Fatih Dur
Biographic Data
| ID | 4360758 |
|---|---|
| NAME | Fatih Dur |
| GIVEN NAMES | Fatih |
| FAMILY NAME | Dur |
| SIGNATURE | DUR F |
| AFFILIATIONS | Queensland University of Technology |
| ORCID | 0009-0008-3883-6643 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 16 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Mapping heat vulnerability in Australian capital cities
Pioneers machine learning with multi-source data to improve heat vulnerability assessments. • The random forest model exhibited superior accuracy with high R2 in training and testing. • Urban areas demonstrated greater heat vulnerability than suburban and rural zones. • Health conditions, age, and education were the dominant heat vulnerability factors. • These findings support targeted mitigation policies and adaptation strategies. Heat vulnerabi…
Assessing heat vulnerability and multidimensional inequity
Forms a heat vulnerability index using socio-demographic, health, and environmental data. • Validated the heat vulnerability index against heat-related mortality data. • Finds higher heat urban exposure with varying sensitivity and capacity shaping vulnerability. • Identifies vulnerable groups: Indigenous, low English proficiency, and transit commuters. • Offers targeted strategies to boost climate resilience and thermal equity in Australian citi…
Towards prosperous sustainable cities
Towards prosperous sustainable cities
Assessing heat vulnerability and multidimensional inequity
Forms a heat vulnerability index using socio-demographic, health, and environmental data. • Validated the heat vulnerability index against heat-related mortality data. • Finds higher heat urban exposure with varying sensitivity and capacity shaping vulnerability. • Identifies vulnerable groups: Indigenous, low English proficiency, and transit commuters. • Offers targeted strategies to boost climate resilience and thermal equity in Australian citi…
Mapping heat vulnerability in Australian capital cities
Pioneers machine learning with multi-source data to improve heat vulnerability assessments. • The random forest model exhibited superior accuracy with high R2 in training and testing. • Urban areas demonstrated greater heat vulnerability than suburban and rural zones. • Health conditions, age, and education were the dominant heat vulnerability factors. • These findings support targeted mitigation policies and adaptation strategies. Heat vulnerabi…
Computer Science (3 works) · Geography (3 works) · Business (2 works) · Climate Change and Health Impacts (2 works) · Computer security (2 works) · Engineering (2 works) · Cartography (1 works) · Civil engineering (1 works) · Data science (1 works) · Ecology (1 works)