气候变化领域集成服务门户(CCPortal): Rapid forecasting of urban flood inundation using multiple machine learning models

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DOI	10.1007/s11069-021-04782-x
	Rapid forecasting of urban flood inundation using multiple machine learning models
	Hou J.; Zhou N.; Chen G.; Huang M.; Bai G.
发表日期	2021
ISSN	0921030X
起始页码	2335
结束页码	2356
卷号	108 期号:2
英文摘要	Urban flood inundation is worsening as the number of short-duration rainstorms increases, and it is difficult to accurately predict urban flood inundation over a long lead time; however, the traditional hydrodynamic-based urban flood models still have difficulty realizing real-time prediction. This study establishes a rapid forecasting model of urban flood inundation based on machine learning (ML) algorithms and a hydrodynamic-based urban flood model. The ML model is obtained by training the simulation results of the hydrodynamic model and rainfall characteristic parameters. Part of Fengxi New Town, China, was used to validate the forecasting model. A comparison of ML predictions and hydrodynamic model simulations shows that when using one ML algorithm (random forest (RF) or K-nearest neighbor (KNN)) for inundation prediction, the accuracy of the inundation water volume and area is insufficient, with a maximum error of 28.56%. Combining the RF and KNN models can effectively improve the prediction accuracy and overall stability, the mean relative errors of the inundation area and depth are less than 5%, and the mean relative errors of the inundation volume can control within 10%. The simulated time of a single rainfall event can be controlled within 20 s, which can provide sufficient lead time for emergency decision-making, thereby helping decision-makers to take more appropriate measures against inundation. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	K-nearest neighbor model Machine learning Random forest model Rapid forecasting Urban inundation
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/206279
作者单位	State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an, Shaanxi 710048, China; Beijing Capital Co.Ltd., Bei’jing, 100044, China; Shaanxi Meteorological Service Center, Xi’an, Shaanxi 710014, China
推荐引用方式 GB/T 7714	Hou J.,Zhou N.,Chen G.,et al. Rapid forecasting of urban flood inundation using multiple machine learning models[J],2021,108(2).
APA	Hou J.,Zhou N.,Chen G.,Huang M.,&Bai G..(2021).Rapid forecasting of urban flood inundation using multiple machine learning models.Natural Hazards,108(2).
MLA	Hou J.,et al."Rapid forecasting of urban flood inundation using multiple machine learning models".Natural Hazards 108.2(2021).