DOI: 10.1007/s11069-021-04782-x
论文题名: Rapid forecasting of urban flood inundation using multiple machine learning models
作者: Hou J. ; Zhou N. ; Chen G. ; Huang M. ; Bai G.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 108, 期: 2 起始页码: 2335
结束页码: 2356
语种: 英语
中文关键词: K-nearest neighbor model
; Machine learning
; Random forest model
; Rapid forecasting
; Urban inundation
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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169078
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
Hou J.,Zhou N.,Chen G.,et al. Rapid forecasting of urban flood inundation using multiple machine learning models[J]. Natural Hazards,2021-01-01,108(2)