Ozone exceedance forecasting with enhanced extreme instance augmentation: A case study in Germany

Environmental Modelling & Software, vol. 181, pp. 106162

Abstract

Accurately forecasting ozone levels that exceed specific thresholds is pivotal for mitigating adverse effects on both the environment and public health. However, predicting such ozone exceedances remains challenging due to the infrequent occurrence of high-concentration ozone data. This research, leveraging data from 57 German monitoring stations from 1999 to 2018, introduces an Enhanced Extreme Instance Augmentation Random Forest (EEIA-RF) approach that significantly improves the prediction of days when the maximum daily 8-hour average ozone concentrations exceed 120μg/m3. A pre-trained machine learning model is used to generate additional high-concentration data, which, combined with selectively reduced low-concentration data, forms a new dataset for training a refined model. This method achieved an improvement of at least 8% in the accuracy of predicting days with ozone exceedances across Germany. Our experiment underscores the approach’s value in enhancing atmospheric modeling and supporting public health advisories and environmental policy-making related to ozone pollution.

Authors 5

1. Tuo Deng corresponding

   Delft University of Technology

   Affiliation as printed

   Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands

2. Astrid Manders

   Netherlands Organisation for Applied Scientific Research

   Affiliation as printed

   TNO, Department of Climate, Air and Sustainability, Utrecht, The Netherlands

3. Arjo J. Segers

   Netherlands Organisation for Applied Scientific Research

   Affiliation as printed

   TNO, Department of Climate, Air and Sustainability, Utrecht, The Netherlands

4. Arnold Willem Heemink

   Delft University of Technology

   Affiliation as printed

   Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands

5. Hai Xiang Lin Aachen Institute of Environmental Sciences (CML)

   Leiden University · Delft University of Technology

   Affiliation as printed

   Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands

   Institute of Environmental Sciences, Leiden University, The Netherlands