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Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam

Author

Listed:
  • Chinh Luu

    (National University of Civil Engineering)

  • Quynh Duy Bui

    (National University of Civil Engineering)

  • Romulus Costache

    (Transilvania University of Brasov)

  • Luan Thanh Nguyen

    (Vietnam Academy for Water Resources)

  • Thu Thuy Nguyen

    (University of Technology Sydney)

  • Tran Phong

    (Vietnam Academy of Science and Technology)

  • Hiep Le

    (University of Transport Technology)

  • Binh Thai Pham

    (University of Transport Technology)

Abstract

Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In particular, Quang Binh province is often affected by floods and storms over the year. However, it still lacks studies on flood hazard estimation and prediction tools in this area. This study aims to develop a flooding susceptibility assessment tool using various machine learning (ML) techniques namely alternating decision tree (AD Tree), logistic model tree (LM Tree), reduced-error pruning tree (REP Tree), J48 decision tree (J48) and Naïve Bayes tree (NB Tree); historical flood marks; and available data of topography, hydrology, geology, and environment considering Quang Binh province as a study area. We used flood mark locations of major flooding events in the years 2007, 2010, and 2016; and ten flood conditioning factors to construct and validate the ML models. Various validation methods, including area under the ROC curve (AUC), were used to validate and compare the models. The result of the models’ validation suggests that all models have good performance: AD Tree (AUC = 0.968), LM Tree (AUC = 0.967), REP Tree (AUC = 0.897), J48 (AUC = 0.953), and NB Tree (AUC = 0.986). Out of these, NB Tree managed to achieve the best performance in terms of flood prediction with an accuracy higher than 92 %. The final flood susceptibility map highlights 6,265 km2 (78.8 % area) with a very low flooding hazard, 391 km2 (4.9 % area) with a low flooding hazard, 224 km2 (2.8 % area) with a moderate flooding hazard, 243 km2 (3.1 %) with a high flooding hazard, and 829 km2 (10.4 % area) with very high flooding hazard. The final flooding susceptibility assessment map could add a valuable source for flood risk reduction and management activities of Quang Binh province.

Suggested Citation

  • Chinh Luu & Quynh Duy Bui & Romulus Costache & Luan Thanh Nguyen & Thu Thuy Nguyen & Tran Phong & Hiep Le & Binh Thai Pham, 2021. "Flood-prone area mapping using machine learning techniques: a case study of Quang Binh 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. 108(3), pages 3229-3251, September.
  • Handle: RePEc:spr:nathaz:v:108:y:2021:i:3:d:10.1007_s11069-021-04821-7
    DOI: 10.1007/s11069-021-04821-7
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    References listed on IDEAS

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    1. Albano, Raffaele & Mancusi, Leonardo & Abbate, Andrea, 2017. "Improving flood risk analysis for effectively supporting the implementation of flood risk management plans: The case study of “Serio” Valley," Environmental Science & Policy, Elsevier, vol. 75(C), pages 158-172.
    2. Chinh Luu & Jason Meding & Sittimont Kanjanabootra, 2018. "Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, 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. 90(3), pages 1031-1050, February.
    3. Sebastian Scheuer & Dagmar Haase & Volker Meyer, 2011. "Exploring multicriteria flood vulnerability by integrating economic, social and ecological dimensions of flood risk and coping capacity: from a starting point view towards an end point view of vulnera," 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. 58(2), pages 731-751, August.
    4. Chinh Luu & Hieu Xuan Tran & Binh Thai Pham & Nadhir Al-Ansari & Thai Quoc Tran & Nga Quynh Duong & Nam Hai Dao & Lam Phuong Nguyen & Huu Duy Nguyen & Huong Thu Ta & Hiep Van Le & Jason von Meding, 2020. "Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
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    4. Maelaynayn El baida & Mohamed Hosni & Farid Boushaba & Mimoun Chourak, 2024. "A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 5823-5864, December.
    5. Erfan Mahmoodi & Mahmood Azari & Mohammad Taghi Dastorani & Aryan Salvati, 2024. "Comparison of Hydrological Modeling, Artificial Neural Networks and Multi-Criteria Decision Making Approaches for Determining Flood Source Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5343-5363, October.
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    • J48 - Labor and Demographic Economics - - Particular Labor Markets - - - Particular Labor Markets; Public Policy

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