Generalized Linear Model for Estimation of Missing Daily Rainfall Data

The analysis of rainfall data with no missingness is vital in various applications including climatological, hydrological and meteorological study. The issue of missing data is a serious concern since it could introduce bias and lead to misleading conclusions. In this study, five imputation methods including simple arithmetic average, normal ratio method, inverse distance weighting method, correlation coefficient weighting method and geographical coordinate were used to estimate the missing data. However, these imputation methods ignored the seasonality in rainfall dataset which could give more reliable estimation. Thus this study is aimed to estimate the missingness in daily rainfall data by using generalized linear model with gamma and Fourier series as the link function and smoothing technique, respectively. Forty years daily rainfall data for the period from 1975 until 2014 which consists of seven stations at Kelantan region were selected for the analysis. The findings indicated that the imputation methods could provide more accurate estimation values based on the least mean absolute error, root mean squared error and coefficient of variation root mean squared error when seasonality in the dataset are considered.