Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

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

doi:10.3390/ijerph18031333

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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)

Ahmad R. Alsaber

^1,*,†

Jiazhu Pan

and

Adeeba Al-Hurban

Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XH, UK

Department of Earth and Environmental Sciences, Faculty of Science, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait

Author to whom correspondence should be addressed.

^†

Current address: Livingstone Tower (Level 9), 26 Richmond Street, Glasgow G1 1XH, UK.

Int. J. Environ. Res. Public Health 2021, 18(3), 1333; https://doi.org/10.3390/ijerph18031333

Received: 5 December 2020 / Revised: 26 January 2021 / Accepted: 27 January 2021 / Published: 2 February 2021

(This article belongs to the Collection Environment and Applied Ecology)

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Abstract

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

N O_{2}

(18.4%),

C O

(18.5%),

P M_{10}

(57.4%),

S O_{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 values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data. View Full-Text

Keywords: missing imputation; random forest; high dimensional data; missing data mechanism; air quality

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Alsaber, A.R.; Pan, J.; Al-Hurban , A. Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018). Int. J. Environ. Res. Public Health 2021, 18, 1333. https://doi.org/10.3390/ijerph18031333

AMA Style

Alsaber AR, Pan J, Al-Hurban A. Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018). International Journal of Environmental Research and Public Health. 2021; 18(3):1333. https://doi.org/10.3390/ijerph18031333

Chicago/Turabian Style

Alsaber, Ahmad R., Jiazhu Pan, and Adeeba Al-Hurban . 2021. "Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)" International Journal of Environmental Research and Public Health 18, no. 3: 1333. https://doi.org/10.3390/ijerph18031333

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