4.7 Article

A regional scale quantitative risk assessment for landslides: case of Kumluca watershed in Bartin, Turkey

期刊

LANDSLIDES
卷 10, 期 1, 页码 55-73

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10346-012-0317-9

关键词

Risk assessment; Landslide; Hazard; Element at risk; Vulnerability

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The paper proposes a methodology for quantitative landslide risk assessment for regional-scale analysis. Each component of risk, i.e., hazard, vulnerability, and consequence analysis, is quantitatively assessed. The developed landslide risk assessment methodology is tested in Kumluca watershed, in BartA +/- n, Turkey. Geographic information systems and remote sensing techniques are used to create landslide factor maps, to obtain susceptibility maps, hazard maps, elements at risk, and risk maps. Susceptibility maps are obtained by using a logistic regression model while adopting a grid-based mapping unit. In addition to spatial probability of occurrence of damaging events, landslide hazard calculation requires the determination of the temporal probability. Precipitation triggers the majority of landslides in the study region. The critical rainfall thresholds were estimated by using antecedent rainfalls and landslide occurrence dates based on Gumble Distribution approach. The elements at risk are extracted from existing digital cadastral databases and the vulnerabilities are obtained by adopting some generalization approaches. To conclude, quantitative risk maps were produced on a continuous scale where numerical values indicate the distribution of risk including the annual probability of expected losses in TL per pixel and the annual probability of life loss per pixel for property and life, respectively. For the considered case study, it is found that the annual probability of property loss is the highest for the provincial highway and the provincial road. The property loss map highlights that the annual expected loss to power network is medium. The annual probability of life loss map illustrates that the region surrounded by Kumluca town, KA +/- zA +/- llar, and Zafer villages have medium and high annual expected loss of population values, respectively.

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