Study of tide prediction method influenced by nonperiodic factors based on support vector machines

HE Shijun ZHOU Wenjun ZHOU Ruyan HUANG Dongmei

HEShijun, ZHOUWenjun, ZHOURuyan, HUANGDongmei. Study of tide prediction method influenced by nonperiodic factors based on support vector machines[J]. 海洋学报英文版, 2012, (5): 160-164. doi: 10.1007/s13131-012-0245-5
引用本文: HEShijun, ZHOUWenjun, ZHOURuyan, HUANGDongmei. Study of tide prediction method influenced by nonperiodic factors based on support vector machines[J]. 海洋学报英文版, 2012, (5): 160-164. doi: 10.1007/s13131-012-0245-5
HE Shijun, ZHOU Wenjun, ZHOU Ruyan, HUANG Dongmei. Study of tide prediction method influenced by nonperiodic factors based on support vector machines[J]. Acta Oceanologica Sinica, 2012, (5): 160-164. doi: 10.1007/s13131-012-0245-5
Citation: HE Shijun, ZHOU Wenjun, ZHOU Ruyan, HUANG Dongmei. Study of tide prediction method influenced by nonperiodic factors based on support vector machines[J]. Acta Oceanologica Sinica, 2012, (5): 160-164. doi: 10.1007/s13131-012-0245-5

Study of tide prediction method influenced by nonperiodic factors based on support vector machines

doi: 10.1007/s13131-012-0245-5
基金项目: The Shanghai Committee of Science and Technology of China under contract No. 10510502800; the Graduate Student Education Innovation Program Foundation of Shanghai Municipal Education Commission of China; the National Key Science Foundation Research "973" Project of the Ministry of Science and Technology of China under contract No. 2012CB316200.

Study of tide prediction method influenced by nonperiodic factors based on support vector machines

  • 摘要: Harmonic analysis, the traditional tidal forecasting method, cannot take into account the impact of noncyclical factors, and is also based on the BP neural network tidal prediction model which is easily limited by the amount of data. According to the movement of celestial bodies, and considering the insufficient tidal characteristics of historical data which are impacted by the nonperiodic weather, a tidal prediction method is designed based on support vector machine (SVM) to carry out the simulation experiment by using tidal data from Xiamen Tide Gauge, Luchaogang Tide Gauge and Weifang Tide Gauge individually. And the results show that the model satisfactorily carries out the tide prediction which is influenced by noncyclical factors. At the same time, it also proves that the proposed prediction method, which when compared with harmonic analysis method and the BP neural network method, has faster modeling speed, higher prediction precision and stronger generalization ability.
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出版历程
  • 收稿日期:  2011-08-15
  • 修回日期:  2012-03-30

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