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

• 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
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-017-1468-y

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.