Stepwise Multisensor Estimation of Shelter Hazard and Lifeline Outages for Disaster Response and Resilience: A Case Study of the 2024 Noto Peninsula Earthquake

Satomi Kimijima (), Chun Ping, Shono Fujita, Makoto Hanashima, Shingo Toride and Hitoshi Taguchi
Additional contact information
Satomi Kimijima: National Research Institute for Earth Science and Disaster Resilience, Ibaraki 305-0006, Japan
Chun Ping: National Research Institute for Earth Science and Disaster Resilience, Ibaraki 305-0006, Japan
Shono Fujita: National Research Institute for Earth Science and Disaster Resilience, Ibaraki 305-0006, Japan
Makoto Hanashima: National Research Institute for Earth Science and Disaster Resilience, Ibaraki 305-0006, Japan
Shingo Toride: National Research Institute for Earth Science and Disaster Resilience, Ibaraki 305-0006, Japan
Hitoshi Taguchi: National Research Institute for Earth Science and Disaster Resilience, Ibaraki 305-0006, Japan

Sustainability, 2025, vol. 17, issue 20, 1-25

Abstract: 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.

Keywords: disaster response; disaster resilience; evacuation shelter; lifeline service outages; multisensor; shelter functionality; shelter operability; situational understanding (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/20/9261/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/20/9261/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:20:p:9261-:d:1774573

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().