Prediction of Ground Level Ozone by applying Artificial Neural Network nearby School Area in South Seberang Perai, Penang and Parit Buntar, Perak, Malaysia

Abstract

Ground level ozone gives intensively concern of its
adverse effects towards human health and environmental.
Increasing numbers of vehicles which also known to be one of the
sources of its precursors that emitted from vehicle exhausts leads
to the production of ground level ozone. Meteorological
conditions are also affecting the production of this pollutant.
Feedforward Backpropagation models were developed by using
artificial neural network to predict ozone concentrations nearby
selected schools area by considering the relationship with its
precursors and meteorological parameters. From the results, the
best hidden nodes found for all FFBP models for SSH, SSN, SST
and STR were 17, 12, 15 and 14, respectively. SSN found to has
the best FFBP models with the highest accuracy measures of IA,
PA and R2 which are 0.9832, 0.9686 and 0.8674, respectively if
compared to other schools. SSN also recorded the lowest error
for RMSE with the value of 3.3327. However, STR found to has
the lowest error of NAE with the value of 0.1004. Nevertheless,
the value of NAE for SSN can still be considered as lower
amongst all schools.