DOI: 10.1007/s11069-021-04821-7
论文题名: Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam
作者: Luu C. ; Bui Q.D. ; Costache R. ; Nguyen L.T. ; Nguyen T.T. ; Van Phong T. ; Van Le H. ; Pham B.T.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 108, 期: 3 起始页码: 3229
结束页码: 3251
语种: 英语
中文关键词: Alternating decision tree
; Flood susceptibility map
; J48
; Logistic model tree
; Naïve Bayes tree
; Reduced-error pruning tree
英文关键词: accuracy assessment
; error analysis
; flood control
; flood frequency
; flooding
; hazard assessment
; logistics
; machine learning
; model validation
; Quang Binh
; Viet Nam
英文摘要: 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. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169193
Appears in Collections: 气候变化与战略
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作者单位: Faculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi, Viet Nam; Department of Geodesy, National University of Civil Engineering, Hanoi, Viet Nam; Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, Brasov, 500152, Romania; Key Laboratory of River and Coastal Engineering, Vietnam Academy for Water Resources, Hanoi, Viet Nam; Center for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; Institute of Geological Sciences, Vietnam Academy of Science and Technology, Hanoi, Viet Nam; University of Transport Technology, Hanoi, Viet Nam
Recommended Citation:
Luu C.,Bui Q.D.,Costache R.,et al. Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam[J]. Natural Hazards,2021-01-01,108(3)