Exploration of transfer learning techniques for the prediction of PM10
Abstract Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such dynamics, including the prediction of air pollution concentrations, yet demanding e...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-86550-6 |
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| author | Michael Poelzl Roman Kern Simonas Kecorius Mario Lovrić |
| author_facet | Michael Poelzl Roman Kern Simonas Kecorius Mario Lovrić |
| author_sort | Michael Poelzl |
| collection | DOAJ |
| description | Abstract Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such dynamics, including the prediction of air pollution concentrations, yet demanding extensive training data. To address this, techniques like transfer learning (TL) leverage knowledge from a model trained on a rich dataset to enhance one trained on a sparse dataset, provided there are similarities in data distribution. In our experimental setup, we utilize meteorological and pollutant data from multiple governmental air quality measurement stations in Graz, Austria, supplemented by data from one station in Zagreb, Croatia to simulate data scarcity. Common ML models such as Random Forests, Multilayer Perceptrons, Long-Short-Term Memory, and Convolutional Neural Networks are explored to predict particulate matter in both cities. Our detailed analysis of PM10 suggests that similarities between the cities and the meteorological features exist and can be further exploited. Hence, TL appears to offer a viable approach to enhance PM10 predictions for the Zagreb station, despite the challenges posed by data scarcity. Our results demonstrate the feasibility of different TL techniques to improve particulate matter prediction on transferring a ML model trained from all stations of Graz and transferred to Zagreb. Through our investigation, we discovered that selectively choosing time spans based on seasonal patterns not only aids in reducing the amount of data needed for successful TL but also significantly improves prediction performance. Specifically, training a Random Forest model using data from all measurement stations in Graz and transferring it with only 20% of the labelled data from Zagreb resulted in a 22% enhancement compared to directly testing the trained model on Zagreb. |
| format | Article |
| id | doaj-art-453ba8c440264817ad8cac5c06b73f2f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-453ba8c440264817ad8cac5c06b73f2f2025-01-26T12:25:43ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-86550-6Exploration of transfer learning techniques for the prediction of PM10Michael Poelzl0Roman Kern1Simonas Kecorius2Mario Lovrić3Institute of Interactive Systems and Data Science, Graz University of TechnologyInstitute of Interactive Systems and Data Science, Graz University of TechnologyInstitute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental HealthInstitute for Anthropological ResearchAbstract Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such dynamics, including the prediction of air pollution concentrations, yet demanding extensive training data. To address this, techniques like transfer learning (TL) leverage knowledge from a model trained on a rich dataset to enhance one trained on a sparse dataset, provided there are similarities in data distribution. In our experimental setup, we utilize meteorological and pollutant data from multiple governmental air quality measurement stations in Graz, Austria, supplemented by data from one station in Zagreb, Croatia to simulate data scarcity. Common ML models such as Random Forests, Multilayer Perceptrons, Long-Short-Term Memory, and Convolutional Neural Networks are explored to predict particulate matter in both cities. Our detailed analysis of PM10 suggests that similarities between the cities and the meteorological features exist and can be further exploited. Hence, TL appears to offer a viable approach to enhance PM10 predictions for the Zagreb station, despite the challenges posed by data scarcity. Our results demonstrate the feasibility of different TL techniques to improve particulate matter prediction on transferring a ML model trained from all stations of Graz and transferred to Zagreb. Through our investigation, we discovered that selectively choosing time spans based on seasonal patterns not only aids in reducing the amount of data needed for successful TL but also significantly improves prediction performance. Specifically, training a Random Forest model using data from all measurement stations in Graz and transferring it with only 20% of the labelled data from Zagreb resulted in a 22% enhancement compared to directly testing the trained model on Zagreb.https://doi.org/10.1038/s41598-025-86550-6 |
| spellingShingle | Michael Poelzl Roman Kern Simonas Kecorius Mario Lovrić Exploration of transfer learning techniques for the prediction of PM10 Scientific Reports |
| title | Exploration of transfer learning techniques for the prediction of PM10 |
| title_full | Exploration of transfer learning techniques for the prediction of PM10 |
| title_fullStr | Exploration of transfer learning techniques for the prediction of PM10 |
| title_full_unstemmed | Exploration of transfer learning techniques for the prediction of PM10 |
| title_short | Exploration of transfer learning techniques for the prediction of PM10 |
| title_sort | exploration of transfer learning techniques for the prediction of pm10 |
| url | https://doi.org/10.1038/s41598-025-86550-6 |
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