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

Landslide Hazard Map of Europe (www.safeland-fp7.eu) Summary report October 2012

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

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)

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

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.

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

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.

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, 271-289. 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.

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, 1153-1171.

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, 43-58.

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, 272-299. Exceedance probability

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, 527-545.

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. ● ●

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

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.

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.

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.

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.

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.

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.

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