Determination of significant variables to particulate matter (PM10) variations in northern region, Malaysia during haze episodes (2006-2015)

  • K.M.K. Ku Yusof
  • A Azid
  • M.A. Jamalani
Keywords: haze studies, sensitivity analysis, artificial neural network, principal component regression

Abstract

The most substantial air pollutant variables during haze episode in Northern region for 10-consecutive years (2006-2015) were analyzed and highlighted. ANN together with SAPCR were integrated to identify the variables contributed to fluctuation of particulate matter (PM10) during haze period. 13 variables including air pollutant and meteorological factor were included as explorable variables. The humidity, wind speed and ozone were recognized as determinant to PM10 variation during haze from 2006-2015. Three artificial neural models were created based on all parameters, leave-out method and PCR-factor loading. The best model will be selected based on a few criterions like determination of coefficient, R2, root-mean-square-error and squared sum of all errors. ANN-HM-LO was a better model than ANN-HM-PCR in overall prediction performance with R2 result for ANN-HM-LO was 0.839, whilst ANN-HM-PCR was just 0.801.

Keywords: haze studies; sensitivity analysis; artificial neural network; principal component regression
Published
2018-03-16

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eISSN: 1112-9867