This presentation is co-financed by the European Social Fund and the state budget of the Czech Republic

Advanced Landslide Assessment of the Halenkovice Experimental Site

Miloš Marjanović

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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

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)

Introduction

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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

Introduction

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Methods for data pre-processing and selection: Chi-square Entropy

Landslide modeling methods Deterministic, Heuristic, Statistical, Fuzzy, Machine Learning

Methods for data evaluation ROC plot Kappa-index

Methodology

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First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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

Methodology

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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support vectors

e.g. slope

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Experiment design

Methodology

SAGA

SAGA

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Experiment design Testing Cross-Validation Training

Methodology

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Study Area

Case Study Dataset

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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)

Case Study Dataset

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Case Study Dataset

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Case Study Dataset

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Case Study Dataset

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Case Study Dataset

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Case Study Dataset

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Thematic attributes Morphometric attributes Hydrological attributes Environmental attributes Geological attributes

Case Study Dataset

# attribute source

1 DEM Topo-maps

2 Slope DEM

3 Slope length DEM

4 Aspect DEM

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

14 Lithological units Geo-mapsnominal

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Attribute layers

Case Study Dataset

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Model accuracy

Case Study Results

=== Summary ===

Correctly Classified Instances 304080 = 88.16 %Incorrectly Classified Instances 40814 = 11.83 %Kappa statistic 0.1025Mean absolute error 0.1183Root 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 1Avg.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

Comparison with an earlier, non-predictive model based on multivariate regression

Case Study Results

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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

Conclusions

This presentation is co-financed by the European Social Fund and the state budget of the Czech Republic

Advanced Landslide Assessment of the Halenkovice Experimental Site

Miloš Marjanovićmilos.marjanovic01@upol.cz

Thank You For Your Attention!