regional scale landslide susceptibility analysis using different gis based approaches
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Miloš Marjanović Department of Geoinformatics m ilos [email protected] Palack ý University, Olomouc. Regional scale landslide susceptibility analysis using different GIS-based approaches. Project: Methods of artificial intelligence in GIS. area. methods. materials. results.

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regional scale landslide susceptibility analysis using different gis based approaches
Miloš MarjanovićDepartment of Geoinformatics

[email protected] Palacký University, Olomouc

Regional scale landslide susceptibility analysis using different GIS-based approaches

Project: Methods of artificial intelligence in GIS

presentation outline
area

methods

materials

results

conclusion

intro

Presentation outline
slide3
intro

area

methods

materials

results

conclusion

  • Landslide Susceptibility

likelihood of landslide occurrence over specified area or volume

  • Influence factors:
    • Triggering factors (earthquakes, rainstorms, floods etc.)
    • Natural terrain properties (lithology, relief etc.)
    • Human influence
  • Classification (Varnes, 1978)
  • Landslide mechanism (deep seated earth-slides, active & dormant)
  • Scale & detailedness
slide4
area

methods

materials

results

conclusion

intro

Fruška Gora Mountain, Serbia

  • Features (& relation to landslides)
    • Geology
    • Geomorphology
    • Hydrology
  • Landsliding history
    • 10% of the area estimated as unstable (6% dormant, 4% active, deap seated, hosted in pre-Quaternary formations)
slide5
area

methods

materials

results

conclusion

intro

  • Knowledge-driven modeling - Analytical Hierarchy Process (AHP)
  • Statistical modeling - Conditional Probability (CP)
  • Machine learning - Support Vector Machines (SVM)
  • Model evaluation measures:
      • Entropy
      • Certainty
      • Kappa-statistics
      • Area Under Curve (AUC of ROC)
slide6
area

methods

materials

results

conclusion

intro

  • Knowledge-driven modeling - Analytical Hierarchy Process (AHP)
      • Terrain attributes Xi (ranged into arbitrary class intervals)
      • Weights Wi based upon experts’ opinions
      • Addition
slide7
area

methods

materials

results

conclusion

intro

  • Statistical modeling - Conditional Probability (CP)
      • Terrain attributes Xi (ranged into arbitrary class intervals)
      • Density of landslide instances (within each class of each input terrain attribute) – Weight of Evidence
      • logit transformation and Sum
slide8
area

methods

materials

results

conclusion

intro

  • Machine learning - Support Vector Machines (SVM)
      • Classification task
      • Optimization
      • Training over sampling splits (referent data included)
      • Testing the rest of the dataset with trained classifier
slide9
area

methods

materials

results

conclusion

intro

  • Topographic maps 1:25000 (digitized to 30 m DEM)
  • Geological map 1:50000 (digitized to 30 m)
  • LANDSAT TM (bands 1-5, 2006 summer).
  • Geomorphological map 1:50000 (digitized to 30 m)
  • Arc GIS, SAGA GIS, Weka software
slide10
area

methods

materials

results

conclusion

intro

12 terrain attributes + referent landslide map:

  • Slope angle, Slope aspect, Slope length, Elevation, Slope curvature (profile and planar), Buffer of drainage network, Wetness Index
  • Lithological model, Buffer of geological boundaries, Buffer of regional structures, Referent landslide map
  • Land use map
slide11
area

methods

materials

results

conclusion

intro

AHP

CP

slide12
area

methods

materials

results

conclusion

intro

  • SVM
  • 5% of original data
  • 10% of original data
  • 15% of original data

NEW!

slide13
area

methods

materials

results

conclusion

intro

slide14
area

methods

materials

results

conclusion

intro

  • Concluding remarks and directives:
  • SVM surpassed AHP & CP by far (high performance)
  • Possible reduction of input data with similar sampling strategy
  • SVM has demanding data preparation and processing procedure
  • AHP & CP only for general insights, but GIS integrated
  • Postprocessing (smoothing out the apparent errors)
  • Preprocessing (selection of important attributes)
  • Testing on adjacent areas with incomplete data coverage
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