Ground-level Ozone Forecast Based On
Machine Learning
Author(s)
R. Zabkar, J. Zabkar & D. Cemas
Abstract
In this paper we apply methods of machine learning to the problem of groundlevel
ozone forecasting, using measured data and data calculated by the
numerical weather prediction model ALADIN (Aire Limitee Adaptation
Dynamique developement InterNational). Our goal is to build a simple ozoneforecasting
tool to predict the daily maximum of ground-level ozone
concentration per day using meteorological and air quality data.
Tropospheric ozone episodes in Slovenia are mainly due to the local traffic
sources and the long-range transport of ozone and its precursors, generally
originating from Western Europe.
Ozone forecast model was developed for the purpose of issuing public alerts
to avoid exposure to high ground-
Keywords
Related Book
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