EAGER: An Exploratory Study of Multi-Hazard Management through Multi-Source Integration of Physical and Social Sensors

EAGER：通过物理和社会传感器的多源集成进行多危害管理的探索性研究

• 批准号: 1402266
• 负责人: Calton Pu
• 金额: $ 30万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1402266&HistoricalAwards=false
• 关键词: EAGER; Exploratory; Study; Multi; Hazard

Natural and man-made disasters can cause significant material damages and human suffering. For example, Superstorm Sandy of 2012 is estimated to have caused more than $68 billion in damages and killed at least 286 people in seven countries. Improving the preparation for, response to, and recovery from disasters can reduce damages, relieve human suffering, and speed up recovery. Among disasters, a multi-hazard is a sequence of disasters in which the first disaster causes the subsequent disasters, making it far more difficult for emergency response teams to handle all of them. For example, the March 11, 2011, Tohoku, Japan, earthquake triggered an unprecedented tsunami, which led to flooding at, and partial meltdown of, the Fukushima Daiichi Nuclear Power Plant. A more frequent example of multi-hazards is landslides, which can be triggered by many causes including earthquakes, rainfall, and man-made environmental changes.While the detection of a single disaster usually only requires one kind of dedicated sensor, for example, seismographs can detect earthquakes reliably, multi-hazards often require a combination of various kinds of sensors for the detection of the multiple events in the sequence. Indeed, the detection of multi-events in general and multi-hazards in particular is a non-trivial problem due to the various kinds of events involved and the large number of combinations that make offline combinatorial analysis impractical. In the case of landslides, their detection is complicated further by the several possible and unrelated causes of landslides (e.g., earthquake and rainfall), each requiring a different kind of sensor.In this project, the team is building a landslide detection system, called LITMUS, that integrates data from two physical sensors -- USGS Global Seismographic Network (GSN), NASA Tropical Rainfall Monitoring Mission (TRMM) -- with data from pervasive social media platforms. This integration of multiple heterogeneous sensors in LITMUS is an illustrative example of successfully applying big data software tools and analytics techniques to solve real-world problems. Specifically, the team is extending geo-tagging to relevant data items, which are filtered in several stages to reduce noise and false positives, and applying machine learning, information retrieval, and semantic web techniques to each data stream. Finally, filtered social media data are being cross-referenced with physical events from the same geo-location to generate supporting evidence for landslide detection. A LITMUS prototype has been detecting more landslides around the world than traditional landslide reporting systems: tests with live streaming data show that the combined result is a list of landslide events that has included the USGS authoritative list, plus many other confirmed landslides around the world.

自然灾害和人为灾害可能造成重大物质损失和人类痛苦。例如，2012 年的超级风暴桑迪估计在 7 个国家造成了超过 680 亿美元的损失，并造成至少 286 人死亡。改善灾难的准备、响应和恢复可以减少损失、减轻人类痛苦并加快恢复速度。在灾害中，多重灾害是指一系列灾害，其中第一个灾害导致后续灾害，使得应急响应团队处理所有灾害变得更加困难。例如，2011年3月11日，日本东北部地震引发了史无前例的海啸，导致福岛第一核电站发生洪水和部分熔毁。多灾害的一个更常见的例子是山体滑坡，它可以由多种原因引发，包括地震、降雨和人为环境变化。而单一灾害的检测通常只需要一种专用传感器，例如地震仪为了可靠地检测地震，多种灾害通常需要组合各种传感器来检测序列中的多个事件。事实上，由于涉及多种类型的事件和大量的组合，使得离线组合分析变得不切实际，因此一般情况下的多事件和特别是多危害的检测是一个不平凡的问题。就山体滑坡而言，由于山体滑坡的几种可能且不相关的原因（例如地震和降雨），其检测变得更加复杂，每种原因都需要不同类型的传感器。在这个项目中，该团队正在构建一个山体滑坡检测系统，称为LITMUS，它将来自两个物理传感器——美国地质调查局全球地震网络（GSN）、美国宇航局热带降雨监测任务（TRMM）——的数据与来自普遍社交媒体平台的数据集成在一起。 LITMUS 中多个异构传感器的集成是成功应用大数据软件工具和分析技术来解决现实世界问题的说明性示例。具体来说，该团队正在将地理标记扩展到相关数据项，这些数据项会分几个阶段进行过滤，以减少噪音和误报，并将机器学习、信息检索和语义网络技术应用于每个数据流。最后，经过过滤的社交媒体数据与来自同一地理位置的物理事件进行交叉引用，以生成山体滑坡检测的支持证据。与传统的山体滑坡报告系统相比，LITMUS 原型在世界各地检测到更多的山体滑坡：实时流数据测试表明，综合结果是一份山体滑坡事件列表，其中包括 USGS 权威列表，以及世界各地许多其他已确认的山体滑坡。