Persistent Spatio-Temporal Outage Hotspot Detection for Infrastructure Resilience Planning

[2608.14572] Persistent Spatio-Temporal Outage Hotspot Detection for Infrastructure Resilience Planning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Networking and Internet Architecture arXiv:2608.14572 (cs) [Submitted on 6 Jun 2026] Title: Persistent Spatio-Temporal Outage Hotspot Detection for Infrastructure Resilience Planning Authors: Solongo Ganbold , Sohini Roy View a PDF of the paper titled Persistent Spatio-Temporal Outage Hotspot Detection for Infrastructure Resilience Planning, by Solongo Ganbold and 1 other authors View PDF Abstract: Extreme weather events are producing persistent geographic patterns of power-grid disruption across the United States, yet outage hotspot detection and infrastructure cascade modeling are often studied separately. This paper presents a data-driven geospatial framework that links persistent outage vulnerability with downstream cascade impact in interdependent power-communication networks. Using a national outage dataset from 2015-2023, we introduce the Hotspot Persistence Index (HPI), a severity-aware metric for identifying counties that repeatedly emerge as outage hotspots over time. We then apply a multi-scale DBSCAN refinement procedure to convert persistent county-level hotspots into geographically interpretable regional failure scenarios characterized by recurrence, severity, and spatial extent. To evaluate their system-level relevance, these empirically derived scenarios are injected into the Modified Implicative Interdependency Model (MIIM), which captures cascading behavior across coupled power and communication layers. Results show that three persistent regional clusters account for 54.4% of total HPI-weighted cascade impact, while communication-layer entities fail at 2.5X the rate of power buses under high-persistence scenarios. HPI-guided hardening reprioritizes protection candidates relative to a degree- and betweenness-centrality baseline, identifying high-value buses that topology-only rankings overlook. These results demonstrate how persistent geospatial outage patterns can support targeted and empirically grounded infrastructure resilience planning. Subjects: Networking and Internet Architecture (cs.NI) ; Computers and Society (cs.CY) Cite as: arXiv:2608.14572 [cs.NI] (or arXiv:2608.14572v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2608.14572 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Sohini Roy [ view email ] [v1] Sat, 6 Jun 2026 06:11:54 UTC (1,006 KB) Full-text links: Access Paper: View a PDF of the paper titled Persistent Spatio-Temporal Outage Hotspot Detection for Infrastructure Resilience Planning, by Solongo Ganbold and 1 other authors View PDF view license Current browse context: cs.NI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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