IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v103y2020i3d10.1007_s11069-020-04128-z.html
   My bibliography  Save this article

Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China

Author

Listed:
  • Weidong Wang

    (Central South University
    Ministry of Education)

  • Zhuolei He

    (Central South University)

  • Zheng Han

    (Central South University)

  • Yange Li

    (Central South University)

  • Jie Dou

    (Nagaoka University of Technology)

  • Jianling Huang

    (Central South University)

Abstract

A dataset of landslides from Sichuan Province in China, containing 1551 historical individual landslides, is a result of two teams’ effort in the past few years to map the susceptibility to landslides. Considering complex internal relations among the triggering factors, logistic regression (LR) and shallow neural networks, such as back-propagation neural network (BPNN), are often limited. In this paper, we make a straightforward development that the deep belief network (DBN) based on deep learning technology is introduced to map the regional susceptibility to landslides. Seven factors with respect to geomorphology, geology and hydrology are considered and verified through the collinearity test. A DBN model containing three pre-trained layers of restricted Boltzmann machines by stochastic gradient descent method is configured to obtain the susceptibility to landslides. Susceptibility results evaluated by DBN model are compared with those by LR and BPNN in the receive operator characteristic (ROC) analysis, showing that DBN has a better prediction precision, with a lower rate of false alarms and fake alarms. The case study also indicates different sensitivities of the triggering factors to the landslide susceptibility, that the factors of altitude, distance to drainage network and average annual rainfall have significant impact in mapping the susceptibility to landslides in the region. This research will contribute to a better-performance model for regional-scale mapping for the susceptibility to landslides, in particular, at the area where triggering factors show complex relations and relative independence.

Suggested Citation

  • Weidong Wang & Zhuolei He & Zheng Han & Yange Li & Jie Dou & Jianling Huang, 2020. "Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 3239-3261, September.
  • Handle: RePEc:spr:nathaz:v:103:y:2020:i:3:d:10.1007_s11069-020-04128-z
    DOI: 10.1007/s11069-020-04128-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-020-04128-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-020-04128-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gökhan Demir & Mustafa Aytekin & Aykut Akgün & Sabriye İkizler & Orhan Tatar, 2013. "A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(3), pages 1481-1506, February.
    2. D. Kanungo & S. Sarkar & Shaifaly Sharma, 2011. "Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 59(3), pages 1491-1512, December.
    3. Nussaïbah B. Raja & Ihsan Çiçek & Necla Türkoğlu & Olgu Aydin & Akiyuki Kawasaki, 2017. "Landslide susceptibility mapping of the Sera River Basin using logistic regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(3), pages 1323-1346, February.
    4. Liu, Gang & He, Jing & Li, Ru & Li, Weile & Gao, Peichao & Lu, Jiayan & Long, Wen & Li, Lian & Tang, Min, 2018. "Topological and dynamic complexity of rock masses based on GIS and complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1240-1248.
    5. Christos Polykretis & Christos Chalkias, 2018. "Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(1), pages 249-274, August.
    6. Cheng Su & Lili Wang & Xizhi Wang & Zhicai Huang & Xiaocan Zhang, 2015. "Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(3), pages 1759-1779, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mustafa Kamal & Baolei Zhang & Jianfei Cao & Xin Zhang & Jun Chang, 2022. "Comparative Study of Artificial Neural Network and Random Forest Model for Susceptibility Assessment of Landslides Induced by Earthquake in the Western Sichuan Plateau, China," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    2. Junpeng Huang & Sixiang Ling & Xiyong Wu & Rui Deng, 2022. "GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility," Land, MDPI, vol. 11(3), pages 1-25, March.
    3. Jinming Zhang & Jianxi Qian & Yuefeng Lu & Xueyuan Li & Zhenqi Song, 2024. "Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    4. Xianmin Wang & Xinlong Zhang & Jia Bi & Xudong Zhang & Shiqiang Deng & Zhiwei Liu & Lizhe Wang & Haixiang Guo, 2022. "Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning," IJERPH, MDPI, vol. 19(21), pages 1-26, October.
    5. Rui-Xuan Tang & E-Chuan Yan & Tao Wen & Xiao-Meng Yin & Wei Tang, 2021. "Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 13(7), pages 1-25, March.
    6. Tingyu Zhang & Quan Fu & Chao Li & Fangfang Liu & Huanyuan Wang & Ling Han & Renata Pacheco Quevedo & Tianqing Chen & Na Lei, 2022. "Modeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 3327-3358, December.
    7. Lu Fang & Qian Wang & Jianping Yue & Yin Xing, 2023. "Analysis of Optimal Buffer Distance for Linear Hazard Factors in Landslide Susceptibility Prediction," Sustainability, MDPI, vol. 15(13), pages 1-17, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christos Polykretis & Manolis G. Grillakis & Athanasios V. Argyriou & Nikos Papadopoulos & Dimitrios D. Alexakis, 2021. "Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece," Land, MDPI, vol. 10(9), pages 1-23, September.
    2. Somnath Bera & Vaibhav Kumar Upadhyay & Balamurugan Guru & Thomas Oommen, 2021. "Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1257-1289, August.
    3. Christos Polykretis & Christos Chalkias, 2018. "Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(1), pages 249-274, August.
    4. Mária Barančoková & Matej Šošovička & Peter Barančok & Peter Barančok, 2021. "Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone," Land, MDPI, vol. 10(12), pages 1-28, December.
    5. Derya Ozturk & Nergiz Uzel-Gunini, 2022. "Investigation of the effects of hybrid modeling approaches, factor standardization, and categorical mapping on the performance of landslide susceptibility mapping in Van, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 2571-2604, December.
    6. Bilal Aslam & Adeel Zafar & Umer Khalil, 2023. "Comparative analysis of multiple conventional neural networks for landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 673-707, January.
    7. Moumita Palchaudhuri & Sujata Biswas, 2016. "Application of AHP with GIS in drought risk assessment for Puruliya district, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 1905-1920, December.
    8. Kourosh Shirani & Mehrdad Pasandi & Alireza Arabameri, 2018. "Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(3), pages 1379-1418, September.
    9. Di Wang & Mengmeng Hao & Shuai Chen & Ze Meng & Dong Jiang & Fangyu Ding, 2021. "Assessment of landslide susceptibility and risk factors in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 3045-3059, September.
    10. Jie Dou & Hiromitsu Yamagishi & Hamid Pourghasemi & Ali Yunus & Xuan Song & Yueren Xu & Zhongfan Zhu, 2015. "An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 1749-1776, September.
    11. Nhat-Duc Hoang & Quoc-Lam Nguyen & Xuan-Linh Tran, 2019. "Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis," Complexity, Hindawi, vol. 2019, pages 1-14, July.
    12. Gökhan Demir, 2018. "Landslide susceptibility mapping by using statistical analysis in the North Anatolian Fault Zone (NAFZ) on the northern part of Suşehri Town, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 133-154, May.
    13. Biswajeet Pradhan & Mohammed Abokharima & Mustafa Jebur & Mahyat Tehrany, 2014. "Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 1019-1042, September.
    14. Tirthankar Basu & Swades Pal, 2020. "A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4787-4819, June.
    15. Krishna Devkota & Amar Regmi & Hamid Pourghasemi & Kohki Yoshida & Biswajeet Pradhan & In Ryu & Megh Dhital & Omar Althuwaynee, 2013. "Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 135-165, January.
    16. Guru Balamurugan & Veerappan Ramesh & Mangminlen Touthang, 2016. "Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 465-488, October.
    17. Suvam Das & Shantanu Sarkar & Debi Prasanna Kanungo, 2023. "A critical review on landslide susceptibility zonation: recent trends, techniques, and practices in Indian Himalaya," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 23-72, January.
    18. Rajesh Kumar Dash & Philips Omowumi Falae & Debi Prasanna Kanungo, 2022. "Debris flow susceptibility zonation using statistical models in parts of Northwest Indian Himalayas—implementation, validation, and comparative evaluation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 2011-2058, March.
    19. Cahio Guimarães Seabra Eiras & Juliana Ribeiro Gonçalves de Souza & Renata Delicio Andrade de Freitas & César Falcão Barella & Tiago Martins Pereira, 2021. "Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(2), pages 1427-1442, June.
    20. Darya Golovko & Sigrid Roessner & Robert Behling & Birgit Kleinschmit, 2017. "Automated derivation and spatio-temporal analysis of landslide properties in southern Kyrgyzstan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(3), pages 1461-1488, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:103:y:2020:i:3:d:10.1007_s11069-020-04128-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.