The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and NO2 concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data.


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    Title :

    Predicting Real-Time Roadside CO and $\hbox{NO}_{2}$ Concentrations Using Neural Networks


    Contributors:
    Zito, P. (author) / Haibo Chen, (author) / Bell, M.C. (author)


    Publication date :

    2008-09-01


    Size :

    1225025 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English