Urban Planning for Disaster Risk Reduction: A Systematic Review of Essential Requirements DOI Creative Commons

J Ferreira,

Tatiana Tucunduva Philippi Cortese, Tan Yiğitcanlar

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 15, 2024

Abstract Urban planning is critical in mitigating the impacts of disasters, enhancing community resilience and promoting sustainable development. This review study systematically analyzes role urban disaster risk reduction (DRR) through a Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) approach. By reviewing scholarly articles case studies, this paper examines various strategies that contribute to DRR, including land use planning, infrastructure development, mapping, engagement. The findings highlight effectiveness integrating assessments into processes, importance adaptive design, need inclusive practices involve local communities decision-making. also identifies challenges such as inadequate policy implementation, lack resources, interdisciplinary collaboration, analyzing participation academic importance, correlating publication papers with number reported disasters. Through comprehensive analysis existing literature, underscores potential reduce risks enhance resilience. concludes recommendations policymakers, planners, researchers strengthen DRR initiatives via strategic practices. contributes growing body knowledge emphasizes creating safer, more resilient cities.

Язык: Английский

Urban Infrastructure Vulnerability to Climate-Induced Risks: A Probabilistic Modeling Approach Using Remote Sensing as a Tool in Urban Planning DOI Creative Commons
Ignacio Rodríguez Antuñano, Brais Barros, J. Martínez-Sánchez

и другие.

Infrastructures, Год журнала: 2024, Номер 9(7), С. 107 - 107

Опубликована: Июль 4, 2024

In our contemporary cities, infrastructures face a diverse range of risks, including those caused by climatic events. The availability monitoring technologies such as remote sensing has opened up new possibilities to address or mitigate these risks. Satellite images allow the analysis terrain over time, fostering probabilistic models support adoption data-driven urban planning. This study focuses on exploration various satellite data sources, nighttime land surface temperature (LST) from Landsat-8, well ground motion derived techniques MT-InSAR, Sentinel-1, and proximity infrastructure water. Using information Local Climate Zones (LCZs) current use each building in area, economic implications any changes features soil are evaluated. Through construction Bayesian Network model, synthetic datasets generated identify areas quantify risk Barcelona. results this model were also compared with Multiple Linear Regression concluding that provides crucial for managers. It enables adopting proactive measures reduce negative impacts reducing eliminating possible disparities.

Язык: Английский

Процитировано

3
Is coastal urban environment for disaster prevention equitable? Assessing climate justice of shelters in Xiamen, China DOI

ChenZhuguo Ma,

C. G. Wen

Ocean & Coastal Management, Год журнала: 2025, Номер 261, С. 107546 - 107546

Опубликована: Янв. 18, 2025

Язык: Английский

Процитировано

0
Paving the path to urban flood resilience by overcoming barriers: A novel grey structure analysis approach DOI
Huifang Sun, Wenxin Mao, Dang Luo

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106187 - 106187

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Symbiosis theory based urban resilience evaluation under public health emergencies DOI
Zongmin Li,

Yikai Yang,

Jingqi Dai

и другие.

Environmental Hazards, Год журнала: 2025, Номер unknown, С. 1 - 26

Опубликована: Март 27, 2025

Язык: Английский

Процитировано

0
How to enhance the urban community resilience from the perspective of social networks? A case study of Xuzhou, China DOI
Tiantian Gu, Jinyang Hu, Xiaoyu Song

и другие.

Engineering Construction & Architectural Management, Год журнала: 2025, Номер unknown

Опубликована: Май 2, 2025

Purpose This study aims to enhance urban community resilience from the perspective of social networks in mitigating disaster impacts. It addresses critical gap systematic quantifying resilience, with a particular focus on stakeholder interactions within communities. Design/methodology/approach The proposes an integrated framework leveraging network analysis (SNA) map across three distinct phases. Network metrics are employed as evaluation indicators, while hybrid model combining coefficient variation (COV) and Technique for Order Preference by Similarity Ideal Solutions (TOPSIS) is utilized quantify levels Findings Applying Community F Xuzhou City revealed significant variations during phases, response phase demonstrating most intense interactions. Furthermore, exhibited consistent downward trend Originality/value research provides novel approach management integrating SNA models. advances theoretical insights practical strategies enhancing contributing reduction vulnerabilities disasters.

Язык: Английский

Процитировано

0
Scale effects and driving mechanisms of flood in a multilevel sub-basin perspective - A case study of Haihe River Basin, China DOI
Hanyan Li, Qiao Wang, Mingzhang Zuo

и другие.

Environmental Impact Assessment Review, Год журнала: 2025, Номер 115, С. 107984 - 107984

Опубликована: Май 15, 2025

Язык: Английский

Процитировано

0
Resilient planning pathways to community resilience to tsunami in Chile DOI
Paula Villagra, Marie Geraldine Herrmann‐Lunecke, Oneska Peña y Lillo

и другие.

Habitat International, Год журнала: 2024, Номер 152, С. 103158 - 103158

Опубликована: Авг. 15, 2024

Язык: Английский

Процитировано

2
A Novel Modeling Approach to Quantify the Flood Resilience of Cities DOI Open Access
Wenping Xu, Wenwen Du, David Proverbs

и другие.

Water, Год журнала: 2024, Номер 16(7), С. 1066 - 1066

Опубликована: Апрель 7, 2024

In recent years, large-scale flood events have occurred more frequently, and the concept of resilience has become a prevalent approach to managing risk in many regions. This led an increased interest how effectively measure city’s levels. study proposes novel modeling quantify urban by developing D-number theory analytical hierarchy process (AHP) models, which are applied three cities China using VIse Kriterijumski Optimizacioni Racun (VIKOR) method. The findings reveal that Hefei City most effective level resilience, Hangzhou was ranked second, while Zhengzhou least resilience. provides new scientific basis on at city scale useful reference for these specific cities. methods approaches developed this potential be other related aspects disaster prevention, recovery, reconstruction.

Язык: Английский

Процитировано

1
Resilience Assessment and Enhancement Strategies for Urban Transportation Infrastructure to Cope with Extreme Rainfalls DOI Open Access

Qiuling Lang,

Ziyang Wan,

Jiquan Zhang

и другие.

Sustainability, Год журнала: 2024, Номер 16(11), С. 4780 - 4780

Опубликована: Июнь 4, 2024

As climate change intensifies, urban transportation infrastructure faces unprecedented challenges from extreme weather events, such as floods. This study investigates the resilience and vulnerability of under rainfall conditions in Changchun City. Utilizing Multi-Criteria Decision-Making Analysis (MCDM) Geographic Information System (GIS) techniques, we comprehensively assess physical, functional, service vulnerabilities network. Our analysis reveals that only 3.57% area is classified highly resilient, demonstrating effective flood management capabilities. In contrast, a significant 61.73% exhibits very low resilience, highlighting substantial could impact operations. Based on our findings, propose specific strategies to enhance including optimizing drainage systems, upgrading standards, implementing green initiatives, integrating disaster risk factors into planning. These insights provide valuable references for global cities facing similar climatic challenges.

Язык: Английский

Процитировано

1
Providing Quantitative and Site-Specific Decision Support for Urban Flooding Mitigation Using Machine Learning and SHAP DOI
Entong Ke

SSRN Electronic Journal, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

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Язык: Английский

Процитировано

0