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Proceedings Paper

Precipitation estimation by multi-time scale support vector machine with quantum optics inspired optimization algorithm
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Paper Abstract

Summer precipitation estimation is one of the key and difficult tasks in short-term climate prediction because of the large amount of convective precipitation in summer which is characterized by uneven distribution, large intensity, short duration and rapid change with time. In order to improve the accuracy of summer precipitation estimation, an efficient method by multi-time scale Support Vector Machine (SVM) with quantum optics inspired optimization (QOIO) is proposed in this paper. And the performance of the proposed method is verified by radar reflectivity and precipitation data of automatic weather stations (AWSs) in Shanghai. Using radar reflectivity and precipitat ion in the most relevant time scale, a rainfall estimation model based on multi-time scale SVM is established for each AWS to estimate next 6-minute precipitation. Compared with the traditional single Z-R relationship, linear regression, K-nearest neighbor and ordinary SVM, the results show the higher Threat Score and lower root mean square error can be obtained by the proposed method in summer precipitation estimation.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270O (31 January 2020); doi: 10.1117/12.2550459
Show Author Affiliations
HuiYuan Wang, Zhejiang Normal Univ. (China)
ChangJiang Zhang, Zhejiang Normal Univ. (China)
Jing Zeng, Zhejiang Normal Univ. (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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