Rethinking Urban Flood Risk Assessment By Adapting Health Domain Perspective
<|reference_start|>Rethinking Urban Flood Risk Assessment By Adapting Health Domain Perspective: Inspired by ideas from health risk assessment, this paper presents a new perspective for flood risk assessment. The proposed perspective focuses on three pillars for examining flood risk: (1) inherent susceptibility, (2) mitigation strategies, and (3) external stressors. These pillars collectively encompass the physical and environmental characteristics of urban areas, the effectiveness of human-intervention measures, and the influence of uncontrollable external factors, offering a fresh point of view for decoding flood risks. For each pillar, we delineate its individual contributions to flood risk and illustrate their interactive and overall impact. The three-pillars model embodies a shift in focus from the quest to precisely model and quantify flood risk to evaluating pathways to high flood risk. The shift in perspective is intended to alleviate the quest for quantifying and predicting flood risk at fine resolutions as a panacea for enhanced flood risk management. The decomposition of flood risk pathways into the three intertwined pillars (i.e., inherent factors, mitigation factors, and external factors) enables evaluation of changes in factors within each pillar enhance and exacerbate flood risk, creating a platform from which to inform plans, decisions, and actions. Building on this foundation, we argue that a flood risk pathway analysis approach, which examines the individual and collective impacts of inherent factors, mitigation strategies, and external stressors, is essential for a nuanced evaluation of flood risk. Accordingly, the proposed perspective could complement the existing frameworks and approaches for flood risk assessment.<|reference_end|>
@article{liu2024rethinking,
    title={Rethinking Urban Flood Risk Assessment By Adapting Health Domain
  Perspective},
    author={Zhewei Liu, Kai Yin, Ali Mostafavi},
    journal={arXiv preprint arXiv:2403.03996},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03996},
    primaryClass={cs.AI}
}