Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 79-87 |
Journal / Publication | Environmental Research |
Volume | 96 |
Issue number | 1 |
Publication status | Published - Sept 2004 |
Link(s)
Abstract
The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods. © 2003 Elsevier Inc. All rights reserved.
Research Area(s)
- Air pollution, ANN, Artificial neural networks, AQO, Air quality objective, BP, Back propagation, CO, Carbon monoxide, CPU, Central processing unit, Pollution trend, Principal component analysis, Radial basis function, Respirable suspended particulate
Citation Format(s)
Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong. / Lu, Wei-Zhen; Wang, Wen-Jian; Wang, Xie-Kang et al.
In: Environmental Research, Vol. 96, No. 1, 09.2004, p. 79-87.
In: Environmental Research, Vol. 96, No. 1, 09.2004, p. 79-87.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review