Application of Different Statistical Methods to Recover Missing Rainfall Data in the Klang River Catchment

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Year:
2015
Type of Publication:
Article
Keywords:
Data Mining, Knowledge Discovery From Data, Klang River, Interpolation, SPSS
Authors:
Mehrdad Habibi Khalifeloo; Munira Mohammad; Mohammad Heydari
Journal:
IJISM
Volume:
3
Number:
4
Pages:
206-210
Month:
July
Abstract:
Most of hydrological studies are based on statistical science. The first step in water project engineering studies, agricultural development plans and such like is to use of correct data and information. However, because of various reasons, there are several gaps in available data. In this study, for finding missing value (545 records) in our data, we used the daily data from five pluviometry stations in the Klang river basin between 2005 to 2015. Statistical methods used in this study were the linear interpolation, linear regression, trendline command in Excel software and EM method in SPSS. The results obtained from this research showed that the best method for singular missing data is a linear interpolation method and the fastest way to fill the missing gaps was the use of the SPSS software. Also, if the aim is to find the relationship between variables and determine the priorities, the regression method is recommended.

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