Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities
An abundance of impervious surfacesImpervious surfaces like building roofs in densely populated cities make green roofs a suitable solution for urban heat islandUrban heat island (UHI) mitigation. Therefore, we employ random forest (RF) regression to predict the impact of green roofs on the surface UHI (SUHI) in Liege, Belgium. While there have been several studies identifying the impact of green roofs on UHIUrban heat island, fewer studies utilize a remote-sensing-based approach to measure impact on Land Surface TemperaturesLand surface temperatures (LST) that are used to estimate SUHI. Moreover, the RF algorithm, can provide useful insights. In this study, we use LSTLand surface temperatures obtained from Landsat-8 imagery and relate it to 2D and 3D morphological parameters that influence LST and UHI effects. Additionally, we utilise parameters that influence wind (e.g., frontal area index). We simulate the green roofs by assigning suitable values of normalised difference-vegetation index and built-up index to the buildings with flat roofs. Results suggest that green roofs decrease the average LSTLand surface temperatures.
Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities
An abundance of impervious surfacesImpervious surfaces like building roofs in densely populated cities make green roofs a suitable solution for urban heat islandUrban heat island (UHI) mitigation. Therefore, we employ random forest (RF) regression to predict the impact of green roofs on the surface UHI (SUHI) in Liege, Belgium. While there have been several studies identifying the impact of green roofs on UHIUrban heat island, fewer studies utilize a remote-sensing-based approach to measure impact on Land Surface TemperaturesLand surface temperatures (LST) that are used to estimate SUHI. Moreover, the RF algorithm, can provide useful insights. In this study, we use LSTLand surface temperatures obtained from Landsat-8 imagery and relate it to 2D and 3D morphological parameters that influence LST and UHI effects. Additionally, we utilise parameters that influence wind (e.g., frontal area index). We simulate the green roofs by assigning suitable values of normalised difference-vegetation index and built-up index to the buildings with flat roofs. Results suggest that green roofs decrease the average LSTLand surface temperatures.
Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities
The Urban Book Series
Goodspeed, Robert (Herausgeber:in) / Sengupta, Raja (Herausgeber:in) / Kyttä, Marketta (Herausgeber:in) / Pettit, Christopher (Herausgeber:in) / Joshi, Mitali Yeshwant (Autor:in) / Aliaga, Daniel G. (Autor:in) / Teller, Jacques (Autor:in)
International Conference on Computers in Urban Planning and Urban Management ; 2023 ; Montreal, QC, Canada
02.06.2023
19 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Urban Heat Island Effects and Mitigation Strategies in Saudi Arabian Cities
| Springer Verlag | 2020