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

Chinh Luu (), Quynh Duy Bui (), Romulus Costache, Luan Thanh Nguyen, Thu Thuy Nguyen, Tran Phong, Hiep Le and Binh Thai Pham ()
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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

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 108, issue 3, No 38, 3229-3251

Abstract: 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.

Keywords: Flood susceptibility map; Alternating decision tree; Logistic model tree; Reduced-error pruning tree; J48; Naïve Bayes tree (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-021-04821-7

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