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

1. Forecasting rainfall-induced landslides in
the face of climate change
Giorgio Santinelli, Faraz S. Tehrani, Meylin H. Herrera
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2. Detection and Forecasting
• Landslides are destructive and recurrent events
• Natural and Human factor
• Detection, Prediction, Risk assessment, and Mitigation
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3. Landslide Forecasting
• Global landslide hazard maps
• Improving awareness and hazard understanding
• Early warning
• Emergency response
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4. Global Landslide Catalogue
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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.
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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
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day 0day -1day -2day -3…day -10
short-termlong-term

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)
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6Elevation relief = Elevationmax – Elevationmin

7. Soil
• SoilGrids
• 2017
• 250 m x 250 m
•
• Sand fraction
• Silt fraction
• Clay fraction
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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. 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
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9. Machine Learning
• Logistic Regression
• a supervised classification algorithm
• returns a probability value
• maps to two or more discrete classes
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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
=
=
=
=
=

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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. Example: LEWS for Jamaica
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12. Example: LEWS for Jamaica
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13. Example: LEWS for Jamaica
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St. Thomas, Jamaica
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14. Example: LEWS for Jamaica
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April 1, 2017
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15. Example: LEWS for Jamaica
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April 6, 2017
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16. Example: LEWS for Jamaica
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April 11, 2017
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17. Example: LEWS for Jamaica
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April 16, 2017
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18. Example: LEWS for Jamaica
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April 21, 2017
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19. Example: LEWS for Jamaica
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April 26, 2017
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20. Landslide Detection
• Landslide inventory
• Automated detection
• Use of EO imagery and EO-data products
• Pre-processing
• Image Segmentation
• Image Classification
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21. Band Rationing
Post-event
Image Subtraction
Pre-event
Post-event
Pre-event
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Pre-processing
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22. Image segmentation (OBIA)
Spectral
200m
Clewley et al., 2014
+ Spatial + Textural
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23. numClusters (K) : Optimal number of clusters calculated using the Elbow Method
Segmentation algorithm
Shepherd et al. (2014) : K-means Implementation
Segmentation parameters
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24. Image Segmentation
Ratio RG from Image difference: RGD
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200m
Malaysia, 2017
Sierra Leone, 2017 500m
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Image Segmentation
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Image Classification

27. Features Table
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28. Merging algorithm
• Region Growing
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Optimization
NDVI
• VID
• RGD
• Solving imbalanced dataset per segment

30. Confusion matrix
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• 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. Confusion matrix
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• After hyperparameter tuning…
• model achieved a
• precision of 83%
• recall of 83%
• f1-score of 83%

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Validation
New Zealand• The Netherlands
• New Zealand

33. Tools
• Publicly available data from imagery to datasets
• Open source technologies allowing its applicability, re-usability, testing and improvement
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Storage
Google fusion tables
Pre-processing Processing Visualization
PostgreSQL Google Earth Engine
RSGISLib

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.
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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)
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