IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p16247-d994415.html
   My bibliography  Save this article

Evaluation of Geological Disaster Sensitivity in Shuicheng District Based on the WOE-RF Model

Author

Listed:
  • Zefang Zhang

    (School of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China)

  • Zhikuan Qian

    (School of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China)

  • Yong Wei

    (School of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China
    State Key Laboratory of Geological Disaster Prevention and Geological Environment Protection, Chengdu University of Technology, Chengdu 610059, China)

  • Xing Zhu

    (State Key Laboratory of Geological Disaster Prevention and Geological Environment Protection, Chengdu University of Technology, Chengdu 610059, China
    School of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, China)

  • Linjun Wang

    (School of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China)

Abstract

To improve the prevention and control of geological disasters in Shuicheng District, 10 environmental factors—slope, slope direction, curvature, NDVI, stratum lithology, distance from fault, distance from river system, annual average rainfall, distance from road and land use—were selected as evaluation indicators by integrating factors such as landform, basic geology, hydrometeorology and engineering activities. Based on the weight of evidence, random forest, support vector machine and BP neural network algorithms were introduced to build WOE-RF, WOE-SVM and WOE-BPNN models. The sensitivity of Shuicheng District to geological disasters was evaluated using the GIS platform, and the region was divided into areas of extremely high, high, medium, low and extremely low sensitivity to geological disasters. By comparing and analyzing the ROC curve and the distribution law of the sensitivity index, the AUC evaluation accuracy of the WOE-RF, WOE-SVM and WOE-BPNN models was 0.836, 0.807 and 0.753, respectively; the WOE-RF model was shown to be the most effective. In the WOE-RF model, the extremely high-, high-, medium-, low- and extremely low-sensitivity areas accounted for 15.9%, 16.9%, 19.3%, 21.0% and 26.9% of the study area, respectively. The extremely high- and high-sensitivity areas are mainly concentrated in areas with large slopes, broken rock masses, river systems and intensive human engineering activity. These research results are consistent with the actual situation and can provide a reference for the prevention and control of geological disasters in this and similar mountainous areas.

Suggested Citation

  • Zefang Zhang & Zhikuan Qian & Yong Wei & Xing Zhu & Linjun Wang, 2022. "Evaluation of Geological Disaster Sensitivity in Shuicheng District Based on the WOE-RF Model," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16247-:d:994415
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/16247/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/23/16247/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huadan Fan & Yuefeng Lu & Yulong Hu & Jun Fang & Chengzhe Lv & Changqing Xu & Xinyi Feng & Yanru Liu, 2022. "A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model," Sustainability, MDPI, vol. 14(13), pages 1-17, June.
    2. Wei Xie & Wen Nie & Pooya Saffari & Luis F. Robledo & Pierre-Yves Descote & Wenbin Jian, 2021. "Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, 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. 109(1), pages 931-948, October.
    3. 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.
    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. Chuhan Wang & Qigen Lin & Leibin Wang & Tong Jiang & Buda Su & Yanjun Wang & Sanjit Kumar Mondal & Jinlong Huang & Ying Wang, 2022. "The influences of the spatial extent selection for non-landslide samples on statistical-based landslide susceptibility modelling: a case study of Anhui Province 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. 112(3), pages 1967-1988, July.
    2. Eseosa Halima Ighile & Hiroaki Shirakawa & Hiroki Tanikawa, 2022. "Application of GIS and Machine Learning to Predict Flood Areas in Nigeria," Sustainability, MDPI, vol. 14(9), pages 1-33, April.
    3. R. O. E. Ulakpa & V.U.D. Okwu & K. E. Chukwu & M. O. Eyankware, 2020. "Landslide Susceptibility Modelling In Selected States Across Se. Nigeria," Environment & Ecosystem Science (EES), Zibeline International Publishing, vol. 4(1), pages 23-27, March.
    4. Xinfu Xing & Chenglong Wu & Jinhui Li & Xueyou Li & Limin Zhang & Rongjie He, 2021. "Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method," 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. 106(1), pages 97-117, March.
    5. 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.
    6. Sina Paryani & Aminreza Neshat & Saman Javadi & Biswajeet Pradhan, 2020. "Comparative performance of new hybrid ANFIS models in 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. 103(2), pages 1961-1988, September.
    7. Tahir Ali Akbar & Siddique Ullah & Waheed Ullah & Rafi Ullah & Raja Umer Sajjad & Abdullah Mohamed & Alamgir Khalil & Muhammad Faisal Javed & Anwarud Din, 2022. "Development and Application of Models for Landslide Hazards in Northern Pakistan," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    8. Bayes Ahmed, 2015. "Landslide susceptibility modelling applying user-defined weighting and data-driven statistical techniques in Cox’s Bazar Municipality, Bangladesh," 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. 79(3), pages 1707-1737, December.
    9. Sandeep Kumar & Vikram Gupta, 2021. "Evaluation of spatial probability of landslides using bivariate and multivariate approaches in the Goriganga valley, Kumaun Himalaya, 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. 109(3), pages 2461-2488, December.
    10. Hailang He & Weiwei Wang & Zhengxing Wang & Shu Li & Jianguo Chen, 2024. "Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning," Sustainability, MDPI, vol. 16(9), pages 1-24, May.
    11. 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.
    12. Jean Baptiste Nsengiyumva & Geping Luo & Egide Hakorimana & Richard Mind'je & Aboubakar Gasirabo & Valentine Mukanyandwi, 2019. "Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda," Risk Analysis, John Wiley & Sons, vol. 39(11), pages 2576-2595, November.
    13. Yigen Qin & Genlan Yang & Kunpeng Lu & Qianzheng Sun & Jin Xie & Yunwu Wu, 2021. "Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China," Sustainability, MDPI, vol. 13(11), pages 1-20, June.
    14. Idris Bello Yamusa & Mohd Suhaili Ismail & Abdulwaheed Tella, 2022. "Highway Proneness Appraisal to Landslides along Taiping to Ipoh Segment Malaysia, Using MCDM and GIS Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    15. Jing Li & Yuefeng Lu & Xiwen Li & Rui Wang & Ying Sun & Yanru Liu & Kaizhong Yao, 2023. "Evaluation and Analysis of Development Status of Yellow River Beach Area Based on Multi-Source Data and Coordination Degree Model," Sustainability, MDPI, vol. 15(7), pages 1-25, March.
    16. 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.
    17. Javeria Saleem & Sheikh Saeed Ahmad & Amna Butt, 2020. "Hazard risk assessment of landslide-prone sub-Himalayan region by employing geospatial modeling approach," 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. 102(3), pages 1497-1514, July.
    18. Prafull Singh & Ankit Sharma & Ujjwal Sur & Praveen Kumar Rai, 2021. "Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5233-5250, April.
    19. Yimin Li & Xuanlun Deng & Peikun Ji & Yiming Yang & Wenxue Jiang & Zhifang Zhao, 2022. "Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture," IJERPH, MDPI, vol. 19(21), pages 1-24, October.
    20. Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Farbod Farhangi & Soo-Mi Choi, 2021. "COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms," IJERPH, MDPI, vol. 18(18), pages 1-21, September.

    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:jsusta:v:14:y:2022:i:23:p:16247-:d:994415. 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.