GIS IN GEOLOGY Miloš Marjanović Lesson

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: 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 W=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: 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=A 0 +A 1 P 1 +A 2 P 2 +…+A n P n where A i is the overall weight factor in the score P i 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 kN/m 3  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] 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 *sin θ (T=k uniform *h)  Lateral discharge:  Relative wetness w=h w /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 Marjanović Exercise