Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2019.05.006 |
Rapid identification of rainstorm disaster risks based on an artificial intelligence technology using the 2DPCA method | |
Liu, Yuan-yuan1; Li, Lei2,5; Zhang, Wen-hai3; Chan, Pak-wai4; Liu, Ye-sen1 | |
2019-10-01 | |
发表期刊 | ATMOSPHERIC RESEARCH |
ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2019 |
卷号 | 227页码:157-164 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
英文摘要 | An artificial intelligence technology with the two-dimensional principal component analysis (2DPCA) method is introduced into the early identification of rainstorm risks. The characteristic subspace for historical rainstorm events can be constructed through the 2DPCA method. When identifying an ongoing rainfall process, the most similar rainstorm event can be found through comparing the features of the ongoing rainfall to the events in the characteristic subspace. Taking the most similar historical rainstorm event found as a reference, the possible duration and intensity of the ongoing rainfall process can be estimated, thus achieving early identification of rainstorm risks. Two groups of validation experiments are performed based on a database including 116 rainstorm events observed in Shenzhen metropolis, China. The validation shows that although the ratio of historical events to validation events impacts the performance of identification, the identified historical events are generally similar to the ongoing rainfall processes in terms of rainfall duration, range of influence, magnitude, and maximum single-station rainfall. |
英文关键词 | Rainstorm Risk identification Artificial intelligence Machine learning 2DPCA |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000472688500014 |
WOS关键词 | NEURAL-NETWORKS |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/187175 |
专题 | 地球科学 |
作者单位 | 1.China Inst Water Resources & Hydropower Res, Beijing 100038, Peoples R China; 2.Shenzhen Natl Climate Observ, Shenzhen 518040, Peoples R China; 3.Shenzhen Acad Severe Storms Sci, Shenzhen 518057, Peoples R China; 4.Hong Kong Observ, Kowloon, Hong Kong 999077, Peoples R China; 5.Qixiang Rd 1,Zhuzilin St, Shenzhen 518040, Peoples R China |
推荐引用方式 GB/T 7714 | Liu, Yuan-yuan,Li, Lei,Zhang, Wen-hai,et al. Rapid identification of rainstorm disaster risks based on an artificial intelligence technology using the 2DPCA method[J]. ATMOSPHERIC RESEARCH,2019,227:157-164. |
APA | Liu, Yuan-yuan,Li, Lei,Zhang, Wen-hai,Chan, Pak-wai,&Liu, Ye-sen.(2019).Rapid identification of rainstorm disaster risks based on an artificial intelligence technology using the 2DPCA method.ATMOSPHERIC RESEARCH,227,157-164. |
MLA | Liu, Yuan-yuan,et al."Rapid identification of rainstorm disaster risks based on an artificial intelligence technology using the 2DPCA method".ATMOSPHERIC RESEARCH 227(2019):157-164. |
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