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Statistical prediction of the nocturnal urban heat island intensity based on urban morphology and geographical factors - An investigation based on numerical model results for a large ensemble of French cities
- Source :
- Science of the Total Environment, Science of the Total Environment, Elsevier, 2020, 737, pp.139253. ⟨10.1016/j.scitotenv.2020.139253⟩
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- Taking into account meteorological data in urban planning increases in relevance in the context of changing climate and enhanced urbanisation. The present article focusses on the nocturnal urban heat island intensity (UHII) simulated with a physically based atmospheric model for >200,000 Reference Spatial Units (RSU), which correspond to building patches delimited by roads or water bodies in 42 French urban agglomerations. First are investigated the statistical relationships between the UHII and six predictors: Local Climate Zone, distance to the agglomeration centre, population, distance to the coast, climatic region, and elevation differences. It is found that the maximum UHII of an agglomeration increases proportional to the logarithm of its population, decreases for cities closer than 10 km to the coast, and is shaped by the regional climate. Secondly, a Random Forest model and a regression-based model are developed to predict the UHII based on the predictors. The advantage of the regression-based model is that it is easier to understand than the black box Random Forest model. The Random Forest model is able to predict the UHII with
- Subjects :
- education.field_of_study
Environmental Engineering
010504 meteorology & atmospheric sciences
Urban agglomeration
Population
Urban morphology
Context (language use)
Atmospheric model
010501 environmental sciences
01 natural sciences
Pollution
[SHS]Humanities and Social Sciences
13. Climate action
Urban planning
Climatology
Urbanization
Environmental Chemistry
Environmental science
Urban heat island
education
Waste Management and Disposal
ComputingMilieux_MISCELLANEOUS
0105 earth and related environmental sciences
Subjects
Details
- Language :
- English
- ISSN :
- 00489697 and 18791026
- Database :
- OpenAIRE
- Journal :
- Science of the Total Environment, Science of the Total Environment, Elsevier, 2020, 737, pp.139253. ⟨10.1016/j.scitotenv.2020.139253⟩
- Accession number :
- edsair.doi.dedup.....416efea0e329b89d9eebd1b534b26f49