Temperature Prediction at Street Scale During a Heat Wave Using Random Forest
The rising frequency of heatwaves, combined with the urban heat island effect, increases the population’s exposure to high temperatures, significantly impacting the health of vulnerable groups and the overall well-being of residents. While mesoscale meteorological models can reliably forecast temper...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/7/877 |
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| author | Panagiotis Gkirmpas George Tsegas Denise Boehnke Christos Vlachokostas Nicolas Moussiopoulos |
| author_facet | Panagiotis Gkirmpas George Tsegas Denise Boehnke Christos Vlachokostas Nicolas Moussiopoulos |
| author_sort | Panagiotis Gkirmpas |
| collection | DOAJ |
| description | The rising frequency of heatwaves, combined with the urban heat island effect, increases the population’s exposure to high temperatures, significantly impacting the health of vulnerable groups and the overall well-being of residents. While mesoscale meteorological models can reliably forecast temperatures across urban neighbourhoods, dense networks of in situ measurements offer more precise data at the street scale. In this work, the Random Forest technique was used to predict street-scale temperatures in the downtown area of Thessaloniki, Greece, during a prolonged heatwave in July 2021. The model was trained using data from a low-cost sensor network, meteorological fields calculated by the mesoscale model MEMO, and micro-environmental spatial features. The results show that, although the MEMO temperature predictions achieve high accuracy during nighttime compared to measurements, they exhibit inconsistent trends across sensor locations during daytime, indicating that the model does not fully account for microclimatic phenomena. Additionally, by using only the observed temperature as the target of the Random Forest model, higher accuracy is achieved, but spatial features are not represented in the predictions. In contrast, the most reliable approach to incorporating spatial characteristics is to use the difference between observed and mesoscale temperatures as the target variable. |
| format | Article |
| id | doaj-art-4e5d56fdb3694bda9f5eb37b618dd11e |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-4e5d56fdb3694bda9f5eb37b618dd11e2025-08-20T03:58:26ZengMDPI AGAtmosphere2073-44332025-07-0116787710.3390/atmos16070877Temperature Prediction at Street Scale During a Heat Wave Using Random ForestPanagiotis Gkirmpas0George Tsegas1Denise Boehnke2Christos Vlachokostas3Nicolas Moussiopoulos4Sustainability Engineering Laboratory, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, GreeceSustainability Engineering Laboratory, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, GreeceInstitute for Geography and Geoecology, Karlsruhe Institute of Technology, 76131 Karlsruhe, GermanySustainability Engineering Laboratory, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, GreeceMain Campus, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, GreeceThe rising frequency of heatwaves, combined with the urban heat island effect, increases the population’s exposure to high temperatures, significantly impacting the health of vulnerable groups and the overall well-being of residents. While mesoscale meteorological models can reliably forecast temperatures across urban neighbourhoods, dense networks of in situ measurements offer more precise data at the street scale. In this work, the Random Forest technique was used to predict street-scale temperatures in the downtown area of Thessaloniki, Greece, during a prolonged heatwave in July 2021. The model was trained using data from a low-cost sensor network, meteorological fields calculated by the mesoscale model MEMO, and micro-environmental spatial features. The results show that, although the MEMO temperature predictions achieve high accuracy during nighttime compared to measurements, they exhibit inconsistent trends across sensor locations during daytime, indicating that the model does not fully account for microclimatic phenomena. Additionally, by using only the observed temperature as the target of the Random Forest model, higher accuracy is achieved, but spatial features are not represented in the predictions. In contrast, the most reliable approach to incorporating spatial characteristics is to use the difference between observed and mesoscale temperatures as the target variable.https://www.mdpi.com/2073-4433/16/7/877low-cost sensorsurban heat islandNWP downscalingrandom forest regression |
| spellingShingle | Panagiotis Gkirmpas George Tsegas Denise Boehnke Christos Vlachokostas Nicolas Moussiopoulos Temperature Prediction at Street Scale During a Heat Wave Using Random Forest Atmosphere low-cost sensors urban heat island NWP downscaling random forest regression |
| title | Temperature Prediction at Street Scale During a Heat Wave Using Random Forest |
| title_full | Temperature Prediction at Street Scale During a Heat Wave Using Random Forest |
| title_fullStr | Temperature Prediction at Street Scale During a Heat Wave Using Random Forest |
| title_full_unstemmed | Temperature Prediction at Street Scale During a Heat Wave Using Random Forest |
| title_short | Temperature Prediction at Street Scale During a Heat Wave Using Random Forest |
| title_sort | temperature prediction at street scale during a heat wave using random forest |
| topic | low-cost sensors urban heat island NWP downscaling random forest regression |
| url | https://www.mdpi.com/2073-4433/16/7/877 |
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