Articles | Volume 8, issue 4
Nat. Hazards Earth Syst. Sci., 8, 941–960, 2008
https://doi.org/10.5194/nhess-8-941-2008
Nat. Hazards Earth Syst. Sci., 8, 941–960, 2008
https://doi.org/10.5194/nhess-8-941-2008

  29 Aug 2008

29 Aug 2008

Statistical approach to storm event-induced landslides susceptibility

C.-T. Lee1, C.-C. Huang2, J.-F. Lee2, K. -L. Pan1, M.-L. Lin3, and J.-J. Dong1 C.-T. Lee et al.
  • 1Institute of Applied Geology, National Central University, No.300, Jungda Rd., Jungli City, Taoyuan County, 32001, Taiwan
  • 2Central Geological Survey, Ministry of Economic Affairs, No.2, Lane 109, Huashin Street, Chungho City, Taipei County, 23500, Taiwan
  • 3Department of Civil Engineering, National Taiwan University, No.1, Sec.4 Roosevelt Rd. Taipei, 10617, Taiwan

Abstract. For the interpretation of the storm event-induced landslide distribution for an area, deterministic methods are frequently used, while a region's landslide susceptibility is commonly predicted via a statistical approach based upon multi-temporal landslide inventories and environmental factors. In this study we try to use an event-based landslide inventory, a set of environmental variables and a triggering factor to build a susceptibility model for a region which is solved using a multivariate statistical method. Data for shallow landslides triggered by the 2002 typhoon, Toraji, in central western Taiwan, are selected for training the susceptibility model. The maximum rainfall intensity of the storm event is found to be an effective triggering factor affecting the landslide distribution and this is used in the model. The model is built for the Kuohsing region and validated using data from the neighboring Tungshih area and a subsequent storm event – the 2004 typhoon, Mindulle, which affected both the Kuohsing and the Tungshih areas. The results show that we can accurately interpret the landslide distribution in the study area and predict the occurrence of landslides in the neighboring region in a subsequent typhoon event. The advantage of this statistical method is that neither hydrological data, strength data, failure depth, nor a long-period landslide inventory is needed as input.

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