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

自然灾害和人为灾害可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七个国家造成了超过680亿美元的损失，造成至少286人死亡。加强灾害的准备、应对和恢复，可以减少损失，减轻人们的痛苦，加快恢复。在灾害中，多重危险是指第一个灾害导致后续灾害的一系列灾害，这使得应急小组更难处理所有这些灾害。例如，2011年3月11日，日本东北地震引发了一场史无前例的海啸，导致福岛第一核电站发生洪水，部分熔毁。多灾种比较常见的例子是滑坡，它可以由地震、降雨和人为环境变化等多种原因触发。虽然单一灾害的检测通常只需要一种专用传感器，例如地震仪可以可靠地检测地震，但多灾情往往需要各种传感器的组合来检测序列中的多个事件。事实上，由于涉及的各种事件和大量组合使得离线组合分析不切实际，因此检测总体上的多个事件，特别是多个危险是一个不平凡的问题。在滑坡的情况下，它们的检测由于几个可能的和不相关的滑坡原因(例如地震和降雨)而变得更加复杂，每个原因都需要不同的传感器。在这个项目中，该团队正在建立一个名为Litmus的滑坡检测系统，它将来自两个物理传感器的数据--美国地质调查局全球地震网络(GSN)和美国国家航空航天局(NASA)热带降雨监测任务(TRMM)--与普遍存在的社交媒体平台的数据相结合。石蕊中多个异质传感器的集成是成功应用大数据软件工具和分析技术解决现实问题的一个说明性例子。具体地说，该团队正在将地理标记扩展到相关数据项，这些数据项分几个阶段进行过滤，以减少噪音和误报，并将机器学习、信息检索和语义网技术应用于每个数据流。最后，经过过滤的社交媒体数据正在与来自同一地理位置的物理事件进行交叉参考，以生成滑坡检测的支持证据。与传统的滑坡报告系统相比，Litmus原型已经在世界各地检测到更多的滑坡：使用实时流数据进行的测试表明，综合结果是一份滑坡事件清单，其中包括美国地质勘探局的权威清单，以及世界各地许多其他已证实的滑坡。

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.