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)
GIS in Landslide assessment (advanced)
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)
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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:
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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)
GIS in Landslide assessment (advanced)
SINMAP Theory
Infinite slope model (with perpendicular dimensioning) Factor of safety (suppressing vs. driving forces)
Assumptions As in SHALSTAB apart from cohesion dimensionless factor
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
SINMAP Training and calibrating
Pit filling DEM correction Effect of slope and flow direction from corrected DEM effects Specific catchment area calculation
GIS in Landslide assessment (advanced)
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
GIS in Landslide assessment (advanced)
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 Landslide assessment (advanced)
GIS IN GEOLOGY
Miloš MarjanovićExercise 44.11.2010.
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