Regional scale landslide susceptibility analysis using different GIS-based approaches

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 2

Presentation outline


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


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


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)


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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iii XWS

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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

XLALPXLP

W iiiii

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iiWLS

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ln


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


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


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


resultsresults

AHPCP


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!


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)


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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Regional scale landslide susceptibility analysis using different GIS-based approaches