Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia



The air-quality prediction is important for planning, proper actions and controlling

LOCATION OF CONTINUOUS AIR QUALITY MONITORING STATIONS IN THE SOUTHERN REGION OF PENINSULAR MALAYSIA.

strategies. Due to the concern, artificial neural network (ANN) has been applied for prediction purposes, especially on air quality.  The ANN can be used to evaluate the predictive performance, and gives a better performance comparable to other models. Unlike other techniques, ANN is capable in the recognition of non-linear patterns between the variables and complex patterns in data sets, which are not well described by simple mathematical formulae. The ANN can be trained accurately when presented with a new data set.  In this study, the rotated principal components score (RPCs) from the method of principal component analysis(PCA) were used as input variables in the API prediction model using ANN approach.(In Azman Azid‘ research, published in Journal of Environmental Protection Journal 2013 Vol. A2 by Scientific Research Publishing, View Original Post, please click here)

DETAIL DESCRIPTION OF AIR QUALITY MONITORING STATIONS OF THE STUDY AREA.

The findings from this study show that the application of rotated PCs scores based models is more efficient and effective due to reduction of predictor variables without losing important information. It has also proved that the RPCs-ANN models are absolutely very useful tools in helping decision making and problem solving for better atmospheric management of the local environment.