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Regional scale landslide susceptibility analysis using different GIS-based approaches

Regional scale landslide susceptibility analysis using different GIS-based approaches

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Regional scale landslide susceptibility analysis using different GIS-based approaches

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  1. Miloš MarjanovićDepartment of Geoinformatics milos.marjanovic01@upol.cz Palacký University, Olomouc Regional scale landslide susceptibility analysis using different GIS-based approaches Project: Methods of artificial intelligence in GIS

  2. area methods materials results conclusion intro Presentation outline

  3. 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

  4. 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)

  5. 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)

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. area methods materials results conclusion intro AHP CP

  12. area methods materials results conclusion intro • SVM • 5% of original data • 10% of original data • 15% of original data NEW!

  13. area methods materials results conclusion intro

  14. 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

  15. Miloš MarjanovićDepartment for Geoinformatics milosgeomail@yahoo.com Palacký University, Olomouc Thank you for your attention!