Use of neural networks for tropospheric ozone time series approximation and forecasting – a review
Item
Title (Dublin Core)
Use of neural networks for tropospheric ozone time series approximation and forecasting – a review
Description (Dublin Core)
The use of artificial neural networks in atmospheric science expands constantly. During the last years, many papers were published dealing with air pollution modeling. A number of papers deals with the time series approximation and forecasting of tropospheric ozone concentration. Neural networks have been found to outperform other statistical techniques like multiple regression etc. This paper reviews and discusses some practical aspects of the proposed neural network models applied to ozone concentration approximation and forecasting.
Creator (Dublin Core)
Argiriou, A. A.
Date (Dublin Core)
2018-08-09
Type (Dublin Core)
Text
Format (Dublin Core)
application/pdf
Identifier (Dublin Core)
10.5194/acpd-7-5739-2007
https://acp.copernicus.org/preprints/acpd-2007-0030/
Source (Dublin Core)
eISSN: 1680-7324
Language (Dublin Core)
eng
