Clustering-based spatial transfer learning for short-term ozone forecasting

Journal of Hazardous Materials Advances, vol. 8, pp. 100168

Abstract

Ground-level ozone is a critical atmospheric pollutant, and high concentrations of ozone can damage human health, affect plant growth and cause ecological harm. Traditional chemical transport models and popular machine learning models have difficulty in predicting ozone concentrations, especially in times with high concentrations. We proposes a clustering-based spatial transfer learning Multilayer Perceptron (SPTL-MLP) to predict ozone concentration at the target observation station for the next three days. We use k-means clustering algorithm to find similar stations and train them together to get a base model for spatial transfer learning. For practical applications, a weighted loss function has been designed with an extra emphasis on reducing prediction errors of high ozone concentrations. Evaluation using historical data of stations in Germany shows that our SPTL-MLP model has a smaller error (reduced by 9.13%) and higher prediction accuracies of ozone exceedances (improved by 8.21% and 16.9%) compared to MLP (without spatial transfer). The results demonstrate the effectiveness of the SPTL-MLP in the short-term ozone forecast. It can be used for timely warning of ozone exceedances and help governments to detect air quality.

Authors 4

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. Jianbing Jin

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China

4. 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