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

自然和人为灾害可能造成重大的物质损失和人类痛苦。例如，据估计，2012年的超级风暴桑迪造成了680多亿美元的损失，并在7个国家造成至少286人死亡。改进灾害的准备、应对和恢复可以减少损失、减轻人类痛苦并加快恢复。在各种灾害中，多重灾害是指一系列灾害，其中第一个灾害导致随后的灾害，使应急小组更难以处理所有灾害。例如，2011年3月11日，日本东北部发生地震，引发了前所未有的海啸，导致福岛第一核电站发生洪水和部分熔毁。多灾种的一个更常见的例子是山体滑坡，它可以由许多原因引发，包括地震、降雨和人为的环境变化。虽然检测单个灾害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，但多灾种往往需要多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的组合使得离线组合分析不切实际，因此一般多事件和特别是多危险的检测是一个重要的问题。在滑坡的情况下，由于滑坡的几个可能和不相关的原因（例如，在这个项目中，该团队正在构建一个名为LITMUS的滑坡检测系统，该系统将来自两个物理传感器的数据-美国地质勘探局全球地震网络（GSN），美国宇航局热带降雨监测使命（TRMM）-与来自无处不在的社交媒体平台的数据相结合。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解决现实问题的一个说明性例子。具体来说，该团队正在将地理标记扩展到相关数据项，这些数据项在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，过滤后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，以生成滑坡检测的支持证据。LITMUS原型在全球范围内检测到的山体滑坡比传统的山体滑坡报告系统更多：实时流媒体数据测试显示，综合结果是一份包括美国地质勘探局权威列表在内的山体滑坡事件列表，以及世界各地许多其他已确认的山体滑坡。

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.