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788.11J Presentation Landslide Prediction/Detection

Presented by Zhimin Yang Presentation on the work of researchers at. 788.11J Presentation Landslide Prediction/Detection. A landslide is an event where a block of earthen mass slides downhill covering the area underneath with dirt and debris. Landslides are a major geologic hazard in the U.S

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788.11J Presentation Landslide Prediction/Detection

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  1. Presented by Zhimin Yang Presentation on the work of researchers at 788.11J PresentationLandslide Prediction/Detection

  2. A landslide is an event where a block of earthen mass slides downhill covering the area underneath with dirt and debris. Landslides are a major geologic hazard in the U.S 25 to 50 fatalities per year $1B to $3B property damages Landslides

  3. The main idea • Detect and estimate displacements before catastrophic event[1][2] for Finite Element Analysis Model to process • Use strain measurement to predict landslide[3]

  4. System Architecture

  5. Sensor column • Stargate Gateway • Connected to peripherals placed at different lengths of a flexible tube • Geophones • Measure distance from seismic signal sources • Pore Pressure Transducer & Reflectometer • Measure positive and negative pressures and moisture content (si)–Strain • Gauges • Detect tube deformation • Other sensors are powered off until triggered by Strain Gage • Gateway is above ground rest of the column is below ground

  6. The main achievements • Landslide prediction system architecture • Landslide prediction algorithm[2] • Detection, Classification, Localization and Estimation • Threshold Based Detection and Statistical Detection Algorithms[3] • Centralized Vector Based Detection • Distributed Vector Based Detection with Independent Clusters • Distributed Scalar Based Detection with Independent Nodes • Distributed Vector Based Detection with Independent Nodes • Simulations[2][3]. Virtual landslide on 2-D virtual hill simulations[2] (more convincing).

  7. The challenges • Reliably detect and estimate small displacements • Determine columns that moved • Estimate new locations of dislocated columns • Estimate location of slip surface • 3-D localization • Eliminate false alarms (positive/negative)

  8. Innovation • New systems application • Can we simplify the system by using localization of sensor network? (without other types of sensors)

  9. References • [1]Terzis, A.; Anandarajah, A.; Moore, K.; Wang, I.-J., "Slip surface localization in wireless sensor networks for landslide prediction," Information Processing in Sensor Networks, 2006. IPSN 2006. The Fifth International Conference on , vol., no.pp. 109- 116, 19-21 April 2006 • [2]A. N. Sheth, K. Tejaswi, P. Mehta, C. Parekh, R. Bansal,S. Merchant, T. N. Singh, U. B. Desai, C. A. Thekkath, and K. Toyama. Poster Abstract, A Sensor Network Based Landslide Prediction System. In Proceedings of Sensys 2005, Nov. 2005. • [3]DISTRIBUTED DETECTION STRATEGIES FOR LANDSLIDE PREDICTION USING WIRELESS SENSOR NETWORKS, available at http://www.ee.iitb.ac.in/~prakshep/dds_lp.pdf

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