Flood Impact and Risk Assessment on the Road Infrastructure in Federation of Bosnia and Herzegovina

Džebo, Suada; Šarić, Ammar; Reeves, Sarah; Ljevo, Žanesa; Hadžić, Emina

doi:10.1007/978-3-030-24986-1_22

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…As an important part of asset management, which is included in ISO 55001: 2014, risk recognition and management were recognized. For risk identification and prevention, the main steps are: • Hazard identification, • Risk analysis, and • Risk evaluation [7].…”

Section: Figure 1 Examples Of Natural Disastersmentioning

confidence: 99%

Spatial Data Infrastructure in Natural Disaster Management

Ključanin

Rezo

Džebo

et al. 2021

Teh. glas. (Online)

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Natural disasters that cause tremendous material harm and threaten human lives, infrastructure, and economic and social activities become more frequent. While most material and human casualties are the effects of sudden and brief natural disasters, it is not possible to disregard long-term disasters, most frequently triggered by climate change. Damage from long-term disasters is quite significant and hampers the sustainability of the economy and life in densely populated cities. Following climate change, it is concluded that special attention should be paid to coastal cities, cities along rivers and the potential risks of possible disasters should be reduced before they materialize. The reduction of disaster risk is an endeavor involving various professional fields, viewpoints, and stakeholders. An integrated approach that brings together all the necessary elements is therefore required. The planning for protection against natural disasters and in the very moments when it occurs, it is necessary to quickly obtain reliable data to coordinate activities in the field well. However, there are problems in the field with the availability, access, and spatial data use. In this sense, the Spatial Data Infrastructure (SDI) can provide an appropriate framework for sharing spatial data through the cooperation of administration, public, and private institutions and citizens.

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Section: Figure 1 Examples Of Natural Disastersmentioning

confidence: 99%

Spatial Data Infrastructure in Natural Disaster Management

Ključanin

Rezo

Džebo

et al. 2021

Teh. glas. (Online)

Self Cite

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“…In addition, most assessments of urban road waterlogging risk are based on multicriteria comprehensive analysis (MCA). Waterlogging risk assessments have been performed with respect to a hazard–exposure–vulnerability (H–E–V) model in previous studies (Džebo et al, 2020; Tacnet et al, 2012). After combining the vulnerability and exposure indicators to quantify risk, weights are determined subjectively based on the analytic hierarchy process (Roy et al, 2021), and a road waterlogging risk indicator system is constructed.…”

Section: Introductionmentioning

confidence: 99%

Urban road waterlogging risk assessment based on the source–pathway–receptor concept in Shenzhen, China

Sun

Wang

Huang

et al. 2022

J Flood Risk Management

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Urban roads are especially vulnerable to waterlogging during rainfall events.Assessments of urban road waterlogging risk are critical for disaster prevention and mitigation. This paper presents a rapid and practical approach for assessing the risk of urban road waterlogging based on a source-pathway-receptor concept and a digital elevation model (DEM). Waterlogging sources are identified by neighborhood analysis, overland flow pathways are extracted using a DEM, and the properties of cross-nodes between pathways and roads are used to assess the waterlogging risk of roads. Futian District in Shenzhen, China, is selected as the study area. The risk results show that the waterlogging risk sources are clustered in residential land parcels and low-lying road sections, while most road intersections and overpasses in the central area are at higher risks. The results also found that the waterlogging risk decreases as the road grade increases. Actual telephonic flooding records and reported waterlogging areas were used to validate the method, with the risk results displaying good agreement with the actual conditions. The prospects for generalizing this framework and its usage are discussed. The proposed approach provides support for pluvial flooding risk assessment and urban road waterlogging prevention.

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