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Landslides

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A shallow slide prediction model combining rainfall threshold warnings and shallow slide susceptibility in Busan, Korea

  • Ananta Man Singh Pradhan
  • Seung-Rae Lee
  • Yun-Tae Kim
Technical Note
  • 113 Downloads

Abstract

Rainfall-induced shallow landslides are common in many mountainous countries. Highly concentrated precipitation triggers landslides and debris flows in worldwide. Every year, several shallow slides and debris flows occur in Busan, South Korea during heavy rainfall. To reduce and prevent associated damage, we developed a matrix-based approach of rainfall threshold and landslide susceptibility. This study collects landslide inventories which consist of information of 260 landslide location, 35 landslide event times, and corresponding rainfall intensities and durations. A rainfall threshold warning levels were established using rainfall intensities and durations associated with 35 historical shallow slides, and categorised as null (< 5%), watch (5–20%), attention (20–50%), and alarm (> 50%). We used a back propagation ANN machine-learning algorithm to explore the effects of 14 causative factors on shallow slide distribution. The area under the curve was used to assess accuracy, and accuracy was found to be 86.12%. The derived rainfall threshold warning levels were assigned in rows and the susceptibility classes were used in columns of the matrix. The combined result represents the shallow slide hazards to rainfall threshold warning levels with a varying likelihood to shallow slide initiation. The results aim to raise awareness towards landslide hazards and to support regional decision for the land-use planning.

Keywords

Artificial neural network Geographic information system Matrix Rainfall threshold Shallow slide susceptibility 

Notes

Acknowledgements

The authors are thankful to anonymous reviewers for their valuable comments that were very useful in bringing the manuscript into its present form. Dr. Ji-Sung Lee is sincerely acknowledged for his great help during the field work.

Funding information

This work was supported by the Korean Institute of Energy Technology Evaluation and Planning (KETEP), the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20171510101960), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (2018R1A4A1025765).

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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Ocean EngineeringPukyong National UniversityBusanSouth Korea
  2. 2.Department of Civil and Environmental EngineeringKAISTDaejeonSouth Korea

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