2021
DOI: 10.3389/feart.2021.685301
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Are We Missing the Target? A Bias-Variance Perspective on Multi-Hazard Risk Assessment

Abstract: This paper presents a generalization of the bias-variance tradeoff applied to the recent trend toward natural multi-hazard risk assessment. The bias-variance dilemma, a well-known machine learning theory, is presented in the context of natural hazard modeling. It is then argued that the bias-variance statistical concept can provide an analytical framework for the necessity to direct efforts toward systemic risk assessment using multi-hazard catastrophe modeling and inform future mitigation practices.

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Cited by 3 publications
(1 citation statement)
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References 30 publications
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“…Imagination from existing disaster and climate change studies suggests several trajectories to systemic risk. Hazard-prone areas susceptible to multiple hazards often experience cascading events, such as the excessive rainfall in northern Pakistan in 2016 that led to devastating flooding and landslides [6][7][8]. Additionally, large-scale natural hazard events can release energy equivalent to tens of thousands of Hiroshima atomic bombs, causing systemic infrastructure failures and widespread fatalities.…”
Section: Introductionmentioning
confidence: 99%
“…Imagination from existing disaster and climate change studies suggests several trajectories to systemic risk. Hazard-prone areas susceptible to multiple hazards often experience cascading events, such as the excessive rainfall in northern Pakistan in 2016 that led to devastating flooding and landslides [6][7][8]. Additionally, large-scale natural hazard events can release energy equivalent to tens of thousands of Hiroshima atomic bombs, causing systemic infrastructure failures and widespread fatalities.…”
Section: Introductionmentioning
confidence: 99%