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DSD-INT 2018 Global Landslides analysis and forecasting - Tehrani

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Presentation by Faraz Tehrani (Deltares) at the Data Science Symposium 2018, during Delft Software Days - Edition 2018. Thursday 15 November 2018, Delft.

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DSD-INT 2018 Global Landslides analysis and forecasting - Tehrani

  1. 1. Forecasting Rainfall-Induced Landslides Faraz S. Tehrani, Ph.D. Deltares Delft University of Technology Data Science Symposium, Deltares, Delft, November 201815 november 2018
  2. 2. Acknowledgement • Team Giorgio Santinelli, Gennadii Donchyts, Meylin Herrera • Programs • Impact of Extreme Weather Ferdinand Diermanse & Robert McCall • Urban Engineering Jelle Buma & Mandy Korff 15 november 2018 2
  3. 3. Outline  Background  Data  Prediction  Ongoing activities 15 november 2018 3
  4. 4. Landslide research plan 15 november 2018 4 Forecasting Post-hazard Analysis Risk Assessment Mitigation Background Data Prediction Current activities
  5. 5. Landslide research plan 15 november 2018 5 Forecasting Post-hazard Analysis Risk Assessment Mitigation Background Data Prediction Current activities • Global • Regional • Local • Site-specific
  6. 6. Landslide Forecasting 15 november 2018 6 Background Data Prediction Current activities • Improving awareness and hazard understanding • Global landslide susceptibility maps • Early warning • Emergency response Target susceptibility map Datasets Global landslide inventory Rainfall Digital Elevation Models Soil Vegetation Index Temperature Soil moisture Lithology Land cover Drainage network Road network Tehrani & Santinelli (2019)
  7. 7. 15 november 2018 7 Database Herrera (2018) • PostgreSQL / PostGIS database with all relevant data Landslide eventsLandslide_id Landcover Lat, Lon Landslide_id SoilMoisture Lat, Lon Landslide_id Rainfall Lat, Lon Landslide_id VegetationIndex Lat, Lon Landslide_id Topography Lat, Lon Landslide_id Temperature Lat, Lon Landslide_id Lat, Lon Join Join Join Join Join Background Data Prediction Current activities
  8. 8. Global Landslide Catalogue 15 november 2018 8 Background Data Prediction Current activities • NASA • 10,988 landslides • 2007 – 2018 • Based on media Herrera (2018)
  9. 9. Global Landslide Catalogue 15 november 2018 9 Background Data Prediction Current activities 4542 landslides to be used • NASA • 10,988 landslides • 2007 – 2018 • Based on media
  10. 10. PERSIANN CDR • 1983-Present • 0.25° x 0.25° TRMM 3B42 (Daily) • 0.25° x 0.25° • 1998-Present TRMM 3B43 (Monthly) • 0.25° x 0.25° • 1998-Present 15 november 2018 10 day 0day -1day -2day -3…day -10 short-termlong-term Background Data Prediction Current activities Precipitation Tehrani et al. (2019)
  11. 11. Shuttle Radar Topography Mission (SRTM1) • 2000 • 1ʺ× 1ʺ (approximately 30 m × 30 m) Advanced Land Observing Satellite (ALOS) • 2011 • 1ʺ× 1ʺ (approximately 30 m × 30 m) Multi-Error-Removed Improved-Terrain (MERIT) • 2017 • 3ʺ× 3ʺ (approximately 90 m × 90 m) 15 november 2018 11 Digital Elevation Models Elevation relief = Elevationmax – Elevationmin Tehrani et al. (2019) Background Data Prediction Current activities
  12. 12. 15 november 2018 12 Soil & Bedrock SoilGrids • 2017 • 250 m x 250 m • Depth to the bedrock • Sand fraction • Silt fraction • Clay fraction Hengl et al. (2017) Tehrani et al. (2019) Background Data Prediction Current activities [cm]
  13. 13. 15 november 2018 13 Vegetation Index Tehrani et al. (2019) Normalized Difference Vegetation Index ( -1<NDVI<+1) • Distinct colors (wavelengths) of visible and near-infrared sunlight reflected by the plants • Green leaves strongly absorb visible light and reflect near-infrared light NDVI = (NIR — VIS)/(NIR + VIS) MOD13Q1 v.6 • 2000-Present • 250 m x 250 m Background Data Prediction Current activities
  14. 14. 15 november 2018 14 Machine Learning Background Data Prediction Current activities Logistic Regression • a classification algorithm • returns a probability value • maps to two or more discrete classes 1 ( ) 1 z p z e   0 0 1 1 2 2 3 4 n nz w x w x w x w x w x     
  15. 15. 15 november 2018 15 Datasets Background Data Prediction Current activities x0 x1 x2 x3 x4 x5 E0 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 Short-term rain: Accumulated rain for the day prior to and the day of landslide Long-term rain: Accumulated rain for days ranging from 10 days to 2 days before landslide Elevation relief: The difference between maximum and minimum elevation Slope: Average slope of at the location of landslide NDVI before landslide Sand fraction, Silt fraction, Clay fraction and depth ro bedrock Non-Landslide Cases Rain: Noise was only applied to rainfall data DEM: Noise was only applied to topography data All: Noise was applied to both rainfall and topography features 9084 Landslide/Non-Landslide cases
  16. 16. 15 november 2018 16 Datasets Background Data Prediction Current activities x0 x1 x2 x3 x4 x5 E0 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 Short-term rain: Accumulated rain for the day prior to and the day of landslide Long-term rain: Accumulated rain for days ranging from 10 days to 2 days before landslide Elevation relief: The difference between maximum and minimum elevation Slope: Average slope of at the location of landslide NDVI before landslide Sand fraction, Silt fraction, Clay fraction and depth ro bedrock
  17. 17. 15 november 2018 17 Datasets Background Data Prediction Current activities x0 x1 x2 x3 x4 x5 E0 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 Short-term rain: Accumulated rain for the day prior to and the day of landslide Long-term rain: Accumulated rain for days ranging from 10 days to 2 days before landslide Elevation relief: The difference between maximum and minimum elevation Slope: Average slope of at the location of landslide NDVI before landslide Sand fraction, Silt fraction, Clay fraction and depth ro bedrock
  18. 18. 15 november 2018 18 Datasets Background Data Prediction Current activities x0 x1 x2 x3 x4 x5 E0 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 Short-term rain: Accumulated rain for the day prior to and the day of landslide Long-term rain: Accumulated rain for days ranging from 10 days to 2 days before landslide Elevation relief: The difference between maximum and minimum elevation Slope: Average slope of at the location of landslide NDVI before landslide Sand fraction, Silt fraction, Clay fraction and depth ro bedrock
  19. 19. 15 november 2018 19 Landslide detection Background Data Prediction Current activities MSc Thesis Project (Meylin Herrera): Landslide detection using Machine Learning with application in landslide susceptibility mapping Mud Creek, California, 20/05/2017
  20. 20. 15 november 2018 20 Landslide detection Background Data Prediction Current activities MSc Thesis Project (Meylin Herrera): Landslide detection using Machine Learning with application in landslide susceptibility mapping Mud Creek, California, 20/05/2017 Optical Radar
  21. 21. 15 november 2018 21 Landslide detection Background Data Prediction Current activities Pixel-based or OBIA Training Yes Adjust parameters • Satellite Imagery: Sentinel-2 /Landsat- 8 • DEM • NDVI • Landslide Inventory (Franeltalia, 2018) Triggering factor: • Rainfall data Controlling factors: • Topography from DEM (Slope, height, aspect) • NDVI • Soil moisture • Soil Composition • Landcover • Temperature Test INPUT ML Accuracy Assessment Testing Good overall accuracy ? Areas with data scarcity No generateLandslide Inventory maps OUTPUT (LSM) Input Data Input Data Susceptibility Mapping (LSM) Landslide Detection (LD) Validation Herrera (2019?)
  22. 22. Some remarks  A database was created for global rainfall-induced landslides  Preliminary analysis showed that rainfall-induced landslides can be predicted with a reasonable accuracy  More controlling features need to be added  Accuracy and resolution of the data is important and must be improved  Landslide detection algorithms will be developed  Global model is for “awareness” and as a first step towards regional and local predictions and planning  Climate scenarios can be applied to the model for global prediction of landslides in future 15 november 2018 22

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