METHODOLOGICAL CONSIDERATION ON PRE-PROCESSING DATA OPTIMIZATION CONCERNING AIR DISPERSION MODEL AND NEURAL NETWORKS: A CASE-STUDY OF OZONE PREDICTION LEVEL
Item
Title (Dublin Core)
eng
METHODOLOGICAL CONSIDERATION ON PRE-PROCESSING DATA OPTIMIZATION CONCERNING AIR DISPERSION MODEL AND NEURAL NETWORKS: A CASE-STUDY OF OZONE PREDICTION LEVEL
Description (Dublin Core)
eng
This work analyzes the results of a Neural Network model applied to air pollution data. In particular, we forecast ozone
pollutants levels in a short term using both air dispersion models and neural network methods. The purpose of this work is to provide
a novel methodological procedure to analyze environmental data by using a neural net as forecast technique for ozone levels in the
urban area of Rome. Results show that the model performance can be improved by pre-processing input data using typical datamining
techniques and coupling air dispersion model with neural net.
pollutants levels in a short term using both air dispersion models and neural network methods. The purpose of this work is to provide
a novel methodological procedure to analyze environmental data by using a neural net as forecast technique for ozone levels in the
urban area of Rome. Results show that the model performance can be improved by pre-processing input data using typical datamining
techniques and coupling air dispersion model with neural net.
Creator (Dublin Core)
Pelliccioni, A.
Cotroneo, R.
Pungi, F.
Subject (Dublin Core)
eng
Ozone;neural networks;Data mining;Stepwise algorithm selection; resampling
Publisher (Dublin Core)
Croatian meteorological society
Date (Dublin Core)
2008
Type (Dublin Core)
text
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Format (Dublin Core)
application/pdf
Identifier (Dublin Core)
https://hrcak.srce.hr/64321
eng
https://hrcak.srce.hr/file/96449
Source (Dublin Core)
Hrvatski meteorološki časopis
ISSN 1330-0083 (Print)
ISSN 1849-0700 (Online)
Volume 43
Issue 43/2
Language (Dublin Core)
eng
Rights (Dublin Core)
info:eu-repo/semantics/openAccess
The papers of this Journal are free of charge for personal or educational use, with respect of copyright of authors and publisher.