System identification: theory for the user
System identification: theory for the user
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Forecasting of the daily meteorological pollution using wavelets and support vector machine
Engineering Applications of Artificial Intelligence
Environmental Modelling & Software
Hourly ozone prediction for a 24-h horizon using neural networks
Environmental Modelling & Software
Statistical models to assess the health effects and to forecast ground-level ozone
Environmental Modelling & Software
PM10 forecasting for Thessaloniki, Greece
Environmental Modelling & Software
An alternative solution to the model structure selection problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A hybrid neural network and ARIMA model for water quality time series prediction
Engineering Applications of Artificial Intelligence
WSEAS Transactions on Systems and Control
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Air pollution has a negative impact on human health. For this reason, it is important to correctly forecast over-threshold events to give timely warnings to the population. Nonlinear models of the nonlinear autoregressive with exogenous variable (NARX) class have been extensively used to forecast air pollution time series, mainly using artificial neural networks (NNs) to model the nonlinearities. This work discusses the possible advantages of using polynomial NARX instead, in combination with suitable model structure selection methods. Furthermore, a suitably weighted mean square error (MSE) (one-step-ahead prediction) cost function is used in the identification/learning process to enhance the model performance in peak estimation, which is the final purpose of this application. The proposed approach is applied to ground-level ozone concentration time series. An extended simulation analysis is provided to compare the two classes of models on a selected case study (Milan metropolitan area) and to investigate the effect of different weighting functions in the identification performance index. Results show that polynomial NARX are able to correctly reconstruct ozone concentrations, with performances similar to NN-based NARX models, but providing additional information, as, e.g., the best set of regressors to describe the studied phenomena. The simulation analysis also demonstrates the potential benefits of using the weighted cost function, especially in increasing the reliability in peak estimation.