Definition of new parameters to develop an improved predictive model for urban heat island spatial distribution
Publication Date
December 13, 2022
Creator
Equere, Victor
Institute of Environmental Management and Assessment, National Examination Board in Safety and Health, University of Nottingham
Abstract
Urban heat island (UHI) is an urban phenomenon where the inner city experiences a significant elevation in temperature, compared to the suburban areas. UHI formation and intensity have an enormous beneficial and adverse impact on the microclimate of urban spaces. Hence, there is a need to understand how several urban aspects impact this phenomenon, to enable policymakers and urban planners to propose efficient mitigation strategies to adverse the UHI effects and to benefit from its potential positive impacts. Furthermore, a robust predictive model is necessary to simulate the efficacy of a proposed mitigation or enhancement strategy before any real-world implementation.
This dissertation, thus, investigates the impact of urban elevation aspects on the UHI intensity and aims to improve the prediction of the spatial distribution of the UHI by proposing two novel urban elevation parameters defined as the Elevation Factor (EF) and Terrain Factor (TF), accounting for urban superstructures and the topographical aspects of cities. The proposed parameters were integrated with other already existing urban parameters in the remote sensing subject area to train and develop predictive models with more accurate predictive performances. An artificial neural network model was developed, which resulted in further improvement of the prediction accuracy.
The associated procedure for retrieving both EF and TF from point cloud LiDAR data. The urban morphology parameters used in this study were derived from two case study cities of North Park and Oak Brook towns in Illinois State, USA. The integration of TF to the training process significantly improved the model’s prediction performance of the LST for high terrain variation areas, as the average root mean square error decreased from 1.26 to 0.90⁰C and 𝑅2 increased from 0.74 to 0.81. The models were then successfully validated against the latter data of the case study cities to predict the LST for parts of the city, which were not initially included in the training process.
Item Type
ethesis
Thesis Type
PhD
Supervisors
Subjects (LC)
Associated Schools / Departments
Department of Architecture and Built Environment (UK)
eprints ID
69725
UoN Repository URI
Except where otherwise noted, this item's license is described as
File(s)![Thumbnail Image]()
Name
Victor George Equere_PhD Thesis_4290063_Final copy.pdf
Type
Full-text
Description
Examined. Corrected Version
Size
4.65 MB
Format
Adobe PDF
Checksum (MD5)
07a9443508ce9cf1962618913fa4b64e