Prediction of respirable suspended particulate level in Hong Kong downtown area using principal component analysis and artificial neural networks
Author(s)
Author(s)
Lu, W. Z.
Fan, H. Y.
Wang, W. J.
Lo, S. M.
Date Issued
2002
Publisher
IEEE
Related Publication(s)
Proceedings of the 4th World Congress on Intelligent Control and Automation (Volume 1)
Start page
49
End page
53
Abstract
Modeling of the pollutant concentrations is 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 technique is used to reduce and orthogonalize input variables of the neural network model, which is established for forecasting the pollutant concentrations in downtown area of Hong Kong. The new approach is demonstrated and validated with two practical cases of predicting the respirable suspended particulate levels in the central area of Hong Kong. The simulation results show that the proposed method is feasible and efficient.
SFU Affiliated Publication
No
Availability at SFU Library
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