Optimized shelter planning in flood-prone areas using geographic information systems (GIS) and the analytical hierarchy process (AHP): an analysis of Ubon Ratchathani, Thailand

Ajira Tiangtrong, Thanaporn Mangmoon, Sasinaree Apirak, Noppadol Amornwech, Nitipon Noipow and Chyan-Deng Jan ()
Additional contact information
Ajira Tiangtrong: National Cheng Kung University
Thanaporn Mangmoon: Ministry of Interior
Sasinaree Apirak: Rayong Success Solution Co.,Ltd
Noppadol Amornwech: Navamindradhiraj University
Nitipon Noipow: Navamindradhiraj University
Chyan-Deng Jan: National Cheng Kung University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 18, No 13, 21097-21119

Abstract: Abstract As climate-related disasters increase worldwide, effective planning for emergency shelters is essential to reduce disaster risk and strengthen community resilience. While geospatial tools are increasingly used in disaster response, standard shelter site planning often lacks a systematic integration of social vulnerability and spatial risk factors. This study proposes a decision-support framework that combines Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to identify suitable shelter locations in flood-prone areas. A case study in Ubon Ratchathani, Thailand—where recurring floods have repeatedly displaced communities—illustrates the importance of anticipatory and inclusive planning. The framework incorporates dimensions of hazard, exposure, and vulnerability, with a focus on especially at-risk groups, including older adults, persons with disabilities, women, children, and low-income populations. Evaluated shelter locations are classified into 5 levels: highly suitable, suitable, moderately suitable, unsuitable and highly unsuitable. Results show that 3, 11 and 300 out of 566 existing shelters were highly suitable, suitable and moderately suitable, respectively, based on flood risk, road access, and proximity to health facilities. The model achieved a validation accuracy of 96.21% using 2022 flood data. By reducing avoidable relocations, the approach enhances safety, equity, and operational efficiency. The findings support progress toward Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities), 13 (Climate Action), and 10 (Reduced Inequalities). The GIS–AHP model is adaptable to other flood- or climate-affected regions, offering a practical tool for data-driven, inclusive disaster preparedness planning.

Keywords: Shelter planning; Flood risk; Disaster management; GIS; AHP (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-025-07604-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:18:d:10.1007_s11069-025-07604-6

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-025-07604-6

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().