A new approach to temporal modelling for landslide hazard assessment using an extreme rainfall induced-landslide index

An ever-increasing trend of extreme rainfall events hi South Korea due to climate change is causing shallow landslides and shallow landslide induced debris flows in the mountains that cover 70% of the total land area of the nation. These catastrophic, gravity-driven processes cost the government several billion won in losses,, and attendant fatalities, every year. The most common type of landslide observed is the shallow landslide occurring at 13 m depth, which may mobilize into a catastrophic debris flow. A landslide early warning system encompassing different scale-based stages is used to predict potential areas for both the landslide types. Current study focusing on the first stage landslide hazard assessment at regional or medium scale requires the development of spatially evolving landslide hazard maps for both types of landslides based on the real-time rainfall. However, lack of complete landslide inventory data motivates the development of temporal and spatial models as independent components of the landslide hazard. Most of the existing temporal assessment schemes traditionally rooted in recurrence-based concepts does not consider soil factors and are not suitable to be incorporated in to the landslide early warning system since real-time rainfall cannot be considered. This motivated the development of a new probabilistic temporal model termed the extreme rainfall-induced landslide index. The probabilistic index was developed in Gangwon Province through a logistic regression using four factors; namely, continuous rainfall, 20-days antecedent rainfall, saturated'hydraulic conductivity, and storage capacity. The developed model exhibited high area under the curve (AUC) values of 82% and 91% obtained for the training and validation curves, exhibiting good performance of the statistical index. Also, a high performance susceptibility model (training and validation AUC values of 96% and 94%, respectively) was developed using a logistic regression analysis, for Deokjeok-ri Creek, located in Gangwon province. Assuming the independence of the hazard components, a dynamic hazard index (DHI) was established through a joint probability of both the well validated models. The DHI was used to study the evolution of landslide hazard for the July 2006 extreme rainfall-induced landslide events in Deokjeok-ri Creek. (C) 2016 Published by Elsevier B.V.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2016-12
Language
English
Keywords

SUPPORT VECTOR MACHINE; ARTIFICIAL NEURAL-NETWORKS; SUSCEPTIBILITY ASSESSMENT; SHALLOW LANDSLIDES; SOUTH-KOREA; FUZZY-LOGIC; PROCESS AHP; STABILITY; SLOPES; SOIL

Citation

ENGINEERING GEOLOGY, v.215, pp.36 - 49

ISSN
0013-7952
DOI
10.1016/j.enggeo.2016.10.006
URI
http://hdl.handle.net/10203/218739
Appears in Collection
RIMS Journal Papers
Files in This Item
There are no files associated with this item.
  • Hit : 41
  • Download : 0
  • Cited 0 times in thomson ci
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡClick to seewebofscience_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0