Abstract
AbstractOver-threshold event forecasting is of paramount importance in the monitoring of environmental variables, such as those related to air pollution. This paper explores the use of nonlinear polynomial NARX models for the prediction of ozone concentration data, using specific cost functions and identification algorithms devised to enhance the prediction accuracy at the peak values of the signal, in order to improve the over-threshold event detection. Some preliminary results of the experimental data analysis carried out on observed time histories are illustrated to show the effectiveness of the presented methodology.
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