UNIVERSITÀ DEGLI STUDI Non-linearity and spatial DI MILANO ... 09...15/09/2009 1 Non-linearity and...

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15/09/2009 1

Non-linearity and spatial correlation in landslide susceptibility mapping

C. Ballabio, J. Blahut, S. Sterlacchini

University of Milano-Bicocca

GIT 2009

UNIVERSITÀ DEGLI STUDIDI MILANO - BICOCCA

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Summary

Landslide susceptibility modeling

Non-linearity issues

Few examples

Application to a case study

Modeling the residual spatial correlation

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IntroductionUNIVERSITÀ DEGLI STUDIDI MILANO - BICOCCA

Landslide susceptibility modeling

Usually defined as a classification problem: if y=1 is an observed occurrence and y=0 is a point with no occurrence, and x is a series of variables, then we want to know:

P(y=1|x)=f(x, θ)

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-2 0 2 4 6

Linearly separable classesJust find a separating line/plane/hyper-plane

Introduction

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Exactly what LDA, QDA and LR doesUNIVERSITÀ DEGLI STUDIDI MILANO - BICOCCA

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Even for linearly separable classes the best function could be not linear

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-1.0 -0.5 0.0 0.5 1.0

What if the separation can not be performed by linear functions?How can we separate the two classes by using only X1 and X2?

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LDA does not work…

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LDA does not work…

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Neither does QDA…

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Even far more flexible models fail to separate the classes…

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ANNs get close to do the job, but require a lot of tuning…

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Support VectorMachines

Based on the Statistical Learning Theory (Vapnik, 1995)Very good performance in classification tasksIntrinsic “Occam’s razor” logic: the simplest model is preferredEasy to avoid overfittingNot so “Black-box”

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Support VectorMachines

Widely used in machine learningBioinformatics / genetic classificationSpatial mappingRoboticsDigital soil mapping

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Support vectorclassification

Use the best hyperplaneUse the “kernel trick”

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-2 0 2 4 6

Which is the best hyperplane?We need a way to define what “optimal separation” is…

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-2 0 2 4 6

Find the widest gap between classesFit a plane in the middle of the gap

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Kernel Function

The kernel linearize the data in an high dimensional spaceMakes possible to find a flat separating hyperplane

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Kernel Function

The kernel linearize the data in an high dimensional spaceMakes possible to find a flat separating hyperplane

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Kernel Function

Based on the dot product:

Simple to elaborateBut very powerful, can project data in high dimensional spaces: Reproducing Kernel Hilbert Spaces (RKHS)But… it is not known beforehand which kernel is appropriate…

iiiii xxxx

1]'[][',

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Kernels

Polynomial:

Linear:

Radial basis function:

dii xxxxK ,),( =

⎟⎟⎠

⎞⎜⎜⎝

⎛ −−= 2

2exp),(

ixxxxK

Exponential RBF: ⎟⎠⎞

⎜⎝⎛ −−= 22

exp),(σ

ii xxxxK ,),( =

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SVM with Single Gaussian kernelSeparates the classes almost perfectlyReproduces the general trend of the data

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The Stafforabasin study areaTriggering patterns for flowsDEM derived covariates + geology and landuse

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1510000

1520000

0 1 2 3 4 5 Kilometers

LegendNB ProbabilityValue

High : 1

Low : 0Naïve Bayes(≈WoE) predictionNot bad, but we got a lot of high probability areas

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LDA (a.k.a. Maximum Likelihood) predictionBetter than NB, but we still get a lot of high probabilities

1510000

1520000

LegendLDA ProbabilityValue

High : 1

Low : 0

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1510000

1520000

LegendSVM ProbabilityValue

High : 1

Low : 0SVMJust better…

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Use cross-validation and ROC curves to compare the models

Far less false positivesPredicted in the cross-validationsample

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Success curves (Fabbri and Chung, 2003)It’s a ROC with only true positives rate…

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Why use MachineLearning?

Increasing availability of low cost / high information topographic surveys

i.e. LiDAR, Hyper-spectral data

A lot raw derived information

A ML system can automatically interpreter the data without the need of refinements (automatic mapping systems).

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What happened if we use only DEM derived data?We still get a decent prediction from SVM, but not from LR/LDA/NB

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distance

200 400 600 800

Once we predict with SVM can we derive useful information from the data?There is still a lot of autocorrelation al low distances

Residual spatial correlation

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500 1500 2500

detrended.occurrence

svm.pred.occurrence

500 1500 2500

occurrence

detrended.svm.pred

svm.pred

detrended

We can implement a Kriging system to model the residual informationOr, we can use MK-SVM to model spatial variation

Original trend

Residual correlation

Model correlation

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A monodimensionalexample

Combination ofcosine functionswith different λPlus some Additive Gaussian NoiseSample 30% of the data points

0 5 10 15 20 25 30

−0.4

0 5 10 15 20 25 30

−1.0

0 5 10 15 20 25 30

−1.5

−1.0

−0.5

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Multi-kernel analysis

Two gaussianRBF kernelsMK-SVR is able to separate the two signals, even in presence of noise.

0 5 10 15 20 25 30

−1.0

0 5 10 15 20 25 30

−1.0

0 5 10 15 20 25 30

−0.4

0 5 10 15 20 25 30

−0.1

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Spatial SVM performance

Within slope predicted probabilityAverage probability close to max probability

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Cross-validation performanceAverage prob. still close to max probability

UNIVERSITÀ DEGLI STUDIDI MILANO - BICOCCA Spatial SVM performance

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UNIVERSITÀ DEGLI STUDIDI MILANO - BICOCCA Spatial SVM rules extraction

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Conclusions

SVM clearly outperform most of the statistical techniques commonly applied for landslide susceptibility perdictionIt IS a “black box” technique, but not so much… several algorithms for feature selection and ranking are availableVery good for automatic and real time mappingCan easily update the model if new data is providedGood for automatic mapping systems

UNIVERSITÀ DEGLI STUDI Non-linearity and spatial DI MILANO ... 09...15/09/2009 1 Non-linearity and...

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