Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities

peer reviewed ; An abundance of impervious surfaces like building roofs in densely populated cities make green roofs a suitable solution for urban 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 UHI, fewer studies utilize a remote-sensing-based approach to measure impact on Land Surface Temperatures (LST) that are used to estimate SUHI. Moreover, the RF algorithm, can provide useful insights. In this st... Mehr ...

Verfasser: Joshi, Mitali
Aliaga, Daniel G.
Teller, Jacques
Dokumenttyp: book part
Erscheinungsdatum: 2023
Verlag/Hrsg.: Springer Science and Business Media Deutschland GmbH
Schlagwörter: Green roofs / Land surface temperature (LST) / Random forest regression / Urban heat island (UHI) / Geography / Planning and Development / Urban Studies / Engineering / computing & technology / Ingénierie / informatique & technologie
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29304670
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://orbi.uliege.be/handle/2268/308501

peer reviewed ; An abundance of impervious surfaces like building roofs in densely populated cities make green roofs a suitable solution for urban 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 UHI, fewer studies utilize a remote-sensing-based approach to measure impact on Land Surface Temperatures (LST) that are used to estimate SUHI. Moreover, the RF algorithm, can provide useful insights. In this study, we use LST 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 LST.