| HE Shijun,ZHOU Wenjun,ZHOU Ruyan,HUANG Dongmei. 2012. Study of tide prediction method influenced by nonperiodic factors based on support vector machines. Acta Oceanologica Sinica, (5):160－164 |
| Study of tide prediction method influenced by nonperiodic factors based on support vector machines |
| Study of tide prediction method influenced by nonperiodic factors based on support vector machines |
| Received:August 15, 2011 Revised:March 30, 2012 |
| DOI：10.1007/s13131-012-0245-5 |
| Key words:tidal prediction support vector machines celestial motion law harmonic analysis BP neural network nonperiodic factors |
| 中文关键词: tidal prediction support vector machines celestial motion law harmonic analysis BP neural network nonperiodic factors |
| 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. |
| Author Name | Affiliation | E-mail | | HE Shijun | College of information, Shanghai Ocean University, Shanghai 201306, China | heshijun6@163.com | | ZHOU Wenjun | College of information, Shanghai Ocean University, Shanghai 201306, China | | | ZHOU Ruyan | College of information, Shanghai Ocean University, Shanghai 201306, China | | | HUANG Dongmei | College of information, Shanghai Ocean University, Shanghai 201306, China | |
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| Abstract: |
| 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. |
| 中文摘要: |
| 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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