A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Forecasting daily high ozone concentrations by classification trees
10.1002/env.631.abs
This article proposes the use of classification trees (CART) as a suitable technique for forecasting the daily exceedance of ozone standards established by Italian law. A model is formulated for predicting, 1 and 2 days beforehand, the most probable class of the maximum daily urban ozone concentration in the city of Bologna. The standard employed is the so‐called ‘warning level’ (180 μg/m3). Meteorological forecasted variables are considered as predictors. Pollution data show a considerable discrepancy between the dimensions of the two classes of events. The first class includes those days when the observed maximum value exceeds the established standard, while the second class contains those when the observed maximum value does not exceed the said standard. Due to this peculiarity, model selection procedures using cross‐validation usually lead to overpruning. We can overcome this drawback by means of techniques which replicate observations, through the modification of their inclusion probabilities in the cross‐validation sets. Copyright © 2004 John Wiley & Sons, Ltd.
Forecasting daily high ozone concentrations by classification trees
10.1002/env.631.abs
This article proposes the use of classification trees (CART) as a suitable technique for forecasting the daily exceedance of ozone standards established by Italian law. A model is formulated for predicting, 1 and 2 days beforehand, the most probable class of the maximum daily urban ozone concentration in the city of Bologna. The standard employed is the so‐called ‘warning level’ (180 μg/m3). Meteorological forecasted variables are considered as predictors. Pollution data show a considerable discrepancy between the dimensions of the two classes of events. The first class includes those days when the observed maximum value exceeds the established standard, while the second class contains those when the observed maximum value does not exceed the said standard. Due to this peculiarity, model selection procedures using cross‐validation usually lead to overpruning. We can overcome this drawback by means of techniques which replicate observations, through the modification of their inclusion probabilities in the cross‐validation sets. Copyright © 2004 John Wiley & Sons, Ltd.
Forecasting daily high ozone concentrations by classification trees
Bruno, F. (author) / Cocchi, D. (author) / Trivisano, C. (author)
Environmetrics ; 15 ; 141-153
2004-03-01
13 pages
Article (Journal)
Electronic Resource
English
Forecasting daily high ozone concentrations by classification trees
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