A New Back-propagation Algorithm for Modelling Air Quality Time Series
نویسندگان
چکیده
In this paper a new Back-propagation algorithm appropriately studied for modelling air pollution time series is proposed. The underlying idea is that of modifying the error definition in order to improve the capability of the model to forecast episodes of poor air quality. In the paper five different expressions of error definition are proposed and their performances are rigorously evaluated in the framework of a real case study which refer to the modelling of 1 hour average daily maximum Ozone concentration recorded in the industrial area of Melilli (Siracusa, Italy). Results indicate that despite the traditional and the proposed version of Back-propagation performs quite similarly in terms of Success Index which gives a cumulative evaluation of the model, this latter algorithm performs better in terms of the percentage of exceedences correctly forecast. Copyright c ©2005 IFAC
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