Selection of the data time interval for the prediction of maximum ozone concentrations

This paper highlights the problem of step-length selection for the one-step-ahead prediction of ozone called the data time interval. This is done using a case study-based comparison of two approaches for predicting the maximum daily values of tropospheric ozone. The first approach is the 1-day-ahead...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2018-06, Vol.32 (6), p.1759-1770
Main Authors: Kocijan, Juš, Gradišar, Dejan, Stepančič, Martin, Božnar, Marija Zlata, Grašič, Boštjan, Mlakar, Primož
Format: Article
Language:English
Subjects:
Aquatic Pollution
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Earth and Environmental Science
Earth Sciences
Environment
Gaussian process
Iterative methods
Math. Appl. in Environmental Science
Mathematical models
Original Paper
Ozone
Physics
Predictions
Probability Theory and Stochastic Processes
Statistics for Engineering
Waste Water Technology
Water Management
Water Pollution Control
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Summary:This paper highlights the problem of step-length selection for the one-step-ahead prediction of ozone called the data time interval. This is done using a case study-based comparison of two approaches for predicting the maximum daily values of tropospheric ozone. The first approach is the 1-day-ahead prediction and the second is the prediction of the maximum values based on a multi-step-ahead iteration of 1-h predictions. Gaussian process modelling is utilised for this comparison. In particular, evolving Gaussian-process models are used that update on-line with the incoming measurement data. These sorts of models have been successfully used in the past for the prediction of ozone pollution. This paper contributes an assessment of the way that the maximum ozone values are predicted. A comparison of the daily maximum ozone values forecasted by a model based on 1-day-ahead predictions with those obtained by iterated 1-h-ahead predictions of the ozone with predictions at predetermined hours of the day is given. The forecast results are in favour of the on-line model based on hourly predictions when approaching closer to the real maximum values of ozone, and in favour of the daily predictions when they are made on a daily basis.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-017-1468-y