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

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. 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. Outline
 Background
 Data
 Prediction
 Ongoing activities
15 november 2018 3

4. Landslide research plan
15 november 2018 4
Forecasting
Post-hazard
Analysis
Risk
Assessment
Mitigation
Background Data Prediction Current activities

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. 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. 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. Global Landslide Catalogue
15 november 2018 8
Background Data Prediction Current activities
• NASA
• 10,988 landslides
• 2007 – 2018
• Based on media
Herrera (2018)

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. 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. 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. 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. 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. 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 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. 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. 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. 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. 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. 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. 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. 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