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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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    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.

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