Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.engappai.2009.04.002 | Eng. Appl. of AI |
Keywords | Field | DocType |
polynomial narx,different weighting function,suitable model structure selection,peak estimation,nonlinear model,air pollution time series,cost function,model performance,forecasting peak air pollution,air pollution,nn-based narx model,weight function,time series,prediction error,artificial neural network,artificial neural networks,ozone,performance index | Autoregressive model,Population,Mathematical optimization,Weighting,Nonlinear system,Nonlinear autoregressive exogenous model,Polynomial,Computer science,Artificial intelligence,Artificial neural network,Weighted arithmetic mean,Machine learning | Journal |
Volume | Issue | ISSN |
22 | 4-5 | Engineering Applications of Artificial Intelligence |
Citations | PageRank | References |
10 | 1.36 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrico Pisoni | 1 | 39 | 6.69 |
| Marcello Farina | 2 | 335 | 36.83 |
| Claudio Carnevale | 3 | 51 | 8.60 |
| Luigi Piroddi | 4 | 311 | 25.04 |