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Use Case DC6 - landslide prediction

by clagoze last modified Dec 01, 2010 12:38 PM

  • Goal
    • Prediction of urban area mud slides
  • Summary
    • Sustained heavy rains can cause devastating landslides, which may be triggered by seismic events.  Multiple data sources are needed to predict when and where landslides may occur. This use case describes research performed to improve landslide prediction for a particular urban area using data acquired from satellite, aircraft and in situ sensors as well as weather forecasts.
  • Queries

  • Operations/Tasks
      • Josephine searches the local library to find information on historical landslides that have affected the city. She acquires a soil type map from the regional office of the USGS. She then subcontracts to a local firm to generate a 10cm horizontal resolution digital elevation model (DEM) of the region around the city using scanning lidar. From these data she is able to identify potentially unstable slopes.  To predict when and where a landslide might occur she needs to monitor two potential triggers, which may act in tandem:  soil saturation and seismic activity.  For the former she accesses medium range weather forecasts from NOAA's National Centers for Environmental Prediction (NCEP) ftp site. For the latter she accesses real-time information from the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC) using the web service interface. She also monitors for slow slope motions by accessing interferometric synthetic aperture data (InSAR) and GPS data from UNAVCO, also accessed via web services.  She must be able to read lidar data in LAS format, NCEP predictions in GRIB format, seismographic data in SEED format, InSAR data in CEOS format and GPS data in RINEX format. Rainfall and seismograph data are fed into her model on a continual basis and if hazardous conditions arise she issues an alert to local authorities.  She also monitors the GPS data in real time and InSAR data as it becomes available.

  • Data sets and associated metadata
    • NOAA weather forecasts
    • Real-time information from the Incorporated Research Institutions for Seismology Data Management Center
    • interferometric synthetic aperture data
    • GPS data
  • Metrics of completion/success

  • Ontologies
    • LAS
    • GRIB
    • SEED
    • CEOS
    • RNEX
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