IAEG5-10th September 2010
Auckland, New Zealand
Regional scale landslide susceptibility analysis Regional scale landslide susceptibility analysis using different GIS-based approachesusing different GIS-based approaches
Miloš Marjanović Department of [email protected] Palacký University, Olomouc
Project: Methods of artificial intelligence in GIS
IAEG 2010, Auckland, New Zealand 3
introintro
• Landslide Susceptibilitylikelihood 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 Zealand 4
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)
areaarea
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methodsmethods
• 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)
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methodsmethods
• Knowledge-driven modeling - Analytical Hierarchy Process (AHP)
• Terrain attributes Xi (ranged into arbitrary class intervals)
• Weights Wi based upon experts’ opinions• Addition
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methodsmethods
• 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
AL
XL
ALP
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methodsmethods
• 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
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materialsmaterials
• 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
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materialsmaterials
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
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resultsresults
SVM• 5% of original data• 10% of original data• 15% of original data
method κ-index κI* κII* κIII* AUC
SVMSET1 0.57 0.56 0.62 0.55 0.79
SVMSET2 0.67 0.66 0.68 0.68 0.82
SVMSET3 0.73 0.70 0.76 0.76 0.84NEW!
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resultsresults
AHP
entropy certainty κ-index
conditional κ-index (for each class)
1.51 0.20very low low moderate high very high
0.48 0.69 0.33 0.39 0.57 0.62CP
entropy certainty
1.22 0.55
0.34 0.51 0.28 0.25 0.32 0.35SVM
κ-index auc
0.62 0.82
0.29 0.44 0.19 0.17 0.22 0.30(AHP)
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conclusionconclusion
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
IAEG5-10th September 2010
Auckland, New Zealand
Thank you for your attention!Thank you for your attention!
Miloš Marjanović Department for [email protected] Palacký University, Olomouc
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