WEKO3
アイテム
Stepwise Multisensor Estimation of Shelter Hazard and Lifeline Outages for Disaster Response and Resilience: A Case Study of the 2024 Noto Peninsula Earthquake
https://nied-repo.bosai.go.jp/records/7340
https://nied-repo.bosai.go.jp/records/7340edfe2394-e18d-4761-9667-270d3aee6c8b
| Item type | researchmap(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2026-05-04 | |||||||
| タイトル | ||||||||
| 言語 | en | |||||||
| タイトル | Stepwise Multisensor Estimation of Shelter Hazard and Lifeline Outages for Disaster Response and Resilience: A Case Study of the 2024 Noto Peninsula Earthquake | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 著者 |
Satomi Kimijima
× Satomi Kimijima
|
|||||||
| 抄録 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | Addressing earthquake risk remains a significant global challenge, requiring rapid assessment of evacuation shelters for effective disaster response. Existing frameworks, such as FEMA’s Hazus, Copernicus EMS, and UNOSAT, offer valuable insights but are typically regional, static, and event-focused, lacking mechanisms for continuous shelter-level updates. This study introduces the Shelter Hazard Impact and Lifeline Outage Estimation (SHILOE) framework. SHILOE is a stepwise estimation approach integrating multisensor datasets for time-scaled assessments of shelter functionality and operability. These datasets include seismic intensity, liquefaction probability, tsunami inundation, IoT-derived power outage data, communication network disruptions, and social media. Application to the 2024 Noto Peninsula earthquake showed that ≥93.6% of designated and activated shelters were impacted by at least one hazard, with all experiencing at least one lifeline outage. The framework delivers estimates through three phases: immediate (within tens of minutes, e.g., simulation-based hazard models and lifeline data), intermediate (days, e.g., observation-based datasets), and refinement (ongoing, e.g., Social Networking Service and detailed field surveys). By progressively incorporating new data across these phases, SHILOE generates dynamic, facility-level insights that capture evolving hazard exposure and lifeline status. These outputs provide actionable information for emergency managers to prioritize resources, reinforce shelters, and sustain critical services, thereby advancing disaster resilience. | |||||||
| 言語 | en | |||||||
| 書誌情報 |
en : Sustainability 巻 17, 号 20, p. 9261-9261, 発行日 2025-10-18 |
|||||||
| 出版者 | ||||||||
| 言語 | en | |||||||
| 出版者 | MDPI AG | |||||||
| ISSN | ||||||||
| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 2071-1050 | |||||||
| DOI | ||||||||
| 関連識別子 | 10.3390/su17209261 | |||||||
