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Imputation methods on daily PM10 data (2010-15)

Abd Rani, Nurul Latiffah and Azid, Azman and Yunus, Kamaruzzaman (2019) Imputation methods on daily PM10 data (2010-15). Bioscience Research, 16 (S1). pp. 306-310. ISSN 1811-9506 E-ISSN 2218-3973

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Air pollution monitoring especially PM10 pollutant is very important since the air pollutant data originated from the continuous ambient air quality stations (CAAQS) usually had missing data due to the machine failure, routine maintenance and human error. In view of this fact, a study of PM10 imputation method was performed with the objective to determine the coefficient of determination (R2) and root mean square error (RMSE) in order to portray the goodness of fit for all of the imputation methods used (mean substitution, nearest neighbour and expectation maximization based algorithm (EMB)). The results of R2 obtained for 5%, 10%, 15%, 25% and 40% proportion of missing data using nearest neighbor imputation methods are 0.9318, 0.8126, 0.6546, 0.5458 and 0.3946, while RMSE are 7.47, 12.27, 16.68, 19.13 and 21.76, respectively. Meanwhile, results of R2 obtained for 5%, 10%, 15%, 25% and 40% proportion of missing data using mean imputation methods are 0.9274, 0.8117, 0.6484, 0.5400 and 0.3910, while RMSE are 7.47, 12.36, 16.90, 19.13 and 22.07, respectively. In the meantime, the results of R2 for EMB imputation method applied at 5%, 10%, 15%, 25% and 40% proportion of missing data are 0.9084, 0.8468, 0.7530, 0.5791 and 0.5004, while RMSE are 8.58, 11.18, 14.20, 18.53 and 20.48, respectively. A measure of performances (R2 and RMSE) for each imputation methods decreased and increase respectively as the percentages of simulated missing data increases

Item Type: Article (Journal)
Additional Information: 5410/76209
Uncontrolled Keywords: Imputation methods; Mean; Nearest neighbor; Expectation-Maximization Based Algorithm (EMB), PM10
Subjects: Q Science > QD Chemistry
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Science
Depositing User: Professor Dr. Kamaruzzaman Yunus
Date Deposited: 16 Dec 2019 17:08
Last Modified: 09 Jan 2020 12:58
URI: http://irep.iium.edu.my/id/eprint/76209

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