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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)

Author

Listed:
  • Ahmad R. Alsaber

    (Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XH, UK
    Current address: Livingstone Tower (Level 9), 26 Richmond Street, Glasgow G1 1XH, UK.)

  • Jiazhu Pan

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

  • Adeeba Al-Hurban

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

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.

Suggested Citation

  • Ahmad R. Alsaber & Jiazhu Pan & 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)," IJERPH, MDPI, vol. 18(3), pages 1-25, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1333-:d:491512
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    References listed on IDEAS

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