GIS in Geology

1
GIS in Geology

• Miloš Marjanovic

Lesson 5 4.11.2010.
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GIS in Landslide assessment (advanced)

• 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
• 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

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GIS in Landslide assessment (advanced)
Database Management Systems (DBMS)
Image Processing (IP)
Computer Aided Drawing (CAD)
Desktop mapping
Desktop and Web publishing
Geostatistics
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GIS in Landslide assessment (advanced)

• Once gain the procedure of susceptibility/hazard/r
  isk 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, RHV(ER)

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GIS in Landslide assessment (advanced)

• 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

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GIS in Landslide assessment (advanced)

• Bivariate statistics
• Relating two maps using descriptive statistics
• Procedure
• Overlaying i-th geo-parameter map and landslide
  reference map, calculating landslide density per
  each class and overall landslide density
• Calculating the weight per each class by relating
  class to overall density
• Reclassification of initial geo-parameter map
• Combination of geo-parameter maps into a final
  map
• Reclassify the final map into levels adjusted by
  initial landslide map
• Techniques
• Information value
• Weights of evidence
• Frequency ratio

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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)
• W0 no contribution effect (irrelevant
  factor) W 0 no contribution effect (irrelevant
  factor)
• Wgt0 contributes the presence of landslides
  Wgt0 contributes the absence of landslides
• Wlt0 contributes the absence of landslides Wlt0
  contributes the presence of landslides
• Repeat per every geo-parameter (geology, slope,
  land cover, elevation)
• Calculate probability of landslide occurrence

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GIS in Landslide assessment (advanced)

• Bivariate statistics techniques
• Weight of evidence
• Weight relates densities of landslide per class
  and per entire map
• Sum-up / weights
• W0 no contribution effect (irrelevant factor)
• Wgt0 contributes the presence of landslides
• Wlt0 contributes the absence of landslides
• Repeat per every geo-parameter (geology, slope,
  land cover, elevation)
• Calculate probability of landslide occurrence

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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
• Quantification and normalization of the inputs
  (note that with bivariate categorical classes
  were possible)
• Group independent variables in classes as in
  bivariate case
• Correlate the input variables between each other
  by bivariate correlations or AHP or black box
  models (AI approach)
• Solve the distribution in a hyper-plane that
  separates the initial cluster of data
• Techniques
• Discriminant score
• Logistic regression
• Cluster analysis

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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
• DSA0A1P1A2P2AnPn
• 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

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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

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GIS in Landslide assessment (advanced)

• 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

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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

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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 (Darcys law)
• geo-mechanic parameters
• C - cohesive strength of the soil 0
• (no cohesion and no root system reinforcement
  effect)
• f - internal friction angle 45
• ? - volume weight ranges from 16-20 kN/m3
• Stability model
• solve by h/z

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GIS in Landslide assessment (advanced)

• SHALSTAB (SHAllow Landslide STABility)
• Hydrologic model (transmissivity T vs. rainfall q
  trough Darcys 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
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GIS in Landslide assessment (advanced)

• SHALSTAB (SHAllow Landslide STABility)
• Training and calibrating
• Effects of parametrization
• Volume weight and friction angle constant,
  (allowing C0 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

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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

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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)

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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

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GIS in Landslide assessment (advanced)

• SINMAP
• Theory
• Hydrologic model - Topographic Wetness Index
  (TWI)
• Specific catchment area aA/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 sin?
• (Tkuniform h)
• Lateral discharge
• Relative wetness whw/h now
  with max set to 1 (superficial
  flow)
• R/T becomes a singleparameter that treats
  climatic and hydrologic influence

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GIS in Landslide assessment (advanced)

• SINMAP
• Theory
• Stability model Stability index
• From
  to
• where r0,5 but C, R/T and tan f are normally
  distributed variables (uncertainty involved)
• Spatial and temporal probability is included
  ranging from worst case scenario
  (lowest C, highest R/T, lowest tan f) to best
  case scenario (vice versa)
• Probabilities of SI

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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

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GIS in Landslide assessment (advanced)

• GEOtop
• Analyzes 3D hydrologic flow (lateral and normal)
  by solving general case of Richards equation
• Uses Bishops failure criteria
• Takes antecedent conditions of soil moist into
  account

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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

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GIS in Geology

• Miloš Marjanovic

Exercise 4 4.11.2010.