| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | May 6, 2014 |
| Latest Amendment Date: | June 29, 2015 |
| Award Number: | 1402266 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Marilyn McClure
mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | May 1, 2014 |
| End Date: | April 30, 2017 (Estimated) |
| Total Intended Award Amount: | $300,000.00 |
| Total Awarded Amount to Date: | $308,000.00 |
| Funds Obligated to Date: |
FY 2015 = $8,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
GA US 30332-0280 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CSR-Computer Systems Research |
| Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Disaster management is a difficult task due to the unpredictable alterations of the environment by a disaster such as an earthquake and tsunami. Although some physical sensor networks can provide detailed information on specific kinds of disasters (e.g., USGS Global Seismographic Network on earthquakes), there are many disasters for which very little physical information is available. For example, landslides often are triggered by other disasters such as earthquakes and heavy rainfall, but there are few physical landslide detectors for landslides.
A promising approach to obtaining sufficient information to handle unpredictable environmental alterations is the integration of social network information (e.g., Twitter) with physical sensor data. This project conducted successful research on Multi-Source Integration (MSI) of disparate big data sources presents significant systems level and data analytics research and development challenges. Applying the MSI approach, our team developed a software toolkit to enable the initial integration of physical and social sensor networks and support the evolution of the participating sources in the integrated big data service. Using the toolkit, we developed the LITMUS landslide information service, which runs continuously as a live demo in our web portal. LITMUS is collecting live information on landslides around the world and available for researchers and the public with near-real-time information on landslides. LITMUS currently combines near real-time information from the USGS earthquake monitoring system, the NASA TRMM rainfall monitoring system, and social networks such as Twitter, Facebook, and YouTube. To the best of our knowledge, LITMUS is the most comprehensive real-time information service on worldwide landslides currently available.
The first version of LITMUS integrates physical sensor data with social media data in English. Soon we realized that the information on landslides in many (non-English speaking) countries are primarily reported in their local languages. We have been collecting landslide-related social media data in several languages, including Japanese, Chinese, Portuguese, and Russian, and extended the MSI approach to integrate data from multiple languages. Our currently ongoing research concerns an evaluation of machine learning filters created by developers fluent in those languages compared to translations from the current generation of automated translators such as Bing Translate and Google Translate. The results show a slight advantage of native filters, but good quality results by automated translators as well.
Last Modified: 07/08/2017
Modified by: Calton Pu
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