气候变化领域集成服务门户(CCPortal): Dynamic spatial-temporal precipitation distribution models for short-duration rainstorms in Shenzhen, China based on machine learning

CCPortal

DOI	10.1016/j.atmosres.2020.104861
	Dynamic spatial-temporal precipitation distribution models for short-duration rainstorms in Shenzhen, China based on machine learning
	Liu Y.-Y.; Li L.; Liu Y.-S.; Chan P.W.; Zhang W.-H.
发表日期	2020
ISSN	0169-8095
卷号	237
英文摘要	Short-duration rainstorm, the main cause of urban waterlogging and mountain torrents, is characterized by sudden, intense, and highly destructive rainfall. Understanding the dynamic temporal and spatial distribution patterns of short-duration rainstorm can help to predict their development processes. In this study, the dynamic temporal and spatial distribution models of various types of short-duration rainstorm events were established by using machine learning (ML) method based on the rainfall data of the recent decade in a Chinese coastal megacity, Shenzhen. The dynamic characteristics of these rainstorm events were extracted by using ML method in conjunction with the Locally Linear Embedding algorithm, which shows a potential capability to predict the developmental trend of a heavy rainstorm before it occurs. Based on the method put forward in the current study, characteristic rainfall process models consistent with the local temporal and spatial distribution characteristics of rainstorms can be designed, which is important to understand the risks of the rainstorms and consequently helpful for the assessment of urban flood insurance, the scientific design of drainage systems and the forecasting and warning of urban waterlogging. © 2020 Elsevier B.V.
英文关键词	Dynamic spatial-temporal distribution; Locally linear embedding method; Machine learning; Shenzhen; Short-duration rainstorm
学科领域	Design; Embeddings; Flood insurance; Forecasting; Learning systems; Machine learning; Rain; Risk assessment; Thunderstorms; Dynamic characteristics; Dynamic spatial; Locally linear embedding; Locally linear embedding algorithms; Precipitation distribution; Shenzhen; Short durations; Temporal and spatial distribution; Spatial distribution; algorithm; climate modeling; drainage network; machine learning; precipitation assessment; rainfall; rainstorm; spatial distribution; spatiotemporal analysis; waterlogging; weather forecasting; China; Guangdong; Shenzhen
语种	英语
scopus关键词	Design; Embeddings; Flood insurance; Forecasting; Learning systems; Machine learning; Rain; Risk assessment; Thunderstorms; Dynamic characteristics; Dynamic spatial; Locally linear embedding; Locally linear embedding algorithms; Precipitation distribution; Shenzhen; Short durations; Temporal and spatial distribution; Spatial distribution; algorithm; climate modeling; drainage network; machine learning; precipitation assessment; rainfall; rainstorm; spatial distribution; spatiotemporal analysis; waterlogging; weather forecasting; China; Guangdong; Shenzhen
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120443
作者单位	School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai, 519082, China; China Institute of Water Resources and Hydropower Research, Beijing, 100038, China; Shenzhen National Climate Observatory, Shenzhen, 518040, China; Hong Kong Observatory, Kowloon, Hong Kong 999077, China; Shenzhen Academy of Severe Storms Science, Shenzhen, 518057, China
推荐引用方式 GB/T 7714	Liu Y.-Y.,Li L.,Liu Y.-S.,et al. Dynamic spatial-temporal precipitation distribution models for short-duration rainstorms in Shenzhen, China based on machine learning[J],2020,237.
APA	Liu Y.-Y.,Li L.,Liu Y.-S.,Chan P.W.,&Zhang W.-H..(2020).Dynamic spatial-temporal precipitation distribution models for short-duration rainstorms in Shenzhen, China based on machine learning.Atmospheric Research,237.
MLA	Liu Y.-Y.,et al."Dynamic spatial-temporal precipitation distribution models for short-duration rainstorms in Shenzhen, China based on machine learning".Atmospheric Research 237(2020).