Regional scale landslide susceptibility analysis using different gis based approaches
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Miloš Marjanović Department of Geoinformatics m ilos .[email protected] 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

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Regional scale landslide susceptibility analysis using different gis based approaches

Miloš MarjanovićDepartment of Geoinformatics

[email protected] 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


Regional scale landslide susceptibility analysis using different gis based approaches

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


Regional scale landslide susceptibility analysis using different gis based approaches

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)


Regional scale landslide susceptibility analysis using different gis based approaches

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)


Regional scale landslide susceptibility analysis using different gis based approaches

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


Regional scale landslide susceptibility analysis using different gis based approaches

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


Regional scale landslide susceptibility analysis using different gis based approaches

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


Regional scale landslide susceptibility analysis using different gis based approaches

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


Regional scale landslide susceptibility analysis using different gis based approaches

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


Regional scale landslide susceptibility analysis using different gis based approaches

area

methods

materials

results

conclusion

intro

AHP

CP


Regional scale landslide susceptibility analysis using different gis based approaches

area

methods

materials

results

conclusion

intro

  • SVM

  • 5% of original data

  • 10% of original data

  • 15% of original data

NEW!


Regional scale landslide susceptibility analysis using different gis based approaches

area

methods

materials

results

conclusion

intro


Regional scale landslide susceptibility analysis using different gis based approaches

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


Thank you for your attention

Miloš MarjanovićDepartment for Geoinformatics

[email protected] University, Olomouc

Thank you for your attention!


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