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Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018) by Ahmad R. Alsaber; Jiazhu Pan; Adeeba Al-Hurban is a Medicine article available to read on EtoBox.

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In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO 2 (18.4%), CO (18.5%), PM 10 (57.4%), SO 2 (19.0%), and O 3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing val

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Author

Ahmad R. Alsaber; Jiazhu Pan; Adeeba Al-Hurban

Publisher

MDPI AG

Published

2021

Language

EN

Field

Medicine (Health Sciences)