气候变化领域集成服务门户(CCPortal): Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models

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DOI	10.1007/s11069-021-04877-5
	Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models
	Ha H.; Luu C.; Bui Q.D.; Pham D.-H.; Hoang T.; Nguyen V.-P.; Vu M.T.; Pham B.T.
发表日期	2021
ISSN	0921030X
英文摘要	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. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	Flash flood susceptibility Flood risk management Highway Hybrid machine learning models Transportation Vietnam
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206260
作者单位	Departement of Geodesy and Geomatics, National University of Civil Engineering, Hanoi, 100000, Viet Nam; Faculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi, 100000, Viet Nam; Faculty of Bridges and Roads, National University of Civil Engineering, Hanoi, 100000, Viet Nam; University of Transport Technology, Hanoi, 100000, Viet Nam
推荐引用方式 GB/T 7714	Ha H.,Luu C.,Bui Q.D.,et al. Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models[J],2021.
APA	Ha H..,Luu C..,Bui Q.D..,Pham D.-H..,Hoang T..,...&Pham B.T..(2021).Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models.Natural Hazards.
MLA	Ha H.,et al."Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models".Natural Hazards (2021).