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Journal of Applied Sciences
  Year: 2012 | Volume: 12 | Issue: 14 | Page No.: 1488-1494
DOI: 10.3923/jas.2012.1488.1494
 
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Performance of Multiple Linear Regression Model for Long-term PM10 Concentration Prediction Based on Gaseous and Meteorological Parameters

Ahmad Zia Ul-Saufie, Ahmad Shukri Yahaya, NorAzam Ramli and Hazrul Abdul Hamid

Abstract:
The aim of this study was to investigate performance of Multiple Linear Regression (MLR) method in predicting future (next day, next 2 days and next 3 days) PM10 concentration levels in Seberang Perai, Malaysia. The developed model was compared to multiple linear regression models. The model used gaseous (NO2, SO2, CO), PM10 and meteorological parameters (temperature, relative humidity and wind speed) as predictors. Performance indicators such as Prediction Accuracy (PA), Coefficient of Determination (R2), Index of Agreement (IA), Normalized Absolute Error (NAE) and Root Mean Square Error (RMSE) were used to measure the accuracy of the models. Performance indicator shows next day (RMSE = 11.211, NAE = 0.124, PA = 0.927, IA = 0.960, R2 = 0.858,) and next 2-day (RMSE = 14.652, NAE = 0.155, PA = 0.881, IA = 0.925, R2 = 0.775) and next 3-day (RMSE = 15.611, NAE = 0.167, PA = 0.849, IA = 0.912, R2 = 0.720). Assessment of model performance indicated that multiple linear regression method can be used for long term PM10 concentration prediction with next day for next day.
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How to cite this article:

Ahmad Zia Ul-Saufie, Ahmad Shukri Yahaya, NorAzam Ramli and Hazrul Abdul Hamid, 2012. Performance of Multiple Linear Regression Model for Long-term PM10 Concentration Prediction Based on Gaseous and Meteorological Parameters. Journal of Applied Sciences, 12: 1488-1494.

DOI: 10.3923/jas.2012.1488.1494

URL: https://scialert.net/abstract/?doi=jas.2012.1488.1494

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