Application of imputation methods for missing values of PM۱۰ and O۳ data: Interpolation, moving average and K-nearest neighbor methods

سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 223

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شناسه ملی سند علمی:

JR_EHEM-8-3_007

تاریخ نمایه سازی: 6 مهر 1400

چکیده مقاله:

Background: PIn air quality studies, it is very often to have missing data due to reasons such as machine failure or human error. The approach used in dealing with such missing data can affect the results of the analysis. The main aim of this study was to review the types of missing mechanism, imputation methods, application of some of them in imputation of missing of PM۱۰ and O۳ in Tabriz, and compare their efficiency. Methods: Methods of mean, EM algorithm, regression, classification and regression tree, predictive mean matching (PMM), interpolation, moving average, and K-nearest neighbor (KNN) were used. PMM was investigated by considering the spatial and temporal dependencies in the model. Missing data were randomly simulated with ۱۰, ۲۰, and ۳۰% missing values. The efficiency of methods was compared using coefficient of determination (R۲), mean absolute error (MAE) and root mean square error (RMSE). Results: Based on the results for all indicators, interpolation, moving average, and KNN had the best performance, respectively. PMM did not perform well with and without spatio-temporal information. Conclusion: Given that the nature of pollution data always depends on next and previous information, methods that their computational nature is based on before and after information indicated better performance than others, so in the case of pollutant data, it is recommended to use these methods.

نویسندگان

Parisa Saeipourdizaj

Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran

Parvin Sarbakhsh

Corresponding author: Health and Environment Research Center, Tabriz University of Medical Sciences, Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran

Akbar Gholampour

Health and Environment Research Center, Tabriz University of Medical Sciences, Department of Environmental Health Engineering, School of Public Health, Tabriz University of Medical Sciences, Tabriz, Iran

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  • Kamarehie B, Ghaderpoori M, Jafari A, Karami M, Mohammadi A, ...
  • Kim KH, Kabir E, Kabir S. A review on the ...
  • Atkinson RW, Fuller GW, Anderson HR, Harrison RM, Armstrong B. ...
  • Kushkbaghi S, Ehrampoush MH, Mirhosseinidehabadi SA. Assessment of role of ...
  • Chock DP, Winkler SL, Chen C. A study of the ...
  • Anderson JO, Thundiyil JG, Stolbach A. Clearing the air: a ...
  • Amann M, Derwent D, Forsberg B, Hanninen O, Hurley F, ...
  • Keshtgar L, Shahsavani S, Maghsoudi A, Anushiravani A, Zaravar F, ...
  • Cadelis G, Tourres R, Molinie J. Short-term effects of the ...
  • Shah AS, Langrish JP, Nair H, McAllister DA, Hunter AL, ...
  • Imtiaz SA, Shah SL. Treatment of missing values in process ...
  • Norazian MN, Shukri YA, Azam RN, Al Bakri AM. Estimation ...
  • Hawthorne G, Hawthorne G, Elliott P. Imputing cross-sectional missing data: ...
  • Chatfield C. ۱۹. Statistical Analysis with Missing Data. Journal of ...
  • MSC EPIDEMIOLOGY. Modern methods of data analysis. [cited ۲۰۲۱ Jun] ...
  • Hamzah FB, Hamzah FM, Razali SF, Jaafar O, Abdul Jamil ...
  • Alsaber AR, Pan J, Al-Hurban A. Handling complex missing data ...
  • Gill MK, Asefa T, Kaheil Y, McKee M. Effect of ...
  • Moritz S, Bartz-Beielstein T. imputeTS: time series missing valueImputation in ...
  • Ishak AB, Daoud MB, Trabelsi A. Ozone concentration forecasting using ...
  • Junger WL, De Leon AP. Imputation of missing data in ...
  • Valdiviezo HC, Van Aelst S. Tree-based prediction on incomplete data ...
  • Kotsiantis S, Kostoulas A, Lykoudis S, Argiriou A, Menagias K. ...
  • Allen RJ, DeGaetano AT. Estimating missing daily temperature extremes using ...
  • McLachlan GJ, Peel D. Finite Mixture Models. USA: John Wiley ...
  • Rubin DB. Multiple Imputation for Nonresponse in Surveys. USA: John ...
  • Schafer JL. Analysis of Incomplete Multivariate Data. USA: CRC Press; ...
  • Junninen H, Niska H, Tuppurainen K, Ruuskanen J, Kolehmainen M. ...
  • Gelman A, Hill J. Data Analysis Using Regression and Multilevel/Hierarchical ...
  • Engels JM, Diehr P. Imputation of missing longitudinal data: a ...
  • Marwala T. Computational Intelligence for Missing Data Imputation, Estimation, and ...
  • Allison PD. Missing Data. USA: SAGE Publications; ۲۰۰۲. doi: ۱۰.۴۱۳۵/۹۷۸۱۴۱۲۹۸۵۰۷۹ ...
  • Donders AR, van der Heijden GJ, Stijnen T, Moons KG. ...
  • Chapra SC, Canale RP. Numerical Methods for Engineers. ۶th ed. ...
  • Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete ...
  • Lepkowski JM, Raghunathan TE, Solenberger P, Van Hoewyk J. A ...
  • Burgette LF, Reiter JP. Multiple imputation for missing data via ...
  • Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and ...
  • Hastie T, Tibshirani R, Friedman J. The Elements of Statistical ...
  • Gold MS, Bentler PM. Treatments of missing data: A Monte ...
  • Scheffer J. Dealing with missing data. Research Letters in the ...
  • Landerman LR, Land KC, Pieper CF. An empirical evaluation of ...
  • Morris TP, White IR, Royston P. Tuning multiple imputation by ...
  • Vink G, Frank LE, Pannekoek J, van Buuren S. Predictive ...
  • Yuan YC. Multiple imputation for missing data: Concepts and new ...
  • van Buuren S, Groothuis-Oudshoorn K, Vink G, Schouten R, Robitzsch ...
  • van Buuren S. Flexible Imputation of Missing Data. ۲nd ed. ...
  • Widaman KF. Missing data: What to do with or without ...
  • Dong Y, Peng CY. Principled missing data method for researchers. ...
  • Willmott CJ. On the Evaluation of Model Performance in Physical ...
  • Legates DR, McCabe Jr GJ. Evaluating the use of goodness ...
  • Nash JE, Sutcliffe JV. River flow forecasting through conceptual models ...
  • Schneider T. Analysis of incomplete climate data: Estimation of Mean ...
  • Plaia A, Bondì AL. Single imputation method of missing values ...
  • Kondrashov D, Ghil M. Spatio-temporal filling of missing points in ...
  • Marlinda AM. Rainfall data in-filling model with expectation maximization and ...
  • Pollice A, Lasinio GJ. Two approaches to imputation and adjustment ...
  • Zainuri NA, Jemain AA, Muda N. A comparison of various ...
  • Kamaruzaman IF, Zin WZ, Ariff NM. A comparison of method ...
  • Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, ...
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