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  • FEMA  (1)
  • average treatment effect  (1)
  • 1
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    Interpretable Machine Learning Reveals Potential to Overcome Reactive Flood Adaptation in the Continental US (2023)
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    Publication Date: 2023-11-16
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Floods cause average annual losses of more than US$30 billion in the US and are estimated to significantly increase due to global change. Flood resilience, which currently differs strongly between socio‐economic groups, needs to be substantially improved by proactive adaptive measures, such as timely purchase of flood insurance. Yet, knowledge about the state and uptake of private adaptation and its drivers is so far scarce and fragmented. Based on interpretable machine learning and large insurance and socio‐economic open data sets covering the whole continental US we reveal that flood insurance purchase is characterized by reactive behavior after severe flood events. However, we observe that the Community Rating System helps overcome this behavior by effectively fostering proactive insurance purchase, irrespective of socio‐economic backgrounds in the communities. Thus, we recommend developing additional targeted measures to help overcome existing inequalities, for example, by providing special incentives to the most vulnerable and exposed communities.〈/p〉
    Description: Plain Language Summary: Flood resilience of individuals and communities can be improved by bottom‐up strategies, such as insurance purchase, or top‐down measures like the US National Flood Insurance Program's Community Rating System (CRS). Our interpretable machine learning approach shows that flood insurances are mostly purchased reactively, after the occurrence of a flood event. Yet, reactive behaviors are ill‐suited as more extreme events are expected under future climate, also in areas that were not previously flooded. The CRS counteracts this behavior by fostering proactive adaptation across a widespread range of socio‐economic backgrounds. Future risk management including the CRS should support and motivate individuals' proactive adaptation with a particular focus on highly vulnerable social groups to overcome existing inequalities in flood risk.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Flood insurance purchase in the US is dominated by reactive behavior after severe floods〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The Community Rating System (CRS) fosters proactive insurance adoption irrespective of socio‐economic background〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The CRS should further balance existing inequalities by targeting specific population segments〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: https://api.census.gov/data/2018/acs/
    Description: https://www.fema.gov/about/openfema/data-sets#nfip
    Description: https://www.fema.gov/fact-sheet/community-rating-system-overview-and-participation
    Description: https://msc.fema.gov/portal/home
    Description: https://www.fema.gov/case-study/information-about-community-rating-system
    Description: https://doi.org/10.5281/zenodo.8067448
    Keywords: ddc:363.34 ; FEMA ; machine learning ; flood insurance ; human behavior ; flood resilience
    Language: English
    Type: doc-type:article
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  • 2
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    Quantifying Flood Vulnerability Reduction via Private Precaution (2021)
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    Publication Date: 2021-06-27
    Description: Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state‐of‐the‐art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network‐based model BN‐FLEMOps and the rule‐based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.
    Description: Plain Language Summary: Private precautionary measures such as adapted building use, sealing basements and purchasing flood barriers reduce flood damage to residential buildings. Using an empirical dataset consisting of 948 flooded households in Germany, we estimate that the average loss reducing effect of implementing private precautionary measures is 11‐15 thousand EUR per household. This is approximately equal to 27% of the average building loss suffered by the flooded households (48000 EUR). Despite this significant risk mitigation effect, these precautionary measures are hardly considered in flood risk assessment modelling. This results in biased flood loss predictions being used for evaluating risk management strategies. Hence, we compare state‐of‐the‐art flood loss models in respect to their ability to account for building loss reduction due to private precaution. From all tested flood loss models, the expert Bayesian Network based model BN‐FLEMOps and the rule‐based loss model FLEMOps are best able to capture the damage reducing effect of private precaution. These models can be valuable for evaluating adaptable flood risk management strategies.
    Description: Key Points: Private precaution significantly reduces the flood vulnerability of private households as shown by robust empirical matching methods State‐of‐the‐art flood damage models differ strongly based on their ability to capture differences in vulnerability of private households Methodology applied and validated using an extensive object‐level flood damage data set from Germany
    Description: European Union http://dx.doi.org/10.13039/100011102
    Keywords: 333.91 ; flood loss ; average treatment effect ; matching methods ; loss models ; risk analysis ; adaptation
    Type: article
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    CITATION GEO-LEO
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