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Landslides

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Preliminary analyses of a catastrophic landslide occurred on July 23, 2019, in Guizhou Province, China

  • Hai-bo Li
  • Yue-ren Xu
  • Jia-wen Zhou
  • Xie-kang Wang
  • Hiromitsu Yamagishi
  • Jie DouEmail author
News/Kyoto Commitment

Preliminary analyses of a catastrophic landslide

Introduction

On 23 July 2019, at UTC+8 21:20, a catastrophic landslide hit the Pingdi village in the Shuicheng County, Liupanshui City, Guizhou Province, China (E104° 40′ 24″, N26° 15′ 27″) (Fig.  1) after continuous torrential downpours. Because of the long-runout of a large volume of loose debris materials, the residential areas along the landslide traveling path were completely destroyed and the county road passing through the landslide area was cut off (Fig. 2a). The sudden, high-speed debris movement has caused severe damage to the village infrastructures with more than 20 houses buried and about 42 fatalities of residents. Immediately after this landslide event, the China State Council launched the on-site emergency rescue and disaster mitigation operations.

Notes

Funding information

We gratefully acknowledge the financial support of the National Key R&D Program of China (2017YFC1501102), the National Natural Science Foundation of China (51579163, 51639007, and 41977229), and the Fundamental Scientific Research Fund in the IEF, CEA (2019IEF0201), CAS Pioneer Hundred Talents Program and JSPS Program.

References

  1. Chang K-T, Merghadi A, Yunus AP, Pham BT, Dou J (2019) Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep 9:12296.  https://doi.org/10.1038/s41598-019-48773-2 CrossRefGoogle Scholar
  2. Dou J, Yamagishi H, Pourghasemi HR et al (2015) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78:1749–1776.  https://doi.org/10.1007/s11069-015-1799-2 CrossRefGoogle Scholar
  3. Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen CW, Han Z, Pham BT (2019a) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed. Japan Landslides:1–18.  https://doi.org/10.1007/s10346-019-01286-5
  4. Dou J, Yunus AP, Tien Bui D, Merghadi A, Sahana M, Zhu Z, Chen CW, Khosravi K, Yang Y, Pham BT (2019b) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346.  https://doi.org/10.1016/j.scitotenv.2019.01.221 CrossRefGoogle Scholar
  5. Pham BT, Prakash I, Dou J et al (2019) A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto Int.  https://doi.org/10.1080/10106049.2018.1559885
  6. Xing A, Wang G, Li B et al (2014) Long-runout mechanism and landsliding behaviour of large catastrophic landslide triggered by heavy rainfall in Guanling, Guizhou, China. Can Geotech J 52:971–981.  https://doi.org/10.1139/cgj-2014-0122 CrossRefGoogle Scholar
  7. Zhou J, Cui P, Hao M (2016) Comprehensive analyses of the initiation and entrainment processes of the 2000 Yigong catastrophic landslide in Tibet, China. Landslides 13:39–54.  https://doi.org/10.1007/s10346-014-0553-2 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and HydropowerSichuan UniversityChengduPR China
  2. 2.Key Laboratory of Earthquake Predication, Institute of Earthquake ForecastingChina Earthquake AdministrationBeijingChina
  3. 3.Hokkaido Research Center of Geology (HRCG)SapporoJapan
  4. 4.Three Gorges Research Center for Geo-Hazards, Ministry of Education China University of GeosciencesWuhanChina
  5. 5.Department of Civil and Environmental EngineeringNagaoka University of TechnologyNagaokaJapan

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