IAEG 5-10 th September 2010 Auckland, New Zealand Regional scale landslide susceptibility analysis using different GIS-based approaches Miloš Marjanović Department of Geoinformatics University, Olomouc Project: Methods of artificial intelligence in GIS

IAEG 2010, Auckland, New Zealand2 Presentation outline

IAEG 2010, Auckland, New Zealand3intro 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

IAEG 2010, Auckland, New Zealand4 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)area

IAEG 2010, Auckland, New Zealand5methods 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)

IAEG 2010, Auckland, New Zealand6methods Knowledge-driven modeling - Analytical Hierarchy Process (AHP) Terrain attributes X i (ranged into arbitrary class intervals) Weights W i based upon experts’ opinions Addition

IAEG 2010, Auckland, New Zealand7methods Statistical modeling - Conditional Probability (CP) Terrain attributes X i (ranged into arbitrary class intervals) Density of landslide instances (within each class of each input terrain attribute) – Weight of Evidence logit transformation and Sum

IAEG 2010, Auckland, New Zealand8methods 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

IAEG 2010, Auckland, New Zealand9materials 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

IAEG 2010, Auckland, New Zealand10materials 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

IAEG 2010, Auckland, New Zealand11results AHP CP

IAEG 2010, Auckland, New Zealand12results SVM 5% of original data 10% of original data 15% of original data methodκ-indexκI*κI*κ II *κ III *AUC SVM SET SVM SET SVM SET NEW!

IAEG 2010, Auckland, New Zealand13results AHP entropycertainty κ-index conditional κ-index (for each class) very lowlowmoderatehighvery high CP entropycertainty SVM κ-indexauc (AHP)

IAEG 2010, Auckland, New Zealand14conclusion 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

IAEG 5-10 th September 2010 Auckland, New Zealand Thank you for your attention! Miloš Marjanović Department for Geoinformatics University, Olomouc