IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v109y2021i1d10.1007_s11069-021-04877-5.html
   My bibliography  Save this article

Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models

Author

Listed:
  • Hang Ha

    (National University of Civil Engineering)

  • Chinh Luu

    (National University of Civil Engineering)

  • Quynh Duy Bui

    (National University of Civil Engineering)

  • Duy-Hoa Pham

    (National University of Civil Engineering)

  • Tung Hoang

    (National University of Civil Engineering)

  • Viet-Phuong Nguyen

    (National University of Civil Engineering)

  • Minh Tuan Vu

    (National University of Civil Engineering)

  • Binh Thai Pham

    (University of Transport Technology)

Abstract

Flash flood is one of the most common natural hazards affecting many mountainous areas. Previous studies explored flash flood susceptibility models; however, there is still a lack of case studies in the transport sector. This paper aimed to develop advanced hybrid machine learning (ML) algorithms for flash flood susceptibility modeling and mapping using data from the road network National Highway 6 in Hoa Binh province, Vietnam. A single ML model of reduced error pruning trees (REPT) and four hybrid ML models of Decorate-REPT, AdaBoostM1-REPT, Bagging-REPT, and MultiBoostAB-REPT were applied to develop flash flood susceptibility maps. Field surveys were conducted about the flash flood locations on the 115-km route length of the National Highway 6 in 2017, 2018, and 2019 flood events. This study used 88 flash flood locations and 14 flood conditioning factors to construct and validate the proposed models. Statistical metrics, including sensitivity, specificity, accuracy, root mean square error, and area under the receiver operating characteristic curve, were applied to evaluate the models’ performance and accuracy. The DCREPT model showed the best performance (AUC = 0.988) among the training models and had the highest prediction accuracy (AUC = 0.991) among the testing models. We found that 12,572 ha (Decorate-REPT), 9564 ha (AdaBoostM1-REPT), 11,954 ha (Bagging-REPT), 14,432 ha (MultiBoostAB-REPT), and 17,660 ha (REPT) of the 3-km buffer area of the highway are in the high- and very high-flash-flood-susceptibility areas. The proposed methodology could be potentially generalized to other transportation routes in mountainous areas to generate flash flood susceptibility prediction maps.

Suggested Citation

  • Hang Ha & Chinh Luu & Quynh Duy Bui & Duy-Hoa Pham & Tung Hoang & Viet-Phuong Nguyen & Minh Tuan Vu & Binh Thai Pham, 2021. "Flash flood susceptibility prediction mapping for a road network using hybrid machine learning 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. 109(1), pages 1247-1270, October.
    • Handle: RePEc:spr:nathaz:v:109:y:2021:i:1:d:10.1007_s11069-021-04877-5
    DOI: 10.1007/s11069-021-04877-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-04877-5
    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/s11069-021-04877-5?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. Floriana Esposito & Donato Malerba & Giovanni Semeraro & Valentina Tamma, 1999. "The effects of pruning methods on the predictive accuracy of induced decision trees," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 15(4), pages 277-299, October.
    2. Massimo Conforti & Pietro Aucelli & Gaetano Robustelli & Fabio Scarciglia, 2011. "Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, 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. 56(3), pages 881-898, March.
    3. Mohammed Sarfaraz Gani Adnan & Ashraf Dewan & Khatun E. Zannat & Abu Yousuf Md Abdullah, 2019. "The use of watershed geomorphic data in flash flood susceptibility zoning: a case study of the Karnaphuli and Sangu river basins of 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. 99(1), pages 425-448, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Quynh Duy Bui & Hang Ha & Dong Thanh Khuc & Dinh Quoc Nguyen & Jason von Meding & Lam Phuong Nguyen & Chinh Luu, 2023. "Landslide susceptibility prediction mapping with advanced ensemble models: Son La province, Vietnam," 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. 116(2), pages 2283-2309, March.
    2. Hang Ha & Quynh Duy Bui & Huy Dinh Nguyen & Binh Thai Pham & Trinh Dinh Lai & Chinh Luu, 2023. "A practical approach to flood hazard, vulnerability, and risk assessing and mapping for Quang Binh province, Vietnam," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1101-1130, February.
    3. Vishal Singh & Anil Kumar Lohani & Sanjay Kumar Jain, 2022. "Reconstruction of extreme flood events by performing integrated real-time and probabilistic flood modeling in the Periyar river basin, Southern 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. 112(3), pages 2433-2463, July.
    4. Sanjit Kumar & Bablu Kirar & Mayank Agarwal & Vishal Deshpande, 2023. "Application of novel hybrid machine learning techniques for particle Froude number estimation in sewer pipes," 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. 116(2), pages 1823-1842, March.
    5. Rofiat Bunmi Mudashiru & Nuridah Sabtu & Rozi Abdullah & Azlan Saleh & Ismail Abustan, 2022. "A comparison of three multi-criteria decision-making models in mapping flood hazard areas of Northeast Penang, Malaysia," 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 1903-1939, July.

    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. Sandipta Debanshi & Swades Pal, 2020. "Assessing gully erosion susceptibility in Mayurakshi river basin of eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 883-914, February.
    2. Jinzhao Li & Meilan Qi, 2015. "Local scour induced by upstream riverbed level lowering," 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(3), pages 1811-1827, July.
    3. Marta Jurchescu & Florina Grecu, 2015. "Modelling the occurrence of gullies at two spatial scales in the Olteţ Drainage Basin (Romania)," 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(1), pages 255-289, November.
    4. Rabin Chakrabortty & Subodh Chandra Pal & Mehebub Sahana & Ayan Mondal & Jie Dou & Binh Thai Pham & Ali P. Yunus, 2020. "Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern 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. 104(2), pages 1259-1294, November.
    5. Showmitra Kumar Sarkar & Saifullah Bin Ansar & Khondaker Mohammed Mohiuddin Ekram & Mehedi Hasan Khan & Swapan Talukdar & Mohd Waseem Naikoo & Abu Reza Towfiqul Islam & Atiqur Rahman & Amir Mosavi, 2022. "Developing Robust Flood Susceptibility Model with Small Numbers of Parameters in Highly Fertile Regions of Northwest Bangladesh for Sustainable Flood and Agriculture Management," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    6. 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.
    7. Alaa Ahmed & Abdullah Alrajhi & Abdulaziz Alquwaizany & Ali Al Maliki & Guna Hewa, 2022. "Flood Susceptibility Mapping Using Watershed Geomorphic Data in the Onkaparinga Basin, South Australia," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    8. Swades Pal & Sandipta Debanshi, 2018. "Influences of soil erosion susceptibility toward overloading vulnerability of the gully head bundhs in Mayurakshi River basin of eastern Chottanagpur Plateau," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(4), pages 1739-1775, August.
    9. Ali Azedou & Said Lahssini & Abdellatif Khattabi & Modeste Meliho & Nabil Rifai, 2021. "A Methodological Comparison of Three Models for Gully Erosion Susceptibility Mapping in the Rural Municipality of El Faid (Morocco)," Sustainability, MDPI, vol. 13(2), pages 1-30, January.
    10. Alaa Ahmed & Guna Hewa & Abdullah Alrajhi, 2021. "Flood susceptibility mapping using a geomorphometric approach in South Australian basins," 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 629-653, March.
    11. Shan-e-hyder Soomro & Caihong Hu & Muhammad Waseem Boota & Zubair Ahmed & Liu Chengshuai & Han Zhenyue & Li Xiang & Mairaj Hyder Alias Aamir Soomro, 2022. "River Flood Susceptibility and Basin Maturity Analyzed Using a Coupled Approach of Geo-morphometric Parameters and SWAT Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2131-2160, May.
    12. Hamed Ahmadpour & Ommolbanin Bazrafshan & Elham Rafiei-Sardooi & Hossein Zamani & Thomas Panagopoulos, 2021. "Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection," Sustainability, MDPI, vol. 13(18), pages 1-24, September.
    13. Md Shahinoor Rahman & Liping Di, 2020. "A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment," Agriculture, MDPI, vol. 10(4), pages 1-30, April.
    14. Prabal Barua, 2021. "Coping Practices Of Coastal Fishermen In Response To Climate Change For Southern Coastal Belt Of Bangladesh," Social Values & Society (SVS), Zibeline International Publishing, vol. 3(2), pages 74-80, October.
    15. Mustafa El-Rawy & Wael M. Elsadek & Florimond Smedt, 2023. "Flood hazard assessment and mitigation using a multi-criteria approach in the Sinai Peninsula, 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. 115(1), pages 215-236, January.
    16. Kennedy Were & Syphyline Kebeney & Harrison Churu & James Mumo Mutio & Ruth Njoroge & Denis Mugaa & Boniface Alkamoi & Wilson Ng’etich & Bal Ram Singh, 2023. "Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya," Land, MDPI, vol. 12(4), pages 1-19, April.
    17. Omid Rahmati & Ali Haghizadeh & Stefanos Stefanidis, 2016. "Assessing the Accuracy of GIS-Based Analytical Hierarchy Process for Watershed Prioritization; Gorganrood River Basin, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1131-1150, February.
    18. Dan Tran-Thanh & Aprilia Nidia Rinasti & Kavinda Gunasekara & Angsana Chaksan & Makoto Tsukiji, 2022. "GIS and Remote Sensing-Based Approach for Monitoring and Assessment of Plastic Leakage and Pollution Reduction in the Lower Mekong River Basin," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    19. Silvija Šiljeg & Rina Milošević & Marica Mamut, 2024. "Pluvial Flood Susceptibility in the Local Community of the City of Gospić (Croatia)," Sustainability, MDPI, vol. 16(4), pages 1-20, February.
    20. Yesen Liu & Yaohuan Huang & Jinhong Wan & Zhenshan Yang & Xiaolei Zhang, 2020. "Analysis of Human Activity Impact on Flash Floods in China from 1950 to 2015," Sustainability, MDPI, vol. 13(1), pages 1-12, December.

    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:109:y:2021:i:1:d:10.1007_s11069-021-04877-5. 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.