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

HuiYuan Wang; ChangJiang Zhang; Jing Zeng

doi:10.1117/12.2550459

31 January 2020 Precipitation estimation by multi-time scale support vector machine with quantum optics inspired optimization algorithm

HuiYuan Wang, ChangJiang Zhang, Jing Zeng

Author Affiliations +

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114270O (2020) https://doi.org/10.1117/12.2550459
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

HuiYuan Wang, ChangJiang Zhang, and Jing Zeng "Precipitation estimation by multi-time scale support vector machine with quantum optics inspired optimization algorithm", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270O (31 January 2020); https://doi.org/10.1117/12.2550459

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