GIS IN GEOLOGY Miloš Marjanović Lesson 5 4.11.2010.

GIS IN GEOLOGY

Miloš MarjanovićLesson 54.11.2010.

GIS in Landslide assessment (advanced)

1. Statistical analysis of landslide susceptibility/hazard/risk zonation

Comparing landslide occurrence from inventory or on-the-site data and input parameter relevance (weight, or rank according to the density of parameter classes) in the final model by different techniques of statistical dependancy

2. Deterministic models for landslide susceptibility/hazard/risk zonation

Coupling slope stability criteria (static equilibrium) and triggering factor(s) influence(s) in order to map where (& when) the triggering factor of certain intensity overcomes the soil/rock strength, causing the slope failure

Accent on advances in modeling approaches as research level upgrades and upscales

Geostatistics

Desktop and Web

publishing

Desktop mapping

Database Management Systems

(DBMS)

Image Processing

(IP)

Computer Aided

Drawing (CAD)

Contouring and surface modeling

Artificial Intelligence(AI)

General statistics

Spread- sheets

Geographic Information

System (GIS)


Once gain the procedure of susceptibility/hazard/risk zoning Preparation, adjusting scale and level of research Input parameters Performing susceptibility zonation by combining the inputs

in knowledge (as presented in Lesson 3) or data driven approaches over training sets

Calibration over testing sets Selecting the best models with the smallest errors Shifting from susceptibility to hazard and risk

Additional inputs for frequency analysis (spatial-temporal probabilities)

Implementing element at risk by thematic maps (population,

infrastructure, dwelling) of ER vulnerability

Appending upon previous susceptibility map trough risk equation,

R=H*V(ER)


1. Statistical techniques of landslide susceptibility/hazard/risk zonation (applicable from regional to slope scale)

Bivariate Multivariate

Discriminant score Logistic regression Cluster Analysis Principal Component Analysis (PCA) Machine learning (advanced statistical approach)

Artificial Neural Networks Support Vector Machines Decision Trees Fuzzy Logics


Bivariate statistics Relating two maps using descriptive

statistics Procedure:

1. Overlaying i-th geo-parameter map and landslide reference map, calculating landslide density per each class and overall landslide density

2. Calculating the weight per each class by relating class to overall density

3. Reclassification of initial geo-parameter map

4. Combination of geo-parameter maps into a final map

5. Reclassify the final map into levels adjusted by initial landslide map

Techniques: Information value Weights of evidence Frequency ratio


Bivariate statistics techniques Information value

Weight relates densities of landslide per class and per entire map

Calculate +/– weights (how important is the presence/absence of geo-parameter class in the landslide reference map)

W+=0 no contribution effect (irrelevant factor) W– =0 no contribution effect (irrelevant factor)

W+>0 contributes the presence of landslides W–>0 contributes the absence of landslides

W+<0 contributes the absence of landslides W–<0 contributes the presence of landslides

Repeat per every geo-parameter (geology, slope, land cover, elevation…) Calculate probability of landslide occurrence:

classclass

classclass

overall

classi pxLpx

pxLpxW

##

##lnln

classlandslides AAPROB


Bivariate statistics techniques Weight of evidence

Weight relates densities of landslide per class and per entire map

Sum-up +/– weightsW=0 no contribution effect (irrelevant factor)

W>0 contributes the presence of landslides

W<0 contributes the absence of landslides

Repeat per every geo-parameter (geology, slope, land cover, elevation…) Calculate probability of landslide occurrence:

classclass

classclass

overall

classi pxLpx

pxLpxW

##

##lnln

classlandslides AAPROB


Multivariate statistics Relating all geo-parameters (independent variables) to

reference landslide map (dependent variable) simultaneously with correlation between the independent variables

Procedure:1. Quantification and normalization of the inputs (note that with bivariate

categorical classes were possible)

2. Group independent variables in classes as in bivariate case

3. Correlate the input variables between each other by bivariate correlations or AHP or black box models (AI approach)

4. Solve the distribution in a hyper-plane that separates the initial cluster of data

Techniques: Discriminant score Logistic regression Cluster analysis


Multivariate techniques Discriminant score

Assumes a distribution between the parameters to be classified and divides them in two classes: stable A and unstable B

Generate a geo-parameters relation table

Interrelates all the inputs by Discriinant Score function:

DS=A0+A1P1+A2P2+…+AnPn

where Ai is the overall weight factor in the score

Pi is the parameter (geology, slope, elevation…)

Project a hyper-plane to discern classes A and B

Multivariate techniques Discriminant score

If certain threshold is reached the DS function is appropriate and it could serve the model

Accepted weight factors are used to generate the final model of susceptibility/hazard/risk

Compare results according to the susceptibility index with other methods


Multivariate techniques Machine learning algorithms

K-Nearest Neighbor (KNN) Votes per unclassified point Hardware demanding (sorting + voting) and therefore trained on small sets Convenient for spatially correlated data

(clustered data)

Support Vector Machines (SVM) Separates classes by plane with the widest margin If that plane could not be set in ordinary dimension space (2-3D)

it is plotted in higher feature space where observed set is projected

by kernel function (Gaussian) Training set could be significantly reduced with high quality of data


2. Deterministic models for landslide susceptibility/hazard/risk zonation (applicable from regional to local scale):

SHALSTAB: parametric free, simple hydrologic model, shallow landsliding, steady state

TOPOG: additional soil parameters, simple hydrologic model, shallow landsliding, steady state

SINMAP: additional soil parameters (uncertainty included), simple hydrologic model, shallow landsliding, steady state

TRIGRS: advanced 1-D hydrologic model, shallow landsliding, steady state

GeoTOP: advanced 3-D hydrologic model, shallow landsliding, steady state

DYLAM: requires geo-mechanical and meteorological inputs, simple hydrologic model, shallow landsliding, dynamic


SHALSTAB (SHAllow Landslide STABility) Concept: couple the slope stability and hydrologic model

Triggering mechanism: atmospheric discharge (heavy storms) that causes piezometric head gradient high enough to overcome the slope stability

Application: typically a hilly landscape with thick soil cover with unchanneled valleys where soil accumulation and discharge (by landsliding) alternates cyclically.

Limitation: NOT suitable for deep seated landslides, rocky outcrops, areas with deep groundwater tables, unstable glacial or postglacial terrains


SHALSTAB (SHAllow Landslide STABility) Theory:

Infinite slope model

Assumptions: no losses in water balance: effective precipitation equals the rainfall (no

evapotranspiration taken into account), no deep drains and no superficial (overland) flow, only subsurface runoff

runoff trajectories parallel with the slope and slip surface, with the laminar flow (Darcy’s law)

geo-mechanic parameters: C - cohesive strength of the soil = 0

(no cohesion and no root system reinforcement effect) φ - internal friction angle = 45° γ - volume weight ranges from 16-20 kN/m3

Stability model

solve by h/z:


SHALSTAB (SHAllow Landslide STABility) Hydrologic model (transmissivity T vs. rainfall q trough Darcy’s law)

SHALSTAB: solving combined equations of stability and subsurface flow

T/q [m] q/T [1/m]log (q/T) [1/m]

3162 0.00040 -3.4

1259 0.00079 -3.1

631 0.00158 -2.8

316 0.00316 -2.5

158 0.00633 -2.2

79 0.01266 -1.9


SHALSTAB (SHAllow Landslide STABility) Training and calibrating

Effects of parametrization Volume weight and friction angle constant, (allowing C=0 and comparisons

between different landscapes) Field measurements (area of the sliding body, width at the crown or toe, local

slope angle)

Effects of slope angle and drainage area calculation Minor differences due to slope algorithm type (8 neighboring cells) Slope angle gradient vs. slip surface angle gradient Maximum fall vs. multiple direction algorithm for drainage area

Effects of grid size Since coarser resolution gives smoother slopes coarser grids lack in

detailedness


SHALSTAB (SHAllow Landslide STABility) Testing (using field data to accept/reject parametric free

model) Mapping the landslide scar sites and overlaying over SHALSTAB

model Comparing different scenarios


SINMAP Concept: similarly as SHALSTAB couple the slope

stability and hydrologic model but trough the concept of stability index/safety factor (SI/FS) also emphasizing topographic influence (in a way SHALSTAB is a special case of SINMAP) As SHALSTAB considers cases of pore water pressure increase

due to heavy rainstorm events Also holds true for hilly landscape with unchanneled valleys Involves probabilistic uncertainty in parameter setting (such

as cohesion, bulk density and so forth) Faces the same limitations as SHALSTAB (terrain types, high

dependence on DEM accuracy and accuracy of landslide inventory)


SINMAP Theory

Infinite slope model (with perpendicular dimensioning) Factor of safety (suppressing vs. driving forces)

Assumptions As in SHALSTAB apart from cohesion dimensionless factor


SINMAP Theory

Hydrologic model - Topographic Wetness Index (TWI) Specific catchment area a=A/b

based on the approach of hollow areas

(topographic convergence areas) Assuming that:

Subsurface flow follows topographic gradient

(superficial topography is used for calculation of a) Recharge R (heavy rainfall, snowmelt) = lateral discharge q Flux of the recharge = Transmissivity T *sinθ

(T=kuniform *h)

Lateral discharge: Relative wetness w=hw/h now with max set to 1

(superficial flow)

R/T becomes a singleparameter that treats climatic and hydrologic influence


SINMAP Theory

Stability model – Stability index

From to

where r=0,5 but C, R/T and tan φ are normally distributed variables (uncertainty involved)

Spatial and temporal probability is included ranging from worst case scenario (lowest C, highest R/T, lowest tan φ) to best case scenario (vice versa)

Probabilities of SI


SINMAP Training and calibrating

Pit filling DEM correction Effect of slope and flow direction from corrected DEM effects Specific catchment area calculation


GEOtop Analyzes 3D hydrologic flow (lateral and normal) by solving

general case of Richard’s equation Uses Bishops failure criteria Takes antecedent conditions of soil moist into account


DYLAM Also for shallow landsliding Analyzes dynamic data by time vector of rainfall events

(unambiguous temporal probability) Requires additional geo-mechanical parameters as constant

or float values (the latter provides temporal probability) Uses simple subsurface flow hydrology Final output is factor of safety map based on infinite slope

modeling, giving an actual hazard map for the selected time sequence

Couples the GIS environment trough .asc files


GIS IN GEOLOGY

Miloš MarjanovićExercise 44.11.2010.

GIS IN GEOLOGY Miloš Marjanović Lesson 5 4.11.2010.

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