AbstractThis study evaluates the potential of nonlinear time series analysis based methods in predicting the carbon monoxide concentration in an urban area. To establish the functional relationship between current and future observations, two models based on local approximations and neural network approximations are used. To compare the performance of the models, an autoregressive integrated moving average model is also applied. The multi-step forecasting capabilities of the models are evaluated.


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    Title :

    Prediction of ambient carbon monoxide concentration using nonlinear time series analysis technique


    Contributors:
    Chelani, A.B. (author) / Devotta, S. (author)


    Publication date :

    2007-01-01


    Size :

    5 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English