IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i21p14241-d959207.html
   My bibliography  Save this article

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

Author

Listed:
  • Xianmin Wang

    (Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
    Key Laboratory of Geological and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Xinlong Zhang

    (Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Jia Bi

    (Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Xudong Zhang

    (Institute of Geological Survey of Tibet Autonomous Region, Lhasa 850000, China
    The Fifth Geological Brigade, Bureau of Geology and Mineral Exploration and Development of Tibet Autonomous Region, Glomud 816000, China)

  • Shiqiang Deng

    (The Fifth Geological Brigade, Bureau of Geology and Mineral Exploration and Development of Tibet Autonomous Region, Glomud 816000, China)

  • Zhiwei Liu

    (Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Lizhe Wang

    (Key Laboratory of Geological and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Haixiang Guo

    (Laboratory of Natural Disaster Risk Prevention and Emergency Management, School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

Abstract

Catastrophic landslides have much more frequently occurred worldwide due to increasing extreme rainfall events and intensified human engineering activity. Landslide susceptibility evaluation (LSE) is a vital and effective technique for the prevention and control of disastrous landslides. Moreover, about 80% of disastrous landslides had not been discovered ahead and significantly impeded social and economic sustainability development. However, the present studies on LSE mainly focus on the known landslides, neglect the great threat posed by the potential landslides, and thus to some degree constrain the precision and rationality of LSE maps. Moreover, at present, potential landslides are generally identified by the characteristics of surface deformation, terrain, and/or geomorphology. The essential disaster-inducing mechanism is neglected, which has caused relatively low accuracies and relatively high false alarms. Therefore, this work suggests new synthetic criteria of potential landslide identification. The criteria involve surface deformation, disaster-controlling features, and disaster-triggering characteristics and improve the recognition accuracy and lower the false alarm. Furthermore, this work combines the known landslides and discovered potential landslides to improve the precision and rationality of LSE. This work selects Chaya County, a representative region significantly threatened by landslides, as the study area and employs multisource data (geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data) to identify potential landslides and realize LSE based on the time-series InSAR technique and XGBoost algorithm. The LSE precision indices of AUC, Accuracy, TPR, F1-score, and Kappa coefficient reach 0.996, 97.98%, 98.77%, 0.98, and 0.96, respectively, and 16 potential landslides are newly discovered. Moreover, the development characteristics of potential landslides and the cause of high landslide susceptibility are illuminated. The proposed synthetic criteria of potential landslide identification and the LSE idea of combining known and potential landslides can be utilized to other disaster-serious regions in the world.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14241-:d:959207
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/21/14241/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/21/14241/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dimitris Kouhartsiouk & Skevi Perdikou, 2021. "The application of DInSAR and Bayesian statistics for the assessment of landslide susceptibility," 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. 105(3), pages 2957-2985, February.
    2. Anna Roccati & Guido Paliaga & Fabio Luino & Francesco Faccini & Laura Turconi, 2021. "GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment," Land, MDPI, vol. 10(2), pages 1-28, February.
    3. Pieter-Tjerk de Boer & Dirk Kroese & Shie Mannor & Reuven Rubinstein, 2005. "A Tutorial on the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 19-67, February.
    4. Muhammad Waseem & Zia Ur Rehman & Fabio Sabetta & Irshad Ahmad & Mahmood Ahmad & Mohanad Muayad Sabri Sabri, 2022. "Evaluation of the Predictive Performance of Regional and Global Ground Motion Predictive Equations for Shallow Active Regions in Pakistan," Sustainability, MDPI, vol. 14(13), pages 1-18, July.
    5. Charalampos Kontoes & Constantinos Loupasakis & Ioannis Papoutsis & Stavroula Alatza & Eleftheria Poyiadji & Athanassios Ganas & Christina Psychogyiou & Mariza Kaskara & Sylvia Antoniadi & Natalia Spa, 2021. "Landslide Susceptibility Mapping of Central and Western Greece, Combining NGI and WoE Methods, with Remote Sensing and Ground Truth Data," Land, MDPI, vol. 10(4), pages 1-25, April.
    6. Antonio Ganga & Mario Elia & Ersilia D’Ambrosio & Simona Tripaldi & Gian Franco Capra & Francesco Gentile & Giovanni Sanesi, 2022. "Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model," Sustainability, MDPI, vol. 14(14), pages 1-13, July.
    7. 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.
    8. Hunegnaw Desalegn & Arega Mulu & Banchiamlak Damtew, 2022. "Landslide susceptibility evaluation in the Chemoga watershed, upper Blue Nile, Ethiopia," 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. 113(2), pages 1391-1417, September.
    Full references (including those not matched with items on IDEAS)

    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. Michael Makonyo & Zahor Zahor, 2023. "GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania," 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. 118(2), pages 1085-1115, September.
    2. Paúl Carrión-Mero & Maribel Aguilar-Aguilar & Fernando Morante-Carballo & María José Domínguez-Cuesta & Cristhian Sánchez-Padilla & Andrés Sánchez-Zambrano & Josué Briones-Bitar & Roberto Blanco-Torre, 2021. "Surface and Underground Geomechanical Characterization of an Area Affected by Instability Phenomena in Zaruma Mining Zone (Ecuador)," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    3. Mattrand, C. & Bourinet, J.-M., 2014. "The cross-entropy method for reliability assessment of cracked structures subjected to random Markovian loads," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 171-182.
    4. R. Y. Rubinstein, 2005. "A Stochastic Minimum Cross-Entropy Method for Combinatorial Optimization and Rare-event Estimation," Methodology and Computing in Applied Probability, Springer, vol. 7(1), pages 5-50, March.
    5. Kin-Ping Hui, 2011. "Cooperative Cross-Entropy method for generating entangled networks," Annals of Operations Research, Springer, vol. 189(1), pages 205-214, September.
    6. Mathieu Balesdent & Jérôme Morio & Loïc Brevault, 2016. "Rare Event Probability Estimation in the Presence of Epistemic Uncertainty on Input Probability Distribution Parameters," Methodology and Computing in Applied Probability, Springer, vol. 18(1), pages 197-216, March.
    7. Tran, Cong Quoc & Keyvan-Ekbatani, Mehdi & Ngoduy, Dong & Watling, David, 2021. "Stochasticity and environmental cost inclusion for electric vehicles fast-charging facility deployment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    8. Vishal Balaji Devanand & Adam Mubeen & Zoran Vojinovic & Arlex Sanchez Torres & Guido Paliaga & Ahmad Fikri Abdullah & João P. Leitão & Natasa Manojlovic & Peter Fröhle, 2023. "Innovative Methods for Mapping the Suitability of Nature-Based Solutions for Landslide Risk Reduction," Land, MDPI, vol. 12(7), pages 1-15, July.
    9. Xi Chen & Enlu Zhou, 2015. "Population model-based optimization," Journal of Global Optimization, Springer, vol. 63(1), pages 125-148, September.
    10. Lvyang Qiu & Shuyu Li & Yunsick Sung, 2021. "3D-DCDAE: Unsupervised Music Latent Representations Learning Method Based on a Deep 3D Convolutional Denoising Autoencoder for Music Genre Classification," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    11. Zhou, Yuekuan & Zheng, Siqian, 2020. "Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization," Renewable Energy, Elsevier, vol. 153(C), pages 375-391.
    12. Akimoto, Youhei & Auger, Anne & Hansen, Nikolaus, 2022. "An ODE method to prove the geometric convergence of adaptive stochastic algorithms," Stochastic Processes and their Applications, Elsevier, vol. 145(C), pages 269-307.
    13. Anastasia Spiliopoulou & Ioannis Papamichail & Markos Papageorgiou & Yannis Tyrinopoulos & John Chrysoulakis, 2017. "Macroscopic traffic flow model calibration using different optimization algorithms," Operational Research, Springer, vol. 17(1), pages 145-164, April.
    14. Chao Chen & Jian Zhou & Tao Zhou & Weixun Yong, 2021. "Evaluation of vertical shaft stability in underground mines: comparison of three weight methods with uncertainty theory," 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. 109(2), pages 1457-1479, November.
    15. Huang, Zhenzhen & Kwok, Yue Kuen & Xu, Ziqing, 2024. "Efficient algorithms for calculating risk measures and risk contributions in copula credit risk models," Insurance: Mathematics and Economics, Elsevier, vol. 115(C), pages 132-150.
    16. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).
    17. Zhang, Yali & Shang, Pengjian, 2019. "Multivariate multiscale distribution entropy of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 72-80.
    18. Zhen Wu & Huiwen Zhang, 2023. "Numerical Study on the Influence of Block Physical Characteristics on Landslide Migration Using Three-Dimensional Discontinuous Deformation Analysis," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    19. He Yang & Qihong Wu & Jianhui Dong & Feihong Xie & Qixue Zhang, 2023. "Landslide Risk Mapping Using the Weight-of-Evidence Method in the Datong Mining Area, Qinghai Province," Sustainability, MDPI, vol. 15(14), pages 1-27, July.
    20. Chen, Siliang & Chen, Kang & Zhu, Xu & Jin, Xinqiao & Du, Zhimin, 2022. "Deep learning-based image recognition method for on-demand defrosting control to save energy in commercial energy systems," Applied Energy, Elsevier, vol. 324(C).

    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:gam:jijerp:v:19:y:2022:i:21:p:14241-:d:959207. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.