IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v39y2025i7d10.1007_s11269-024-04079-1.html
   My bibliography  Save this article

Influence of Geomorphological Parameters on Flash Flood Susceptibility Analyzed using a Coupled Approach of HEC-HMS Model and Logistic Regression

Author

Listed:
  • Zhenyue Han

    (Tianjin University)

  • Fawen Li

    (Tianjin University)

  • Chengshuai Liu

    (Zhengzhou University)

  • Xueli Zhang

    (Zhengzhou University)

  • Caihong Hu

    (Zhengzhou University)

Abstract

Flash floods pose significant challenges to the stable development of human society, highlighting the need for effective assessment and management of flash flood susceptibility (FFS). This research aims to explore the influence of geomorphological features on FFS using sub-basins as evaluation units, which provide scientific support for accurate flash flood early warning based on disaster monitoring and planning. Firstly, the Dali River Basin was chosen as the study area to simulate the flood processes under different rainfall scenarios using the HEC-HMS hydrological model. Then, the results of the flash flood occurrence under a 40mm-1h rainfall scenario were used to analyze the correlation between basin geomorphological characteristics and FFS, employing only-one-variable Logistic Regression (LR). Additionally, the Least Absolute Shrinkage Selection Operator (LASSO) was employed to select the relevant basin parameters. Subsequently, an FFS assessment model based on selected parameters was developed using LR. This study revealed a significant correlation between the basin shape and the drainage network with FFS, indicating that basins with an equidimensional shape and a well-developed drainage network are more prone to flash floods. The FFS assessment model constructed using geomorphological parameters achieved an Area Under the Curve (AUC) of 0.917 in the Dali River Basin, which can be effectively utilized for assessing FFS in the Dali River and other hydrologically similar basins. Graphical Abstract

Suggested Citation

  • Zhenyue Han & Fawen Li & Chengshuai Liu & Xueli Zhang & Caihong Hu, 2025. "Influence of Geomorphological Parameters on Flash Flood Susceptibility Analyzed using a Coupled Approach of HEC-HMS Model and Logistic Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(7), pages 3031-3051, May.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:7:d:10.1007_s11269-024-04079-1
    DOI: 10.1007/s11269-024-04079-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-04079-1
    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/s11269-024-04079-1?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Tao Jiang & Qiulian Wei & Ming Zhong & Jianfeng Li, 2024. "An Objective Framework for Bivariate Risk Analysis of Flash Floods Under the Compound Effect of Rainfall Characteristics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 2015-2037, April.
    2. Wenlin Yuan & Lu Lu & Hanzhen Song & Xiang Zhang & Linjuan Xu & Chengguo Su & Meiqi Liu & Denghua Yan & Zening Wu, 2022. "Study on the Early Warning for Flash Flood Based on Random Rainfall Pattern," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1587-1609, March.
    3. Romulus Costache & Alireza Arabameri & Iulia Costache & Anca Crăciun & Binh Thai Pham, 2022. "New Machine Learning Ensemble for Flood Susceptibility Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4765-4783, September.
    4. Davor Kvočka & Roger A. Falconer & Michaela Bray, 2016. "Flood hazard assessment for extreme flood events," 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 1569-1599, December.
    5. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    6. Hüseyin Akay, 2024. "Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5365-5383, October.
    7. Pardis Ziaee & Mohammad Javad Abedini, 2023. "Investigating the Effect of Spatial and Temporal Variabilities of Rainfall on Catchment Response," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(13), pages 5343-5366, October.
    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. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    2. Alberti, Federica & Mantilla, César, 2020. "Provision of noxious facilities using a market-like mechanism: A simple implementation in the lab," Working papers 35, Red Investigadores de Economía.
    3. Dinesh Roulo & Subbarao Pichuka, 2024. "Assessing the effects of extreme rainfall patterns and their impact on dam safety: a case study on Indian dam failures," 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. 120(14), pages 12967-12987, November.
    4. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Post-Print halshs-00917797, HAL.
    5. Sandro Radovanovic & Boris Delibasic & Milija Suknovic & Dajana Matovic, 2019. "Where will the next ski injury occur? A system for visual and predictive analytics of ski injuries," Operational Research, Springer, vol. 19(4), pages 973-992, December.
    6. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    7. Reutzel, Fabian, 2024. "The grass is always greener on the other side: (Unfair) inequality and support for democracy," European Journal of Political Economy, Elsevier, vol. 85(C).
    8. Zhang, Guike & Gao, Zengan & Dong, June & Mei, Dexiang, 2023. "Machine learning approaches for constructing the national anti-money laundering index," Finance Research Letters, Elsevier, vol. 52(C).
    9. Lee Anthony & Caron Francois & Doucet Arnaud & Holmes Chris, 2012. "Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-31, January.
    10. Dexin Chen & Meiting Fu & Liangjie Chi & Liyan Lin & Jiaxin Cheng & Weisong Xue & Chenyan Long & Wei Jiang & Xiaoyu Dong & Jian Sui & Dajia Lin & Jianping Lu & Shuangmu Zhuo & Side Liu & Guoxin Li & G, 2022. "Prognostic and predictive value of a pathomics signature in gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    11. Karim Solaimani & Fatemeh Shokrian & Shadman Darvishi, 2023. "An Assessment of the Integrated Multi-Criteria and New Models Efficiency in Watershed Flood Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 403-425, January.
    12. Ryota Nakamura & Martin Mäll & Tomoya Shibayama, 2019. "Street-scale storm surge load impact assessment using fine-resolution numerical modelling: a case study from Nemuro, 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. 99(1), pages 391-422, October.
    13. Na Li & Shenglian Guo & Feng Xiong & Jun Wang & Yuzuo Xie, 2022. "Comparative Study of Flood Coincidence Risk Estimation Methods in the Mainstream and its Tributaries," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 683-698, January.
    14. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2016. "The lasso for high dimensional regression with a possible change point," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 193-210, January.
    15. Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2014. "Efficient iterative maximum likelihood estimation of high-parameterized time series models," SFB 649 Discussion Papers 2014-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
    17. repec:hum:wpaper:sfb649dp2014-010 is not listed on IDEAS
    18. Hettihewa, Samanthala & Saha, Shrabani & Zhang, Hanxiong, 2018. "Does an aging population influence stock markets? Evidence from New Zealand," Economic Modelling, Elsevier, vol. 75(C), pages 142-158.
    19. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    20. Andrés Gómez & Oleg A. Prokopyev, 2021. "A Mixed-Integer Fractional Optimization Approach to Best Subset Selection," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 551-565, May.
    21. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:waterr:v:39:y:2025:i:7:d:10.1007_s11269-024-04079-1. 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.