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Natural Hazards

, Volume 79, Issue 1, pp 511–536 | Cite as

Prediction of highway blockage caused by earthquake-induced landslides for improving earthquake emergency response

  • Jiwen An
  • Xianfu Bai
  • Jinghai Xu
  • Gaozhong Nie
  • Xiuying Wang
Original Paper

Abstract

Earthquake emergency response (EER) supported by the prompt assessment of seismic impact is an effective way to reduce seismic casualties and losses after an earthquake. However, in mountainous areas, highway blockages due to earthquake-induced landslides can delay EER, which, to date, EER planning has not included in assessments to identify. This paper proposes a set of rules to predict the location of highway blockages caused by these landslides. Such predictions would promote rapid implementation of traffic control plans and the prompt clearing of the blocked highways to help keep emergency efforts efficient. We propose a procedure based on the decision tree method to correlate the potential highway blockages with the earthquake-induced landslide susceptibility (ELS), which integrates the classification and quantification aspects of the ELS. Using correlation analysis, a set of rules that judge whether a highway section is likely to be blocked is proposed. These rules are based on the preexisting ELS database for China. This set of rules has been applied in a case study of the 2014 Ludian earthquake to predict the highway blockages caused by the earthquake-induced landslides. The results from this case study showed good agreement with the actual highway blockages as determined by the interpretation of unmanned aerial vehicle images. The predicted results were used to make suggestions about traffic control and blocked highway clearing for EER. The proposed set of rules appears to be effective.

Keywords

Prediction Highway blockage Earthquake-induced landslide susceptibility Earthquake emergency response Correlation analysis Decision tree 

Notes

Acknowledgments

This work was jointly supported by the Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University) (Grant No. 201404), the Special Fund for Basic Scientific Research Operations in Institute of Geology, CEA (Grant No. IGCEA1109), the Jiangsu Surveying, Mapping and Geoinformation Science Research Project (Grant No. JSCHKY201506), and the National Key Technology R&D Program (Grant No. 2012BAK15B06). We sincerely thank T.S. Murty and the anonymous reviewers for their comments and suggestions that greatly improved the manuscript.

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Jiwen An
    • 1
  • Xianfu Bai
    • 2
  • Jinghai Xu
    • 3
  • Gaozhong Nie
    • 1
  • Xiuying Wang
    • 4
  1. 1.Institute of GeologyChina Earthquake AdministrationBeijingChina
  2. 2.Yunnan Earthquake AdministrationKunmingChina
  3. 3.College of Geomatics EngineeringNanjing Tech UniversityNanjingChina
  4. 4.Institute of Crustal DynamicsChina Earthquake AdministrationBeijingChina

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