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Marjanović, M: Advanced Landslide Assessment of the Halenkovice Experimental Site
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Marjanović, M: Advanced Landslide Assessment of the Halenkovice Experimental Site

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  • 1. Advanced Landslide Assessment of the Halenkovice Experimental Site Miloš MarjanovićThis presentation is co-financed by theEuropean Social Fund and the statebudget 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 landslidee.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 0.1025Mean absolute error 0.1183Root mean squared error 0.344Relative 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-landslide300020 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 theThank You For Your Attention! Halenkovice Experimental Site Miloš Marjanović milos.marjanovic01@upol.czThis presentation is co-financed by theEuropean Social Fund and the statebudget of the Czech Republic