Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates

Accurate and timely information on building age is essential for mitigating the impacts of natural disasters such as earthquakes and floods. Traditional methods for collecting these data are often inefficient and costly. This study leverages remote sensing and machine learning to classify building a...

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Bibliographic Details
Published inWater (Basel) Vol. 16; no. 17; p. 2408
Main Authors Alsumaiti, Tareefa, Yagoub, M. M., Tesfaldet, Yacob T., Alhosani, Naeema, Pakam, Subraelu
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2024
Subjects
Age
Al Ain
building age
Buildings
Drainage
Earthquakes
Emergency preparedness
flood hazard
Floods
Geographic information systems
Infrastructure
Integrated approach
Land use
Machine learning
Mapping
Remote sensing
Soils
Storm damage
Topography
UAE
United Arab Emirates
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Summary:Accurate and timely information on building age is essential for mitigating the impacts of natural disasters such as earthquakes and floods. Traditional methods for collecting these data are often inefficient and costly. This study leverages remote sensing and machine learning to classify building age and integrate this information into a comprehensive flood hazard map for Al Ain City. By combining building age with elevation, topography, land use, population density, and other factors, we generated a flood hazard map that effectively identifies high-risk areas. Our results show that a significant portion of the city is has low potential for flooding (61%), while others face moderate (32%), high, or very high flood hazards. This study provides crucial information for informed decision-making regarding future urban planning, disaster management, and development strategies in Al Ain City. By identifying vulnerable buildings in advance, it supports efforts to reduce disaster risks, protect lives, and optimize resource allocation.
ISSN:2073-4441
2073-4441
DOI:10.3390/w16172408