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2023 | OriginalPaper | Buchkapitel

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

verfasst von : Mitali Yeshwant Joshi, Daniel G. Aliaga, Jacques Teller

Erschienen in: Intelligence for Future Cities

Verlag: Springer Nature Switzerland

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Abstract

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.

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Titel: Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities
verfasst von: Mitali Yeshwant Joshi
Daniel G. Aliaga
Jacques Teller
Verlag: Springer Nature Switzerland
Buch: Intelligence for Future Cities
Print ISBN: 978-3-031-31745-3

Electronic ISBN: 978-3-031-31746-0

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-31746-0_16

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