1. Advanced Landslide Assessment of the
Halenkovice Experimental Site
Miloš Marjanović
This presentation is co-financed by the
European Social Fund and the state
budget of the Czech Republic

2. Introduction
 Motifs:
 raising awareness
 need for diverse case studies at different
scales, using different methods
 applicability (decision making for land use planning and civil protection)
 Objectives:
 reliability and coherency of inputs (specially landslide inventory)
 performing advanced modeling (many different methods)
 evaluating models in the best fashion
 providing maps/models as final outputs to be used in
practical/scientific manner
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

3. Introduction
 Landslides – mass movements of the ground
 Landslide susceptibility – spatial probability
of landslide occurrence (relation to hazard, risk…)
 Setting definition:
 Classification after Varnes 1978 (defining the mechanism and typology)
 Scale/resolution (mid-scale, after Fell et all 2008)
 Raster format data structure, pixel resolution 10 m
 Definition of geometry (size, depth, area, frequency of landslides)
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

4. Introduction
 Problems & perspectives in landslide assessment
 lack of data, lack of possibility to relate events with triggers, non-
linearity of the problem…
 piling investigations, promising capacities for monitoring (ground
sensors and Remote Sensing) in the future
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

5. Methodology
 Methods for data pre-processing and selection: q n (ϕ oi , j − ϕ ei , j )2
Χ =∑ ∑
Chi-square
2
 i=1 j =1 ϕ ei , j
Entropy
k ni n
 E ( Sin ) = −∑
N
log 2 i
N
i =1
 Landslide modeling methods ADVANCED!
 Deterministic, Heuristic, Statistical, Fuzzy, Machine Learning
 Methods for data evaluation
 ROC plot
 Kappa-index
n n n
κ =( ∑
i =1
xii − ∑
i =1
yii ) /(1 − ∑y )
i =1
ii
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

6. Methodology
 Machine learning - Support Vector Machines
(SVM)
 Classification task
 Optimization (only two parameters)
 Training over sampling splits
 Testing the rest of the dataset with trained classifier
 Kernels
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

7. Methodology
support vectors
landslide
e.g. aspect
e.g. aspect
stable
e.g. slope e.g. slope
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

8. Methodology
 Experiment design
SAGA
SAGA
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

9. Methodology
 Experiment design
 Testing
 Cross-Validation
 Training
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

10. Case Study Dataset
 Study Area
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

11. Case Study Dataset
 Landslide Inventory
 CGS survey (1:10 000)
http://mapy.geology.cz/svahove_nestability/
 Field investigation
 Independent field survey
 Continuation from previous studies at the department
(Křivka, Marek, Bíl)
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

12. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

13. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

14. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

15. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

16. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

17. Case Study Dataset
 Thematic attributes # attribute source
 Morphometric attributes 1 DEM Topo-maps
2 Slope DEM
 Hydrological attributes 3 Slope length DEM
 Environmental attributes 4 Aspect DEM
 Geological attributes 5 Plan/profile curvature DEM
6 Convergence index DEM
7 Drainage elevations DEM
8 Elevation above drainage DEM
9 Drainage buffer DEM
10 LS factor DEM
11 TWI DEM
12 Catchment area DEM
13 Land cover units Orthophoto
nominal
14 Lithological units Geo-maps
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

18. Case Study Dataset
 Attribute layers
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

19. Case Study Results
 Model accuracy
=== Summary ===
Correctly Classified Instances 304080 = 88.16 %
Incorrectly Classified Instances 40814 = 11.83 %
Kappa statistic 0.1025
Mean absolute error 0.1183
Root mean squared error 0.344
Relative absolute error 75.3045 %
Root relative squared error 136.5789 %
Coverage of cases (0.95 level) 88.1662 %
Mean rel. region size (0.95 level) 50 %
Total Number of Instances 344894
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.932 0.823 0.941 0.932 0.936 0.103 0.555 0.94 0
0.177 0.068 0.156 0.177 0.166 0.103 0.555 0.082 1
Avg.0.882 0.773 0.889 0.882 0.885 0.103 0.555 0.883
=== Confusion Matrix ===
a b <-- classified as: a=non-landslide
300020 21980 | a=0 b=landslide
18834 4060 | b=1
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

20. Case Study Results
 Comparison with an earlier, non-predictive model based
on multivariate regression
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

21. Conclusions
 Overall:
 model seems promising, but there is room for improvements
 the study is in its beginning and it might be interesting to extend
it methodologically and to compare the results
 Drawbacks
 bad communication between GIS and Machine Learning platform
 time consumption
 For further notice:
 it is necessary to increase the number of folds in optimization
 it would be interesting to challenge the algorithm with multi-
class (multinomial) scenario
 post-procesing might be good refinement for the overall
accuracy First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

22. Advanced Landslide Assessment of the
Thank You For Your Attention!
Halenkovice Experimental Site
Miloš Marjanović
milos.marjanovic01@upol.cz
This presentation is co-financed by the
European Social Fund and the state
budget of the Czech Republic