Global sensitivity analysis with aggregated Shapley effects, application to avalanche hazard assessment

Heredia, María Belén; Prieur, Clémentine; Eckert, Nicolas

doi:10.1016/j.ress.2022.108420

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…case I, had the largest impact on all the studied variables (runout return period, impact pressure and risk) compared to varying the friction coefficient ξ (case II). This relates to the high sensitivity to changes in μ of runout distances in accordance with previous studies (Barbolini and others, 2000; Heredia and others, 2021, 2022). To which extent our results are direct consequences of partially subjective model characteristics is thus debatable.…”

Section: Discussionsupporting

confidence: 91%

“…However, it is known for long that the runout distance is mostly controlled by the static friction coefficient μ (e.g. Dent and Lang, 1980; Borstad and McClung, 2009), which has been recently confirmed by more systematic sensitivity analyses by Heredia and others (2021, 2022). Similarly, Teich and others (2012b) showed that focusing on the turbulent friction ξ only when investigating how forest cover impacts avalanche dynamics is not enough since the effects on runout distances of sole slight decrease of ξ remain too limited.…”

Section: Methodsmentioning

confidence: 84%

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Diachronic quantitative snow avalanche risk assessment as a function of forest cover changes

et al. 2022

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This work proposes a holistic quantitative snow avalanche risk assessment that evaluates, at reasonable computational costs and for various types of buildings, the impact of forest cover changes on the probability distribution of runout distances, impact pressures and subsequent risk estimates. A typical case study of the French Alps shows that, from a completely deforested to a completely forested path, avalanche risk for a building located downslope decreases by 53–99%, depending on how forest cover is accounted for in avalanche statistical–dynamical modeling. Local forest cover data inferred from old maps and photographs further demonstrates that a 20–60% risk reduction actually occurred between 1825 and 2017 at the site because of the afforestation dynamics, with significant modulations according to the considered building technology. These results (1) assert the protective role of forests against snow avalanches, (2) highlight the potential of combining nature-based solutions with traditional structural measures to reduce risk to acceptable levels at reasonable costs, (3) suggest a significant decrease in risk to settlements in areas that encountered similar forest cover changes and (4) open the door to the quantification of long-term avalanche risk changes as a function of changes of all its hazard, vulnerability and exposure drivers in various mountain context.

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 84%

Diachronic quantitative snow avalanche risk assessment as a function of forest cover changes

et al. 2022

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Construction of a Farm-Level Food Security Index: Case Study of Turkish Dairy Farms

Koç,

Uzmay

2024

Soc Indic Res

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Food security continues to be a global concern and its importance has recently increased for many reasons. Composite food security indices have been widely used to calculate and monitor food security, but farm-level studies are limited. Therefore, the main objective of this study is to construct a Farm-level Food Security Index (FFSI) for dairy farms to assess their contribution to food security, identify potential areas for improvement and guide policy makers. Data were collected from 126 farms in the Thrace Region of Turkey through face-to-face interviews. The FFSI was constructed with four dimensions, briefly called economic, quality, social and natural resources, containing twenty-three variables. Principal component analysis was used for the determination of variable weights, data envelopment analysis for calculating technical efficiency, and the Tobit model for examining the factors influencing FFSI scores. To assess the robustness of the FFSI, Monte Carlo simulations-based uncertainty and sensitivity analysis, dimension extraction approach and Shapley effects sensitivity analysis were performed. With an average score of 56.8, the key result of the FFSI is that dairy farms are using almost half of their potential to fully contribute to food security. Moreover, according to the Tobit model, FFSI scores are significantly affected by the farmer’s age and education level, credit use, livestock unit, fodder crop area and milk marketing channel. The FFSI is robust to weights and sensitive to normalisation, and the social sustainability dimension can cause the largest shift in index scores. Based on these findings, numerous agricultural policy proposals have been developed in this study by identifying the priority areas that need to be addressed to guarantee food security.

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Variable importance analysis of wind turbine extreme responses with Shapley value explanation

Zhang,

Dimitrov

2024

Renewable Energy

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Global sensitivity analysis with aggregated Shapley effects, application to avalanche hazard assessment

Cited by 6 publications

References 42 publications

Diachronic quantitative snow avalanche risk assessment as a function of forest cover changes

Diachronic quantitative snow avalanche risk assessment as a function of forest cover changes

Construction of a Farm-Level Food Security Index: Case Study of Turkish Dairy Farms

Variable importance analysis of wind turbine extreme responses with Shapley value explanation

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