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DSD-INT 2019 Forecasting rainfall-induced landslides in the face of climate change - Santinelli

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Presentation by Giorgio Santinelli, Deltares, at the Data Science Symposium, during Delft Software Days - Edition 2019. Thursday, 14 November 2019, Delft.

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DSD-INT 2019 Forecasting rainfall-induced landslides in the face of climate change - Santinelli

  1. 1. Forecasting rainfall-induced landslides in the face of climate change Giorgio Santinelli, Faraz S. Tehrani, Meylin H. Herrera D a t a S c i e n c e S y m p o s i u m 2 0 1 9
  2. 2. Detection and Forecasting • Landslides are destructive and recurrent events • Natural and Human factor • Detection, Prediction, Risk assessment, and Mitigation 2 DataScienceSymposium2019
  3. 3. Landslide Forecasting • Global landslide hazard maps • Improving awareness and hazard understanding • Early warning • Emergency response 3 DataScienceSymposium2019
  4. 4. Global Landslide Catalogue 4 235 landslides! • NASA • 11,033 landslides • 2007 – 2018 • Based on media Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52(3), 561-575. DataScienceSymposium2019
  5. 5. Precipitation • 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 DataScienceSymposium2019 5 day 0day -1day -2day -3…day -10 short-termlong-term
  6. 6. Digital Elevation Model (DEM) • 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) DataScienceSymposium2019 6Elevation relief = Elevationmax – Elevationmin
  7. 7. Soil • SoilGrids • 2017 • 250 m x 250 m • • Sand fraction • Silt fraction • Clay fraction DataScienceSymposium2019 7 Hengl, T., de Jesus, J. M., Heuvelink, G. B., Gonzalez, M. R., Kilibarda, M., Blagotić, A., ... & Guevara, M. A. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748.
  8. 8. Vegetation • 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 8 DataScienceSymposium2019
  9. 9. Machine Learning • Logistic Regression • a supervised classification algorithm • returns a probability value • maps to two or more discrete classes DataScienceSymposium2019 9 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= + + + + + 0 1 2 3 : 1 rain slope relief other controling factorsn Example x x x x x = = = = =
  10. 10. DataScienceSymposium2019 10 Results Example set/ Features E0 E1 E2 E3 E4 E5 E6 E7 E8 x1 Short-term rain 1 1 1 1 1 1 1 1 1 x2 Long-term rain 0 0 0 1 1 1 1 1 1 x3 Mean slope 0 1 0 0 0 1 1 1 1 x4 Elevation relief 0 0 1 0 1 0 1 1 1 x5 NDVI 0 0 0 0 1 1 0 1 1 x6 Soil and bedrock 0 0 0 0 1 1 1 0 1
  11. 11. Example: LEWS for Jamaica DataScienceSymposium2019 11
  12. 12. Example: LEWS for Jamaica DataScienceSymposium2019 12
  13. 13. Example: LEWS for Jamaica D a t a S c i e n c e S y m p o s i u m 2 0 1 9 13 St. Thomas, Jamaica DataScienceSymposium2019
  14. 14. Example: LEWS for Jamaica 14 April 1, 2017 DataScienceSymposium2019
  15. 15. Example: LEWS for Jamaica 15 April 6, 2017 DataScienceSymposium2019
  16. 16. Example: LEWS for Jamaica 16 April 11, 2017 DataScienceSymposium2019
  17. 17. Example: LEWS for Jamaica 17 April 16, 2017 DataScienceSymposium2019
  18. 18. Example: LEWS for Jamaica 18 April 21, 2017 DataScienceSymposium2019
  19. 19. Example: LEWS for Jamaica 19 April 26, 2017 DataScienceSymposium2019
  20. 20. Landslide Detection • Landslide inventory • Automated detection • Use of EO imagery and EO-data products • Pre-processing • Image Segmentation • Image Classification 20 DataScienceSymposium2019
  21. 21. Band Rationing Post-event Image Subtraction Pre-event Post-event Pre-event DataScienceSymposium2019 Pre-processing 21
  22. 22. Image segmentation (OBIA) Spectral 200m Clewley et al., 2014 + Spatial + Textural DataScienceSymposium2019 22
  23. 23. numClusters (K) : Optimal number of clusters calculated using the Elbow Method Segmentation algorithm Shepherd et al. (2014) : K-means Implementation Segmentation parameters DataScienceSymposium2019 23
  24. 24. Image Segmentation Ratio RG from Image difference: RGD DataScienceSymposium2019 200m Malaysia, 2017 Sierra Leone, 2017 500m 24
  25. 25. DataScienceSymposium2019 Image Segmentation 25
  26. 26. DataScienceSymposium2019 26 Image Classification
  27. 27. Features Table DataScienceSymposium2019 27
  28. 28. Merging algorithm • Region Growing 28 D a t a S c i e n c e S y m p o s i u m 2 0 1 9 DataScienceSymposium2019
  29. 29. DataScienceSymposium2019 29 Optimization NDVI • VID • RGD • Solving imbalanced dataset per segment
  30. 30. Confusion matrix DataScienceSymposium2019 30 • Random Forest • Bootstrap aggregation and optimized hyperparameters: • Approx. 50 decision trees, classweights of 1:5 (higher weight to the minority class), random selection of features at each split, and a maximum tree depth of 40. • Class ratio not smaller than 1:14 (landslides:non-landslides), thus less imbalanced
  31. 31. Confusion matrix DataScienceSymposium2019 31 • After hyperparameter tuning… • model achieved a • precision of 83% • recall of 83% • f1-score of 83%
  32. 32. DataScienceSymposium2019 32 Validation New Zealand• The Netherlands • New Zealand
  33. 33. Tools • Publicly available data from imagery to datasets • Open source technologies allowing its applicability, re-usability, testing and improvement DataScienceSymposium2019 33 Storage Google fusion tables Pre-processing Processing Visualization PostgreSQL Google Earth Engine RSGISLib
  34. 34. Summary and Conclusion • A database was created for global rainfall-induced landslides. • Preliminary analysis showed that rainfall-induced landslides can be predicted with a fair accuracy. • Accuracy and resolution of the data is important and must be improved. • 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. • Landslide detection gives promising results. • The study helps assist the detection of landslides and improve time-consuming and costly methods. • Satellite optical images acquired from different areas and specific triggering factor. DataScienceSymposium2019 34
  35. 35. Acknowledgment • Ferdinand Diermanse (Extreme Weather Program of Deltares) • Robert McCall (Extreme Weather Program of Deltares) • Faraz S. Tehrani (Landslide forecasting and detection) • Meylin Herrera (Landslide Detection and Database) • Hélène Boisgontier (w-flow for Jamaica) 35 DataScienceSymposium2019

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