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A Spatiotemporal Probabilistic Modelling of Storm-Induced Shallow Landslide Using Aerial Photographs and Logistic Regression. F. C. DAI and C. F. LEE. 報告者:蔡 雨 澄 指導教授:李錫堤 報告日期: 2010/12/30.
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A Spatiotemporal Probabilistic Modelling of Storm-Induced Shallow Landslide Using Aerial Photographs and Logistic Regression F. C. DAI and C. F. LEE 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).
Data cumulative maximum in any 24 h period
1992/7/18 1993/11/4~5 Data
Data DEM (2m×2m) Slope gradient ( <15,15-20,25-30… 50 )(degree) Slope aspect ( 8+1(flat) ) Elevation ( <50,50-100,100-150… 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
1990/9/11 Data Rainfall data 1992/7/18 1993/11/4~5 + 1990/9/11 1992/6/13~14 1992/6/13~14
Data 3000+3000+3000+3000
Modelling result Statistical Package of Social Sciences (SPSS)
10-year 20-year
50-year 100-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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