1. Models for Predicting Landslide Location (1)
Chyi-Tyi Lee Graduate Institute of Applied Geology,
National Central University, Taiwan
2014暑期坡地營課程
06/08/2014

2. Landslide Hazard Map of Europe(
Summary report October 2012

3. Landslide Hazard Map of Japan
(Kawagoe et al., 2010)

4. Landslide Hazard Map of Taiwan
The First
Landslide Susceptibility Map of Taiwan
In 2001
Landslide Ratio vs. Susceptibility Class
Landslide Ratio
Susceptibility Class
Rating for Slope Factor
Slope
0-3
3-10
10-15
15-20
20-30
30-40
>40 A 1 3 6 7 8 9 B 5 10 C
Lithology
Group A
Hard
Miocene, Pre-Miocene, Igneous Rocks
Group B
Soft
Sandstone, Conglomerate
Group C
Mudstone and Shale
Landslide susceptibility map built by using slope and lithology (Liao et al., 2001)

5. Keywords and Steps in Natural Hazard Analysis
Construction of time series inventory of hazard
Inventory Construction
Dividing a region into successive classes representing different grades of slope instability
Susceptibility Analysis
Calculation of the probability of a hazard level in a given region and a given time period.
Hazard Analysis
Calculation of the lost of life or property in a given region and a given time period.
Risk Analysis

6. What is a Landslide Hazard Analysis?●
Landslide hazard analysis is a regional hill-slope stability analysis concerning the probability of instability at any given point in the study region and in a given time period.
It is different from a conventional slope stability analysis. ●
Slope stability analysis is a site-specific stability analysis concerning under what condition the slope will become unstable.
Limit equlibrium slope stability analysis
Landslide probability for 100-year rainfalls, Tachia drainage basin, Taiwan.

7. How a Landslide Hazard Model Predicting Landslide Location?
475-year earth-quake
Yunlin

8. Previous Works on Landslide Susceptibility Analysis●
Qualitative methods
Geomorphologic interpretation
Expert’s point system
Quantitative methods
Conditional probability (bivariate analysis)
Discriminant analysis (multivariate analysis)
Logistic regression (multivariate analysis)
Neural networks (artificial intelligence)
Fuzzy set theory (artificial intelligence)
Infinite slope model (limit equilibrium calculation) ●
☆ The susceptibility analysis aims at dividing the region into successive classes representing different grades of slope stability, spatial and temporal occurrence probability are not discussed. It is different from a landslide hazard analysis.

9. Recent Landslide Hazard Analysis
(1/5) ●
Deterministic approach for earthquake-induced landslides
Map showing probability of seismic triggering of landslides in Northridge-earthquake shaking conditions in part of the Oat Mountain quadrangle.
Landslide occurrence probability for 475-year return period earthquake intensity.
Jibson, R.W., Harp, E.L., Michael, J.A. (2000) A method for producing digital probabilistic seismic landslide hazard maps, Engineering Geology, 58,
Liao, C.W., Lee, C.T. (2004) Probabilistic hazard analysis of earthquake-induced landslides - an example from KouHsing, Taiwan, International Symposium on Landslide and Debris Flow Hazard Assessment, 7-1~ 7-9.

10. Recent Landslide Hazard Analysis
(2/5) ●
Deterministic approach for storm-induced landslides
Schmidt, J., Turek, G., Clark, M. P., Uddstrom, M. and Dymond, J. R.: Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions, Nat. Hazards Earth Syst. Sci., 8, 349–357, 2008.
Montgomery, D. R. and Dietrich, W. E. (1994) A physical-based model for the topographic control on shallow landsliding, Water Resour. Res., 30, 4,

11. Recent Landslide Hazard Analysis
(3/5) ●
Probabilistic approach for earthquake-induced landslides
475-year earth-quake
Yunlin
Lee, Chyi-Tyi (unpublished).
Lee, Chyi-Tyi, Huang, C.C., Lee, J.F., Pan, K.L., Lin, M.L., Dong, J.J. (2008) Statistical approach to earthquake-induced landslide susceptibility, Engineering Geology, 100,

12. Recent Landslide Hazard Analysis
(4/5) ●
Probabilistic approach for storm-induced landslides
- using multi-temporal landslide inventory
5 years
10 years
Fig. 5. Exceedance probability of landslide occurrence obtained computing the mean recurrence interval of past landslide events from the multitemporal inventory (Fig. 3), assuming it will remain the same for the future, and adopting a Poisson probability model (Eq. (9)). Shades of grey show exceedance probability for different periods: A) 5 years, B) 10 years, C) 25 years, D) 50 years. Square bracket indicates class limit is included; round bracket indicates class limit is not included.
25 years
50 years
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., and Ardizzone F. (2005) Probabilistic landslide hazard assessment at the basin scale, Geomorphology, 72,
Exceedance probability

13. Recent Landslide Hazard Analysis
(5/5) ●
Probabilistic approach for storm-induced landslides
- using event-based landslide inventory
5 years
10 years
10 years
100 years
25 years
50 years
香港大嶼山
台灣北部石門水庫集水區
Chung,Y.C., Huang, C.M., Lee, Chyi-Tyi (2009) Validation of landslide prediction model under different rainfall scenarios. Joined Annual Meeting and Conference of the Geological Society of China and the Geophysical Society of China, HZ13-O, 11p.
Dai, C.F. and Lee, C.F. (2003) A Spatiotemporal Probablilistic Modeling of Storm-Induced Shallow Landslide Using Aerail Photographs and Logistic Regression, Earth Surf. Proc. Land., 28,

14. Summary of Methods for Landslide Hazard Analysis●
Deterministic approach: Limit equilibrium analysis of infinite-slope at a region using strength and hydrologic parameters to obtain a safety factor for a given point under rainfall or earthquake condition.
Direct probabilistic approach: Using a long-period multi-temporal landslide inventories to analyze the landslide occurrence probability for each slope under certain return period.
Spatial-Temporal probabilistic approach: To analyze the spatial landslide occurrence probability and the rainfall temporal probability separately. Landslide occurrence probability is obtained via event- based landslide susceptibility analysis. Temporal probability is obtained by hydrological frequency analysis of rainfalls. ● ●

15. Developing a Landslide Hazard Model
Construction of a landslide inventory.
Inventory Construction
Dividing a region into susceptibility classes by using a landslide inventory and some landslide causative factors.
Susceptibility Analysis
Calculation of spatial and temporal probability of landslide in a given region.
Hazard Analysis
Risk Analysis

16. The Use of a Landslide Inventory in Susceptibility/Hazard Analysis
(1/2)
Multi-temporal landslide inventory was commonly used in developing a susceptibility/hazard model. The long the period covered, the better the temporal significance is.
Single-period landslide inventory: Landslide inventory from single period of aerial photos or a satellite image is not valid for training a landslide susceptibility model (e.g., Canuti and Focardi, 1986). For example,
Susceptibility class
High
Mod. High
Moderate
Low
Stable
Ruey-Li earthquake
Chi-Chi earthquake
Toraji-Lekima typhoon
Landslide susceptibility maps of Yunlin quadrangle trained from landslide inventory of different period.

17. The Use of a Landslide Inventory in Susceptibility/Hazard Analysis
(2/2)
Single-period landslide inventory for susceptibility analysis is biased. However, it may not be feasible to use a multi-temporal landslide inventory in some regions. This is especially true in Taiwan (Lee and Fei, 2011), and therefore, an event landslide inventory must be considered.
Event landslide inventory is feasible in developing an event-based susceptibility/hazard model (Lee et al., 2008a, 2008b). The temporal significance may come from the triggering factors used in building the model.

18. Landslide Types Considered in Susceptibility/Hazard Analysis
Deep-seated landslides are commonly controlled by local geological structures. They were usually not considered in building a susceptibility/hazard model.
Shallow landslides are commonly of random features occurred on hill slope. They were the target in building a susceptibility/hazard model. Rock falls have similar random behavior with shallow landslides. The were usually included in the shallow landslide group for analysis.
Debris flows are different with slide and fall type of landslides in many aspects, like location and controlling factors. Their susceptibility/hazard analysis was commonly performed along.

19. Selection of an Analytical Method
Kuohsing Quadrangle, Hilly Terrain
Portion of landslides within
the predicted hazard area
LOGISTIC : Logistc Regression
DA : Discriminant Analysis
NN : Neural Network
DN : Newmark Displacement
Portion of areas predicted as hazard
(Lee, 2006)
123
Logistic regression is an effective and robust method for building a susceptibility/hazard model. NN may built a success model but sometimes fail in prediction. DN commonly involves problems of lacking local soil depth, strength parameters, and groundwater data.

20. Selection of a Mapping Unit
A mapping unit refers to a portion of the land surface which contains a set of ground conditions which differ from the adjacent units across definable boundaries (Hansen, 1984). ●
Grid-cells, divide the territory into regular squares of pre-defined size.
Terrain units, favored by geomorphologists, are based on the observation that frequently reflect geomorphological and geological differences.
Unique-condition units, are homogeneous domains derived from classification of many slope-instability factors. ●
Slope-units, automatically derived from high-quality DTMs, partition the territory into hydrological regions between drainage and divide lines.
Topographic units, are further subdivided into homogeneous morphometric domains from the slope units.

21. Computer Software Needed●
Vector GIS: MapInfo, ArcGIS, and others, for mapping of landslides and output of thematic maps. ●
Image Processing and Raster GIS: Erdas Imagine, and others, for processing of satellite images, and for processing of landslide causative factors from DEM and others. ●
Statistical Package: SPSS, SAS, MATLAB, and others, for statistical tests and multivariate analysis, like logistic regressing and discriminant analysis. ●
Others, like Fortran code designed for specific data processing or analysis.

22. Thanks for your attention!謝謝