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Title: Estimating the societal impact of water infrastructure disruptions: A novel model incorporating individuals’ activity choices
Authors: Yang, Yongsheng
Tatano, Hirokazu  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-7209-4358 (unconfirmed)
Huang, Quanyi
Wang, Ke
Liu, Huan
Author's alias: 多々納, 裕一
Keywords: Societal impact
Infrastructure disruption
Water-related activity estimation
Suffering level
Disaster
Resilient cities
Issue Date: Dec-2021
Publisher: Elsevier BV
Journal title: Sustainable Cities and Society
Volume: 75
Thesis number: 103290
Abstract: The well-being of society can be severely impacted by infrastructure disruptions. This study proposes a novel mathematical model to estimate the societal impact of water disruption quantitatively from two aspects: the percentage of people who can perform certain water-related activities and the percentage of people intolerant to disrupted activities. The model begins by incorporating the tolerance level (TL) to establish a suffering level function of the disrupted activity. Then, from the individual's perspective, an activity estimation model is developed to predict an individual's activity choices when water is limited due to infrastructure disruptions, and this model is mainly driven by prioritizing activities with the maximum suffering level. To quantify the societal impact in regions, a Monte Carlo simulation is adopted to generate simulated residents with randomly sampled TL following lognormal or Weibull distributions, and the activity estimation model is conducted for each simulated resident; consequently, societal impacts can be aggregated and derived. Additionally, an illustrative case study of Osaka and sensitivity analyses are performed; the results validate the model's effectiveness and applicability. The proposed model provides insightful information to support emergency management and can be integrated with infrastructure resilience models to better build human-centric sustainable and resilient cities.
Rights: © 2021. This manuscript version is made available under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license.
The full-text file will be made open to the public on 1 December 2023 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/267472
DOI(Published Version): 10.1016/j.scs.2021.103290
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