A metamodel for background ozone level using radial basis function neural networks

Publication Type:
Conference Proceeding
11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 958 - 963
Issue Date:
Filename Description Size
Thumbnail2009008714OK.pdf377.62 kB
Adobe PDF
Full metadata record
In air quality modelling, determination of the background ozone level is essential as it highly affects the accuracy of the photochemical air quality model. It is known that the background ozone level, especially in urban areas, has been changing over the years. Unfortunately, the reasons of that alteration were not clear and the background ozone itself was not easily derived in practice. In this paper, a new background ozone model will be developed by using the ozone ambient quality data and the meteorological data at the several stations in the Sydney basin. To accomplish the modelling process, an adaptively-tuned radial basis function neural network metamodel is proposed and utilised in the simulation. Different input parameters are considered to evaluate their influence on the constructed background ozone model. The proposed model, subject to some statistical criteria, demonstrates its capability of estimating the background ozone level with a reasonably good accuracy. ©2010 IEEE.
Please use this identifier to cite or link to this item: