气候变化领域集成服务门户(CCPortal): Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring

CCPortal

DOI	10.2166/wst.2024.174
	Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring
	Zheng, Yue; Jing, Xiaoming; Lin, Yonggang; Shen, Dali; Zhang, Yiping; Yuan, Dongdong; Yu, Mingquan; Zhou, Yongchao
发表日期	2024
ISSN	0273-1223
EISSN	1996-9732
英文摘要	With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management. machine learning model to predict.
英文关键词	data-driven; flood prediction; monitoring point optimization; real-time monitoring; urban drainage system
语种	英语
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Water Resources
WOS类目	Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS记录号	WOS:001231898000001
来源期刊	WATER SCIENCE AND TECHNOLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296188
作者单位	Zhejiang University
推荐引用方式 GB/T 7714	Zheng, Yue,Jing, Xiaoming,Lin, Yonggang,et al. Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring[J],2024.
APA	Zheng, Yue.,Jing, Xiaoming.,Lin, Yonggang.,Shen, Dali.,Zhang, Yiping.,...&Zhou, Yongchao.(2024).Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring.WATER SCIENCE AND TECHNOLOGY.
MLA	Zheng, Yue,et al."Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring".WATER SCIENCE AND TECHNOLOGY (2024).