AI Meets Natural Hazard Risk: A Nationwide Vulnerability Assessment of Data Centers to Natural Hazards and Power Outages
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913934583267328 |
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| author | Esparza, Miguel Li, Bo Ma, Junwei Mostafavi, Ali |
| author_facet | Esparza, Miguel Li, Bo Ma, Junwei Mostafavi, Ali |
| contents | Our society is on the verge of a revolution powered by Artificial Intelligence (AI) technologies. With increasing advancements in AI, there is a growing expansion in data centers (DCs) serving as critical infrastructure for this new wave of technologies. This technological wave is also on a collision course with exacerbating climate hazards which raises the need for evaluating the vulnerability of DCs to various hazards. Hence, the objective of this research is to conduct a nationwide vulnerability assessment of (DCs) in the United States of America (USA). DCs provide such support; however, if an unplanned disruption (like a natural hazard or power outage) occurs, the functionality of DCs are in jeopardy. Unplanned downtime in DCs cause severe economic and social repercussions. With the Local Indicator of Spatial Association (LISA) test, the research found that there are a large percentage of DCs that are in non-vulnerable areas of disruption; however, there is still a notable percentage in disruption prone areas. For example, earthquakes, hurricanes, and tornadoes have the most DCs in vulnerable areas. After identifying these vulnerabilities, the research identified areas within the USA that have minimal vulnerabilities to both the aforementioned natural hazards and power outages with the BI-LISA test. After doing a composite vulnerability score on the Cold-Spots from the BILISA analysis, the research found three counties with the low vulnerability scores. These are Koochiching, Minnesota (0.091), Schoolcraft, Michigan (0.095), and Houghton, Michigan (0.096). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14760 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI Meets Natural Hazard Risk: A Nationwide Vulnerability Assessment of Data Centers to Natural Hazards and Power Outages Esparza, Miguel Li, Bo Ma, Junwei Mostafavi, Ali Computers and Society Our society is on the verge of a revolution powered by Artificial Intelligence (AI) technologies. With increasing advancements in AI, there is a growing expansion in data centers (DCs) serving as critical infrastructure for this new wave of technologies. This technological wave is also on a collision course with exacerbating climate hazards which raises the need for evaluating the vulnerability of DCs to various hazards. Hence, the objective of this research is to conduct a nationwide vulnerability assessment of (DCs) in the United States of America (USA). DCs provide such support; however, if an unplanned disruption (like a natural hazard or power outage) occurs, the functionality of DCs are in jeopardy. Unplanned downtime in DCs cause severe economic and social repercussions. With the Local Indicator of Spatial Association (LISA) test, the research found that there are a large percentage of DCs that are in non-vulnerable areas of disruption; however, there is still a notable percentage in disruption prone areas. For example, earthquakes, hurricanes, and tornadoes have the most DCs in vulnerable areas. After identifying these vulnerabilities, the research identified areas within the USA that have minimal vulnerabilities to both the aforementioned natural hazards and power outages with the BI-LISA test. After doing a composite vulnerability score on the Cold-Spots from the BILISA analysis, the research found three counties with the low vulnerability scores. These are Koochiching, Minnesota (0.091), Schoolcraft, Michigan (0.095), and Houghton, Michigan (0.096). |
| title | AI Meets Natural Hazard Risk: A Nationwide Vulnerability Assessment of Data Centers to Natural Hazards and Power Outages |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2501.14760 |