Single and Multiple Imputation Method to Replace Missing Values in Air Pollution Datasets: A Review

Abstract

Abstract Imputation plays an essential role in handling the issue of missing data. The conventional techniques applied to overcome this problem are single imputation (SI) and multiple imputations (MI). These statistical strategies have their strengths and limitations in replacing missing data. This article reviews the state of the art of imputation methods employed in general publications in replacing missing values for air pollution data. A comprehensive review of the literature identifies the use of SI and MI slightly increases over the year. This paper concludes on the trend and the approaches used in the imputation methods. Subsequently, this paper put forward the gaps in imputation technique that less utilized a machine-learning approach in providing a substitute for missing values in air pollution data. The future direction of the research is to extend more machine-learning approach with higher accuracy with higher performance in imputing missing values.