气候变化领域集成服务门户(CCPortal): Deep learning rapid flood risk predictions for climate resilience planning

CCPortal

DOI	10.1016/j.jhydrol.2024.130817
	Deep learning rapid flood risk predictions for climate resilience planning
	Yosri, Ahmed; Ghaith, Maysara; El-Dakhakhni, Wael
发表日期	2024
ISSN	0022-1694
EISSN	1879-2707
起始页码	631
卷号	631
英文摘要	Floods have been causing the world's costliest weather-related catastrophes and their magnitude and frequency are projected to increase even further due to climate change. Current flood risk quantification procedures include the use of complex and highly uncertain hydrologic-hydraulic models for hazard mapping and computationallytedious manipulations for vulnerability evaluation-hindering urban centers climate resilience planning. Adopting a novel approach that bypasses such time-consuming procedures, this study presents a deep learningbased rapid and accurate flood risk prediction tool, RAPFLO, to directly relate flood risk characteristics (level, extent, and likelihood) to their main drivers (e.g., climate, topography, and land cover). The approach employed to develop RAPFLO is generic in nature and the associated methodology is not site-dependent. To demonstrate its utility, RAPFLO is deployed on the City of Calgary, Canada, and is used to reproduce the fluvial flood risk across the city between the years 2010 and 2020. RAPFLO efficiently replicated the risk level with an overall accuracy of 80 % and the risk likelihood with a coefficient of determination of 0.96. Subsequently, RAPFLO was employed for predicting future fluvial flood risk from the year 2025 to 2100 under the RCP 8.5 climate scenario. RAPFLO presents a valuable computationally efficient, accurate, and rapid decision support system that empowers city managers and infrastructure operators to devise effective climate resilience strategies considering different climate projections and future what-if scenarios.
英文关键词	Climate Resilience; Deep learning; Flood Risk; Hierarchical Neural Network; Vulnerability Assessment
语种	英语
WOS研究方向	Engineering ; Geology ; Water Resources
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:001184220000001
来源期刊	JOURNAL OF HYDROLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297548
作者单位	McMaster University; Egyptian Knowledge Bank (EKB); Cairo University; McMaster University
推荐引用方式 GB/T 7714	Yosri, Ahmed,Ghaith, Maysara,El-Dakhakhni, Wael. Deep learning rapid flood risk predictions for climate resilience planning[J],2024,631.
APA	Yosri, Ahmed,Ghaith, Maysara,&El-Dakhakhni, Wael.(2024).Deep learning rapid flood risk predictions for climate resilience planning.JOURNAL OF HYDROLOGY,631.
MLA	Yosri, Ahmed,et al."Deep learning rapid flood risk predictions for climate resilience planning".JOURNAL OF HYDROLOGY 631(2024).