the NR, IDW, and CCW methods [50]. The NRIDC is considered to be the same method as the NRID, proposed by Suhaila et al (2008), with the addition of the correlation coefficient [2,50].…”

“…The missing data are estimated using the following equation [2,50]: 2015) first proposed this method, known as NRIDC, as being a combination of three methods: the NR, IDW, and CCW methods [50]. The NRIDC is considered to be the same method as the NRID, proposed by Suhaila et al (2008), with the addition of the correlation coefficient [2,50]. According to this method, the missing data can be estimated using the following formula: (17) where the power of the correlation coefficient P should be more than 4.…”

Simulation Study of Adjusted Spatial Weighting Method to Estimate Missing Rainfall Data

“…Thus, the problem of having missing value required an appropriate method or technique to handle it effectively as it reduces the statistical power of a study which can produce the biased estimates and can have a significant effect on the conclusions that can be drawn from the rainfall data(Ahmad Azman et al, 2015;Hasana & Crokea, 2013).…”