IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i15p2801-d255139.html
   My bibliography  Save this article

Geospatial Analysis of Mass-Wasting Susceptibility of Four Small Catchments in Mountainous Area of Miyun County, Beijing

Author

Listed:
  • Chen Cao

    (College of Construction Engineering, Jilin University, Changchun, Jilin 130026, China)

  • Jianping Chen

    (College of Construction Engineering, Jilin University, Changchun, Jilin 130026, China)

  • Wen Zhang

    (College of Construction Engineering, Jilin University, Changchun, Jilin 130026, China)

  • Peihua Xu

    (College of Construction Engineering, Jilin University, Changchun, Jilin 130026, China)

  • Lianjing Zheng

    (Department of Architectural Engineering, Changchun Sci-Tech University, Changchun, Jilin 130600, China)

  • Chun Zhu

    (State Key Laboratory for Geomechanic and Deep Underground Engineering, China University of Mining and Technology, Beijing 100083, China)

Abstract

Driven by the pull of gravity, mass-wasting comprises all of the sedimentary processes related to remobilization of sediments deposited on slopes, including creep, sliding, slumping, flow, and fall. It is vital to conduct mass-wasting susceptibility mapping, with the aim of providing decision makers with management advice. The current study presents two individual data mining methods—the frequency ratio (FR) and information value model (IVM) methods—to map mass-wasting susceptibility in four catchments in Miyun County, Beijing, China. To achieve this goal, nine influence factors and a mass-wasting inventory map were used and produced, respectively. In this study, 71 mass-wasting locations were investigated in the field. Of these hazard locations, 70% of them were randomly selected to build the model, and the remaining 30% of the hazard locations were used for validation. Finally, a receiver operating characteristic (ROC) curve was used to assess the mass-wasting susceptibility maps produced by the above-mentioned models. Results show that the FR had a higher concordance and spatial differentiation, with respective values of 0.902 (area under the success rate) and 0.883 (area under the prediction rate), while the IVM had lower values of 0.865 (area under the success rate) and 0.855 (area under the prediction rate). Both proposed methodologies are useful for general planning and evaluation purposes, and they are shown to be reasonable models. Slopes of 6–21° were the most common thresholds that controlled occurrence of mass-wasting. Farmland terraces were mainly composed of gravel, mud, and clay, which are more prone to mass-wasting. Mass-wasting susceptibility mapping is feasible and potentially highly valuable. It could provide useful information in support of environmental health policies.

Suggested Citation

  • Chen Cao & Jianping Chen & Wen Zhang & Peihua Xu & Lianjing Zheng & Chun Zhu, 2019. "Geospatial Analysis of Mass-Wasting Susceptibility of Four Small Catchments in Mountainous Area of Miyun County, Beijing," IJERPH, MDPI, vol. 16(15), pages 1-19, August.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:15:p:2801-:d:255139
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/15/2801/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/16/15/2801/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. F. Falaschi & F. Giacomelli & P. Federici & A. Puccinelli & G. D’Amato Avanzi & A. Pochini & A. Ribolini, 2009. "Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy," 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. 50(3), pages 551-569, September.
    2. H. Pourghasemi & H. Moradi & S. Fatemi Aghda, 2013. "Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances," 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. 69(1), pages 749-779, October.
    3. Hamid Pourghasemi & Biswajeet Pradhan & Candan Gokceoglu, 2012. "Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, 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. 63(2), pages 965-996, September.
    4. Dieu Bui & Owe Lofman & Inge Revhaug & Oystein Dick, 2011. "Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression," 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 1413-1444, December.
    5. W. Botzen & J. Aerts & J. Bergh, 2013. "Individual preferences for reducing flood risk to near zero through elevation," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 18(2), pages 229-244, February.
    6. L. Sharma & Nilanchal Patel & M. Ghose & P. Debnath, 2015. "Development and application of Shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalayas in 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. 75(2), pages 1555-1576, January.
    7. Kyle W. Rowden & Mohamed H. Aly, 2018. "A novel triggerless approach for mass wasting susceptibility modeling applied to the Boston Mountains of Arkansas, USA," 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 347-367, May.
    8. Chen Cao & Peihua Xu & Yihong Wang & Jianping Chen & Lianjing Zheng & Cencen Niu, 2016. "Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas," Sustainability, MDPI, vol. 8(9), pages 1-18, 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. Chen Cao & Peihua Xu & Yihong Wang & Jianping Chen & Lianjing Zheng & Cencen Niu, 2016. "Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas," Sustainability, MDPI, vol. 8(9), pages 1-18, September.
    2. 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.
    3. 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.
    4. Netra Bhandary & Ranjan Dahal & Manita Timilsina & Ryuichi Yatabe, 2013. "Rainfall event-based landslide susceptibility zonation 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. 69(1), pages 365-388, October.
    5. Rajesh Khatakho & Dipendra Gautam & Komal Raj Aryal & Vishnu Prasad Pandey & Rajesh Rupakhety & Suraj Lamichhane & Yi-Chung Liu & Khameis Abdouli & Rocky Talchabhadel & Bhesh Raj Thapa & Rabindra Adhi, 2021. "Multi-Hazard Risk Assessment of Kathmandu Valley, Nepal," Sustainability, MDPI, vol. 13(10), pages 1-27, May.
    6. Amin Salehpour Jam & Jamal Mosaffaie & Faramarz Sarfaraz & Samad Shadfar & Rouhangiz Akhtari, 2021. "GIS-based landslide susceptibility mapping using hybrid MCDM 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. 108(1), pages 1025-1046, August.
    7. Chi Yang & Jinghan Wang & Shuyi Li & Ruihan Xiong & Xiaobo Li & Lin Gao & Xu Guo & Chuanming Ma & Hanxiang Xiong & Yang Qiu, 2024. "Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China," Sustainability, MDPI, vol. 16(11), pages 1-26, May.
    8. 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.
    9. Chonghao Zhu & Jianjing Zhang & Yang Liu & Donghua Ma & Mengfang Li & Bo Xiang, 2020. "Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, 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. 100(1), pages 173-204, January.
    10. Fujun Niu & Jing Luo & Zhanju Lin & Minhao Liu & Guoan Yin, 2014. "Thaw-induced slope failures and susceptibility mapping in permafrost regions of the Qinghai–Tibet Engineering Corridor, 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. 74(3), pages 1667-1682, December.
    11. Ahmad Rajabi & Saeid Shabanlou & Fariborz Yosefvand & Afshin Kiani, 2021. "Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, 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. 109(1), pages 871-901, October.
    12. Mahmoud Rezaei & Farshad Amiraslani & Najmeh Neysani Samani & Kazem Alavipanah, 2020. "Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran," 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. 100(1), pages 363-385, January.
    13. Bosy A. El-Haddad & Ahmed M. Youssef & Hamid R. Pourghasemi & Biswajeet Pradhan & Abdel-Hamid El-Shater & Mohamed H. El-Khashab, 2021. "Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt," 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(1), pages 83-114, January.
    14. Hassan Abedi Gheshlaghi & Bakhtiar Feizizadeh, 2021. "GIS-based ensemble modelling of fuzzy system and bivariate statistics as a tool to improve the accuracy of 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. 107(2), pages 1981-2014, June.
    15. Alaa M. Al-Abadi & Noor A. Al-Najar, 2020. "Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness," 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. 100(2), pages 461-491, January.
    16. Abdulwaheed Tella & Abdul-Lateef Balogun, 2020. "Ensemble fuzzy MCDM for spatial assessment of flood susceptibility in Ibadan, Nigeria," 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. 104(3), pages 2277-2306, December.
    17. Hossain, Mohammad Khalid & Meng, Qingmin, 2020. "A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood," Land Use Policy, Elsevier, vol. 99(C).
    18. Vangelis Pitidis & Deodato Tapete & Jon Coaffee & Leon Kapetas & João Porto de Albuquerque, 2018. "Understanding the Implementation Challenges of Urban Resilience Policies: Investigating the Influence of Urban Geological Risk in Thessaloniki, Greece," Sustainability, MDPI, vol. 10(10), pages 1-24, October.
    19. Txomin Bornaetxea & Juan Remondo & Jaime Bonachea & Pablo Valenzuela, 2023. "Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain)," 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(3), pages 2513-2542, September.
    20. 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.

    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:16:y:2019:i:15:p:2801-:d:255139. 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.