F. C. DAI AND C. F. LEE A Spatiotemporal Probabilistic Modelling of Storm-Induced Shallow Landslide Using Aerial Photographs and Logistic Regression 報告者：蔡雨澄 指導教授：李錫堤 報告日期： 2010/12/30

Varnes (1984) defined natural hazard as the probability of occurrence of a potentially damaging phenomenon within a specified period of time and within a given area. Mapping or delineating areas prone to landsliding is essential for land-use activities and management decision making in hilly or mountainous regions.

Study Area

Mean annual rainfall for the study area over the period 1961– 91 is in the range of 2000 to 2400 mm (Lam and Leung,1994).

Linear Model

Logistic regression

Data aerial photographs The date taken on spatial scales Landslide occurred 1st1991/12/301:8000 2nd1992/11/111: rd1993/12/031: cumulative maximum in any 24 h period

Data 1992/7/181993/11/4~5

Data DEM (2m×2m) Slope gradient ( <15,15-20,25-30… 50 ) (degree) Slope aspect ( 8+1(flat) ) Elevation ( <50,50-100, … 500 ) (m) Slope shape ( LL, LX, LV, XL, XX, XV, VL, VX, VV )

Data Lithology 1:5000 geological maps

Data Land cover (a) developed land (b) grassed land (c) shrub–grassed land (d) forest–shrubbed land (e) forested land

Data Rainfall data 1992/7/ /11/4~ /9/ /6/13~ /9/ /6/13~14

Data numberValue of ln(P/(1-P) ) Landslide grid cells Stable grid cells

Modelling result Statistical Package of Social Sciences (SPSS)

Modelling result Error matrix Observed data occurred Not occurred Predicted result occurred Not occurred Accuracy 89.5%84.9 %

Model Application

10-year 20-year

50-year100-year

DISCUSSION AND CONCLUSIONS For each landslide cell, the maximum rolling 24 h rainfall was designated as the dynamic variable. The rainfall return periods conventionally used were assessed using data from only one site and should be applied only to that site. The antecedent rainfall may have some influence on the occurrence of landslides, but this effect is not accounted for in the predictive model as stated.

DISCUSSION AND CONCLUSIONS Land-use planners may differ in the level of risk they can afford or accept. This model allows them to choose their own level of increased risk. This model has been useful in identifying areas likely to have landsliding in a way that has not been possible previously.

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