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Article: Utilizing world urban database and access portal tools (WUDAPT) and machine learning to facilitate spatial estimation of heatwave patterns

TitleUtilizing world urban database and access portal tools (WUDAPT) and machine learning to facilitate spatial estimation of heatwave patterns
Authors
Issue Date2021
Citation
Urban Climate, 2021, v. 36, p. 100797 How to Cite?
AbstractClimate change lead to more intense, higher frequent and prolonged heat extremes. Understanding the spatial pattern of heatwave is vital for providing the corresponding weather services, making climate change adaptation strategies and heat-health actions. In this study, we present an approach to estimate the heatwave spatial patterns by utilizing the WUDAPT Level 0 data and machine learning. The analysis is based on two years (2009 and 2016) of air temperature data from 86 meteorological monitoring stations in Guangdong province of China, a subtropical region with frequent hot and sultry weather in summer. First, heatwave conditions were quantified by calculating the number of hot days and frequency of heatwave events in each year and used as the response variables. Then, random forest models were built by using a geospatial dataset consisting of WUDAPT and urban canopy parameters (UCP) as predictor variables. Based on the resultant models, spatial patterns of heatwave were estimated and mapped at 100 m spatial-resolution. The results show that this approach is able to estimate heatwave spatial patterns using open data and inform urban policy and decision-making. The study is also a new perspective and a feasible pathway of utilizing WUDPAT Level 0 product to facilitate urban environment applications.
Persistent Identifierhttp://hdl.handle.net/10722/310502
ISSN
2023 Impact Factor: 6.0
2023 SCImago Journal Rankings: 1.318
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorShi, Y-
dc.contributor.authorRen, C-
dc.contributor.authorLuo, M-
dc.contributor.authorChing, J-
dc.contributor.authorLi, X-
dc.contributor.authorBilal, M-
dc.contributor.authorFang, X-
dc.contributor.authorRen, Z-
dc.date.accessioned2022-02-07T07:57:35Z-
dc.date.available2022-02-07T07:57:35Z-
dc.date.issued2021-
dc.identifier.citationUrban Climate, 2021, v. 36, p. 100797-
dc.identifier.issn2212-0955-
dc.identifier.urihttp://hdl.handle.net/10722/310502-
dc.description.abstractClimate change lead to more intense, higher frequent and prolonged heat extremes. Understanding the spatial pattern of heatwave is vital for providing the corresponding weather services, making climate change adaptation strategies and heat-health actions. In this study, we present an approach to estimate the heatwave spatial patterns by utilizing the WUDAPT Level 0 data and machine learning. The analysis is based on two years (2009 and 2016) of air temperature data from 86 meteorological monitoring stations in Guangdong province of China, a subtropical region with frequent hot and sultry weather in summer. First, heatwave conditions were quantified by calculating the number of hot days and frequency of heatwave events in each year and used as the response variables. Then, random forest models were built by using a geospatial dataset consisting of WUDAPT and urban canopy parameters (UCP) as predictor variables. Based on the resultant models, spatial patterns of heatwave were estimated and mapped at 100 m spatial-resolution. The results show that this approach is able to estimate heatwave spatial patterns using open data and inform urban policy and decision-making. The study is also a new perspective and a feasible pathway of utilizing WUDPAT Level 0 product to facilitate urban environment applications.-
dc.languageeng-
dc.relation.ispartofUrban Climate-
dc.titleUtilizing world urban database and access portal tools (WUDAPT) and machine learning to facilitate spatial estimation of heatwave patterns-
dc.typeArticle-
dc.identifier.emailRen, C: renchao@hku.hk-
dc.identifier.authorityRen, C=rp02447-
dc.identifier.doi10.1016/j.uclim.2021.100797-
dc.identifier.scopuseid_2-s2.0-85100632847-
dc.identifier.hkuros331608-
dc.identifier.volume36-
dc.identifier.spage100797-
dc.identifier.epage100797-
dc.identifier.isiWOS:000629533300001-

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