Pollution modelling for Hong Kong downtown area using principal component analysis and artificial neural networks

Abstract

Modeling of the pollutants' concentrations comprises an important part in the field of atmospheric environment research. Neural network modeling is regarded as a reliable and cost-effective method to achieve such prediction task. In this paper, the principal component analysis (PCA) technique is used to reduce and orthogonalize input variables of neural network (NN) model, which is established for forecasting the pollutants' concentrations in downtown area of Hong Kong. The new approach is demonstrated and validated with two practical cases of predicting the respirable suspended particulate (RSP) levels in central area in Hong Kong. The simulation results show that the proposed method is feasible and efficient.