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

机译:使用改进的空间插值加权方法估算彭亨州的降雨数据丢失

摘要

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%).
机译:在气象和水文研究中,缺少降雨数据一直是研究人员必须面对的最具挑战性的问题之一。降雨数据丢失的问题是由于在测量降雨时使用了错误的技术,雨站的重新定位以及仪器故障。在进入下一级别的数据分析之前,找到解决丢失数据问题的合适方法非常关键。大多数研究人员使用空间插值方法来估计特定目标站的缺失降雨数据,该数据基于其相邻站的可用降雨数据。空间插值方法是传统的加权因子之一,也考虑了站点之间的相关性。这项研究使用改进的空间插值加权方法来估计彭亨州缺失的降雨数据,并且仅假设特定目标站具有缺失值。提出了一种新的改进的正态比和相关的距离反比加权的改进方法,简称为NRIDC。使用相似度指数(S-index),平均绝对误差(MAE)和相关系数(R)来评估不同百分比的缺失值(5%-30%)所使用的改进的空间插值加权方法的性能。

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