标题： 人工智能应对自然灾害风险：数据中心对自然灾害和停电的全国脆弱性评估 显示英文标题

标题： AI Meets Natural Hazard Risk: A Nationwide Vulnerability Assessment of Data Centers to Natural Hazards and Power Outages

摘要： 我们社会正处于由人工智能(AI)技术推动的革命边缘。 随着人工智能的不断进步，作为这一新技术浪潮关键基础设施的数据中心(DCs)正在不断扩大。 这股技术浪潮也与日益加剧的气候灾害发生冲突，这引发了评估数据中心对各种灾害脆弱性的需求。 因此，本研究的目的是对美国(USA)的数据中心(DCs)进行全国范围的脆弱性评估。 数据中心提供这种支持;然而，如果发生非计划中断(如自然灾害或停电)，数据中心的功能就会受到威胁。 数据中心的非计划停机时间会造成严重的经济和社会影响。 通过局部空间关联指标(LISA)测试，研究发现有很大一部分数据中心位于非脆弱的中断区域;然而，仍有一部分位于易受中断影响的区域。 例如，地震、飓风和龙卷风在易受影响的区域拥有最多的数据中心。 在确定这些脆弱性之后，研究使用BI-LISA测试识别了美国境内对上述自然灾害和停电具有最小脆弱性的地区。 在对BILISA分析中的冷点进行综合脆弱性评分后，研究发现了三个脆弱性评分较低的县。 它们是明尼苏达州的库奇钦县(0.091)、密歇根州的斯科尔克里夫县(0.095)和霍顿县(0.096)。 显示英文摘要

摘要： 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).