Repository logo
 
Publication

An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples

dc.contributor.authorOliveira, Ana
dc.contributor.authorLopes, António
dc.contributor.authorNiza, Samuel
dc.contributor.authorSoares, Amílcar
dc.date.accessioned2022-02-22T11:18:06Z
dc.date.available2022-02-22T11:18:06Z
dc.date.issued2022
dc.description.abstractSouthern European functional urban areas (FUAs) are increasingly subject to heatwave (HW) events, calling for anticipated climate adaptation measures. In the urban context, such adaptation strategies require a thorough understanding of the built-up response to the incoming solar radiation, i.e., the urban energy balance cycle and its implications for the Urban Heat Island (UHI) effect. Despite readily available, diurnal Land Surface Temperature (LST) data does not provide a meaningful picture of the UHI, in these midlatitudes FUAs. On the contrary, the mid-morning satellite overpass is characterized by the absence of a significant surface UHI (SUHI) signal, corresponding to the period of the day when the urban-rural air temperature difference is typically negative. Conversely, nocturnal high-resolution LST data is rarely available. In this study, an energy balance-based machine learning approach is explored, considering the Local Climate Zones (LCZ), to describe the daily cycle of the heat flux components and predict the nocturnal SUHI, during an HW event. While the urban and rural spatial outlines are not visible in the diurnal thermal image, they become apparent in the latent and storage heat flux maps – built-up infrastructures uptake heat during the day which is released back into the atmosphere, during the night, whereas vegetation land surfaces loose diurnal heat through evapotranspiration. For the LST prediction model, a random forest (RF) approach is implemented. RF results show that the model accurately predicts the LST, ensuring mean square errors inferior to 0.1 K. Both the latent and storage heat flux components, together with LCZ classification, are the most important explanatory variables for the nocturnal LST prediction, supporting the adoption of the energy balance approach. In future research, other locations and time-series data shall be trained and tested, providing an efficient local urban climate monitoring tool, where in-situ air temperature observations are not available.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationOliveira, A., Lopes, A., Niza, S., & Soares, A. (2022). An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naples. Science of The Total Environment, 805, 150130. https://doi.org/10.1016/j.scitotenv.2021.150130pt_PT
dc.identifier.doi10.1016/j.scitotenv.2021.150130pt_PT
dc.identifier.issn0048-9697
dc.identifier.urihttp://hdl.handle.net/10451/51444
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationSUSTAINABLE ENERGY SISTEMS
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0048969721052050?via%3Dihubpt_PT
dc.subjectUrban climate adaptationpt_PT
dc.subjectHeatwavept_PT
dc.subjectUrban Heat Islandpt_PT
dc.subjectLand surface temperaturept_PT
dc.subjectLocal climate zonespt_PT
dc.subjectRandom forestpt_PT
dc.subjectMultisensor data fusionpt_PT
dc.subjectSatellite thermal imagerypt_PT
dc.titleAn urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in Naplespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleSUSTAINABLE ENERGY SISTEMS
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//PD%2FBD%2F52304%2F2013/PT
oaire.citation.startPage150130pt_PT
oaire.citation.titleScience of The Total Environmentpt_PT
oaire.citation.volume805pt_PT
person.familyNameLopes
person.givenNameAntónio
person.identifier216928
person.identifier.ciencia-id1D15-FB93-4687
person.identifier.orcid0000-0002-9357-7639
person.identifier.ridF-3217-2010
person.identifier.scopus-author-id55951850000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication5ec106ce-350f-4b1b-aed6-1acd9f11f7f1
relation.isAuthorOfPublication.latestForDiscovery5ec106ce-350f-4b1b-aed6-1acd9f11f7f1
relation.isProjectOfPublication900f4f68-aa3c-4f8d-b16b-47c34c55a71f
relation.isProjectOfPublication.latestForDiscovery900f4f68-aa3c-4f8d-b16b-47c34c55a71f

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Oliveira_Lopes_Niza_Soares_2022.pdf
Size:
7.15 MB
Format:
Adobe Portable Document Format