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Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach

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Abstract

Landslide is a natural disaster that threatens human lives and properties worldwide. Numerous have been conducted on landslide susceptibility mapping (LSM), in which each has attempted to improve the accuracy of final outputs. This study presents a novel region-partitioning approach for LSM to understand the effects of partitioning a focused region into smaller areas on the prediction accuracy of common regression models. Results showed that the partitioning of the study area into two regions using the proposed method improved the prediction rate from 0.77 to 0.85 when support vector machine was used, and from 0.87 to 0.88 when logistic regression model was utilized. The spatial agreements of the models were also improved after partitioning the area into two regions based on Shannon entropy equations. Our comparative study indicated that the proposed method outperformed the geographically weighted regression model that considered the spatial variations in landslide samples. Overall, the main advantages of the proposed method are improved accuracy and the reduction of the effects of spatial variations exhibited in landslide-conditioning factors.

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Authors and Affiliations

  1. College of Geographic Sciences, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Haoyuan Hong & Axing Zhu

  2. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu, 210023, China

    Haoyuan Hong & Axing Zhu

  3. Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, Universiti Putra Malaysia, 43400, Selangor, Malaysia

    Biswajeet Pradhan, Maher Ibrahim Sameen & Bahareh Kalantar

  4. School of Systems, Management and Leadership, Faculty of Engineering and Information Technology, University of Technology Sydney, Building 11, Level 06, 81 Broadway, PO Box 123, Ultimo, NSW, 2007, Australia

    Biswajeet Pradhan

  5. Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro Gwangjin-gu, Seoul, 05006, Republic of Korea

    Biswajeet Pradhan

  6. Department of Geography, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Axing Zhu

  7. School of Geology and Environment, Xi’an University of Science and Technology, Xi’an, 710054, China

    Wei Chen

Authors
  1. Haoyuan Hong
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  2. Biswajeet Pradhan
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  3. Maher Ibrahim Sameen
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  4. Bahareh Kalantar
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  5. Axing Zhu
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  6. Wei Chen
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Corresponding author

Correspondence to Biswajeet Pradhan.

Appendix 1

Appendix 1

The pseudo-code of tessellated classification algorithm

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Hong, H., Pradhan, B., Sameen, M.I. et al. Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach. Landslides 15, 753–772 (2018). https://doi.org/10.1007/s10346-017-0906-8

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  • DOI: https://doi.org/10.1007/s10346-017-0906-8

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