Ozone prediction based on neural networks and Gaussian processes

Grašič, Bostjan and Mlakar, P. and Božnar, M. Z. (2006) Ozone prediction based on neural networks and Gaussian processes. Il nuovo cimento C, 29 (6). pp. 651-661. ISSN 1826-9885

[img]
Preview
Text
ncc9186.pdf

Download (240kB) | Preview
Official URL: https://www.sif.it/riviste/sif/ncc/econtents/2006/...

Abstract

The urban environment in Slovenia is confronted with the air pollution problem of harmfully high ozone concentrations. In the last two decades the automatic ozone measuring network was extended and now covers regions where the highest values are expected. Due to topographical and climatological conditions and the presence of extensive urban environments, the most critical locations are the ones in the western part of Slovenia. In the city of Nova Gorica a modern automatic urban air pollution measuring station was installed. Measurements at this station clearly showed that ozone is a considerable pollutant there, especially in the summer time. In this work a perceptron neural-network–based model and a Gaussian-process–based model for ozone concentration forecasting for the city of Nova Gorica was developed and evaluated. The methods of feature determination and pattern selection for the model training process are delineated. The shortcomings of the models and possibilities for improvements are discussed with respect to evaluation of the effectiveness of the methods.

Item Type: Article
Uncontrolled Keywords: Air quality and air pollution
Subjects: 300 Scienze sociali > 360 Problemi e servizi sociali; associazioni > 363 Altri problemi e servizi sociali > 363.7 Problemi ambientali (classificare qui la tutela ambientale; l’effetto dei rifiuti, dell’inquinamento, delle iniziative per controllarli) > 363.73 Inquinamento > 363.739 Inquinamento di specifici ambienti > 363.7392 Inquinamento atmosferico
Depositing User: Marina Spanti
Date Deposited: 19 Mar 2020 14:55
Last Modified: 19 Mar 2020 14:55
URI: http://eprints.bice.rm.cnr.it/id/eprint/16123

Actions (login required)

View Item View Item