Handling complex missing data using random forest approach for an air quality monitoring dataset : a case study of Kuwait environmental data (2012 to 2018)
| Autor: | Ahmad Alsaber, Jiazhu Pan, Adeeba Al-Hurban |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
missing imputation
Health Toxicology and Mutagenesis high dimensional data lcsh:Medicine 01 natural sciences Article Environmental data 010104 statistics & probability 03 medical and health sciences Air Pollution Statistics Imputation (statistics) 0101 mathematics QA Air quality index 030304 developmental biology Mathematics 0303 health sciences Models Statistical GE lcsh:R Temperature Public Health Environmental and Occupational Health Statistical model Missing data air quality Random forest Data set Kuwait Research Design Skewness missing data mechanism random forest |
| Zdroj: | International Journal of Environmental Research and Public Health, Vol 18, Iss 1333, p 1333 (2021) International Journal of Environmental Research and Public Health Volume 18 Issue 3 |
| ISSN: | 1660-4601 |
| Popis: | 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 NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (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. |
| Databáze: | OpenAIRE |
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