ISSN:
1433-3058
Schlagwort(e):
Keywords:Air pollution; ARX models; Bayesian learning; Multi-layer Perceptrons
Quelle:
Springer Online Journal Archives 1860-2000
Thema:
Informatik
,
Mathematik
Notizen:
In this paper we present the preliminary results on the use of neural networks to forecast SO 2 concentration levels in the industrial area of Ravenna. Ground level concentrations of pollutants were analysed in the area of Ravenna, in particular the high levels of SO2 occurring during relatively rare episodes. These events are typically correlated with many different aspects, like complex local meteorology, topography, and industrial emissions parameters. In many cases, during these episodes, the deterministic models (e.g. Gaussian models) fail to explain the high ground level concentrations. The neural networks are trained with a Bayesian learning scheme.
Materialart:
Digitale Medien
URL:
http://dx.doi.org/10.1007/s005210070020
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