IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v117y2023i3d10.1007_s11069-023-05983-2.html
   My bibliography  Save this article

Integrating development inhomogeneity into geological disasters risk assessment framework in mountainous areas: a case study in Lushan–Baoxing counties, Southwestern China

Author

Listed:
  • Yufeng He

    (Xipu Campus of Southwest Communication University)

  • Mingtao Ding

    (Xipu Campus of Southwest Communication University)

  • Hao Zheng

    (Xipu Campus of Southwest Communication University)

  • Zemin Gao

    (Xipu Campus of Southwest Communication University)

  • Tao Huang

    (Xipu Campus of Southwest Communication University)

  • Yu Duan

    (Southwest Petroleum University)

  • Xingjie Cui

    (Southwest Petroleum University)

  • Siyuan Luo

    (Southwest Petroleum University)

Abstract

The impact of geological disasters on mountainous settlement have been focused on in recent years. Despite the booming development in the modeling of hazards and vulnerability, the risk assessment in mountainous area still have difficulty in absence of refined data. Moreover, development imbalances widely exist in mountainous areas, which were ignored in previous research. In this study, the refined distribution of socioeconomic data is obtained by using spatialization from the census, which eases the situation of the lack of data. Then, a frequency ratio-random forest model is conducted to evaluate the geological disasters hazards. Meanwhile, vulnerability was evaluated using triangular fuzzy number‑based analytic hierarchy process. In vulnerability assessment, the inhomogeneity index is integrated to evaluate the imbalance between indicators, the use of which can reward a more realistic vulnerability result. Finally, risk map was produced by multiplying hazard and vulnerability. The risk assessment framework is applied in Lushan and Baoxing counties of Southwestern China, which is a typical mountainous area with frequent earthquakes, uneven development, and a lack of high-precision data. The total area of high and extremely high hazards (868.82 km2), vulnerability (258.66 km2), and risk (113.49 km2) are estimated and mapped. The proposed risk assessment framework quantifies the impact of development inhomogeneity on risk and contributes to the scientific assessment of vulnerability for mountainous settlements.

Suggested Citation

  • Yufeng He & Mingtao Ding & Hao Zheng & Zemin Gao & Tao Huang & Yu Duan & Xingjie Cui & Siyuan Luo, 2023. "Integrating development inhomogeneity into geological disasters risk assessment framework in mountainous areas: a case study in Lushan–Baoxing counties, Southwestern 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. 117(3), pages 3203-3229, July.
  • Handle: RePEc:spr:nathaz:v:117:y:2023:i:3:d:10.1007_s11069-023-05983-2
    DOI: 10.1007/s11069-023-05983-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-05983-2
    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/s11069-023-05983-2?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. Zemin Gao & Mingtao Ding, 2022. "Application of convolutional neural network fused with machine learning modeling framework for geospatial comparative analysis 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. 113(2), pages 833-858, September.
    2. Chengguang Lai & Xiaohong Chen & Zhaoli Wang & Haijun Yu & Xiaoyan Bai, 2020. "Flood Risk Assessment and Regionalization from Past and Future Perspectives at Basin Scale," Risk Analysis, John Wiley & Sons, vol. 40(7), pages 1399-1417, July.
    3. Mao, Ning & Song, Mengjie & Deng, Shiming, 2016. "Application of TOPSIS method in evaluating the effects of supply vane angle of a task/ambient air conditioning system on energy utilization and thermal comfort," Applied Energy, Elsevier, vol. 180(C), pages 536-545.
    4. Ataollah Shirzadi & Lee Saro & Oh Hyun Joo & Kamran Chapi, 2012. "A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, 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. 64(2), pages 1639-1656, November.
    5. Tom McLeod Logan & Terje Aven & Seth David Guikema & Roger Flage, 2022. "Risk science offers an integrated approach to resilience," Nature Sustainability, Nature, vol. 5(9), pages 741-748, September.
    6. Sasenarine Tomby & Jing Zhang, 2019. "Vulnerability assessment of Guyanese sugar to floods," Climatic Change, Springer, vol. 154(1), pages 179-193, May.
    7. Yong-Ling Zhang & Wen-Jiao You, 2014. "Social vulnerability to floods: a case study of Huaihe River Basin," 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. 71(3), pages 2113-2125, April.
    8. Eun-Sung Chung & Patricia Jitta Abdulai & Hyesun Park & Yeonjoo Kim & So Ra Ahn & Seong Joon Kim, 2016. "Multi-Criteria Assessment of Spatial Robust Water Resource Vulnerability Using the TOPSIS Method Coupled with Objective and Subjective Weights in the Han River Basin," Sustainability, MDPI, vol. 9(1), pages 1-17, December.
    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. Jianlin Ren & Shasha Duan & Leihong Guo & Hongwan Li & Xiangfei Kong, 2022. "Effects of Return Air Inlets’ Location on the Control of Fine Particle Transportation in a Simulated Hospital Ward," IJERPH, MDPI, vol. 19(18), pages 1-21, September.
    2. Zhang, Sheng & Lin, Zhang & Ai, Zhengtao & Huan, Chao & Cheng, Yong & Wang, Fenghao, 2019. "Multi-criteria performance optimization for operation of stratum ventilation under heating mode," Applied Energy, Elsevier, vol. 239(C), pages 969-980.
    3. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    4. Gowhar Meraj & Shakil Romshoo & A. Yousuf & Sadaff Altaf & Farrukh Altaf, 2015. "Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir 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. 77(1), pages 153-175, May.
    5. Wei, Lu & Jing, Haozhe & Huang, Jie & Deng, Yuqi & Jing, Zhongbo, 2023. "Do textual risk disclosures reveal corporate risk? Evidence from U.S. fintech corporations," Economic Modelling, Elsevier, vol. 127(C).
    6. Haoyuan Hong & Himan Shahabi & Ataollah Shirzadi & Wei Chen & Kamran Chapi & Baharin Bin Ahmad & Majid Shadman Roodposhti & Arastoo Yari Hesar & Yingying Tian & Dieu Tien Bui, 2019. "Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning 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. 96(1), pages 173-212, March.
    7. Binh Thai Pham & Ataollah Shirzadi & Himan Shahabi & Ebrahim Omidvar & Sushant K. Singh & Mehebub Sahana & Dawood Talebpour Asl & Baharin Bin Ahmad & Nguyen Kim Quoc & Saro Lee, 2019. "Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
    8. Zizhen Xu & Shauhrat S. Chopra, 2023. "Interconnectedness enhances network resilience of multimodal public transportation systems for Safe-to-Fail urban mobility," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    9. Wan, Taocheng & Bai, Yan & Wang, Tingxiang & Wei, Zhuo, 2022. "BPNN-based optimal strategy for dynamic energy optimization with providing proper thermal comfort under the different outdoor air temperatures," Applied Energy, Elsevier, vol. 313(C).
    10. Wenmin Qin & Aiwen Lin & Jian Fang & Lunche Wang & Man Li, 2017. "Spatial and temporal evolution of community resilience to natural hazards in the coastal areas of 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. 89(1), pages 331-349, October.
    11. Haoran Wang & Mengdi Zhang & Chuanying Wang & Kaiyue Wang & Chen Wang & Yang Li & Xiuling Bai & Yunkai Zhou, 2022. "Spatial and Temporal Changes of Landscape Patterns and Their Effects on Ecosystem Services in the Huaihe River Basin, China," Land, MDPI, vol. 11(4), pages 1-19, April.
    12. Binh Thai Pham & Indra Prakash & Wei Chen & Hai-Bang Ly & Lanh Si Ho & Ebrahim Omidvar & Van Phong Tran & Dieu Tien Bui, 2019. "A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 11(22), pages 1-30, November.
    13. Qiang Liu & Delong Huang & Aiping Tang & Xiaosheng Han, 2021. "Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics," 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 1047-1067, August.
    14. Francisco Parra & Jaime González & Max Chacón & Mauricio Marín, 2023. "Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile," Sustainability, MDPI, vol. 15(24), pages 1-31, December.
    15. Zhang, Sheng & Lu, Yalin & Niu, Dun & Lin, Zhang, 2022. "Energy performance index of air distribution: Thermal utilization effectiveness," Applied Energy, Elsevier, vol. 307(C).
    16. Eric Tate & Md Asif Rahman & Christopher T. Emrich & Christopher C. Sampson, 2021. "Flood exposure and social vulnerability in the United States," 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 435-457, March.
    17. Zhang, Sheng & Cheng, Yong & Oladokun, Majeed Olaide & Huan, Chao & Lin, Zhang, 2019. "Heat removal efficiency of stratum ventilation for air-side modulation," Applied Energy, Elsevier, vol. 238(C), pages 1237-1249.
    18. Peipei You & Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "Operation Performance Evaluation of Power Grid Enterprise Using a Hybrid BWM-TOPSIS Method," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    19. 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.
    20. Mao, Ning & Pan, Dongmei & Li, Zhao & Xu, Yingjie & Song, Mengjie & Deng, Shiming, 2017. "A numerical study on influences of building envelope heat gain on operating performances of a bed-based task/ambient air conditioning (TAC) system in energy saving and thermal comfort," Applied Energy, Elsevier, vol. 192(C), pages 213-221.

    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:117:y:2023:i:3:d:10.1007_s11069-023-05983-2. 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.