marjanović, m: advanced landslide assessment of the halenkovice experimental site
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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
e.g.
asp
ect
Methodology
e.g. slope
e.g.
asp
ect
landslide
stable
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!
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