Roles of single interpolation and statistical distributions on application in air quality data sets: An overview

Abstract

Since missing data in environmental data sets usually happen and ignorable the missing data and discard those incomplete cases in data sets as approach. However, this is not suitable for time-series prediction especially for air quality data sets. From the literatures study, has a valid method to predict the missing data in air pollutant data sets, its interpolation method. Interpolation as a well known problem in numerical method and it has been used in difference approaches in environmental data sets. Air pollutant concentrations are essentially random variables and can be well described by statistical distribution models. The statistical distribution models useful tools in predicting the distribution of air pollutant concentration. In other hand, through usage statistical distributions also able to estimate missing value in air quality concentration data sets.