基于小波变换和支持向量机的中国大陆强震预测
|
摘要
将小波变换和支持向量机用于中国大陆年度最大地震震级预测。先用小波变换把中国大陆年度最大地震序列分解成几个不同尺度水平(频率)的子序列,然后使用支持向量机对分解后的子序列分别进行预测,最后通过重构几个子序列的支持向量机预测结果得到最终预测结果,预测次年中国大陆最大地震震级。与支持向量机和神经网络方法对比,结果表明小波变换和支持向量机相结合方法具有更高的预测精度,预测效果很好,说明此方法可用于地震时间序列预测。
In this paper,the model based on wavelet transform and support vector machine(SVM) was applied to forecast the annual maximum earthquake magnitude in China's mainland.Firstly,using wavelet transform the series of the annual maximum earthquake sequence in china's mainland was decomposed into several sub-sequences with different scales(frequency band).Secondly,the support vector machine was applied to forecast the decomposed sub-sequences,respectively.Finally,the reconstruction of forecasted sub-sequences was used as the final predicted result,to forecast the maximum earthquake magnitude in China's mainland next year.The comparison of SVM with ANN method was made.The forecasted results show that our method possesses higher prediction precision and excellent forecasting effect,and prove that the method to combine wavelet transform with SVM is feasible to forecast time series of strong earthquakes.
In this paper,the model based on wavelet transform and support vector machine(SVM) was applied to forecast the annual maximum earthquake magnitude in China's mainland.Firstly,using wavelet transform the series of the annual maximum earthquake sequence in china's mainland was decomposed into several sub-sequences with different scales(frequency band).Secondly,the support vector machine was applied to forecast the decomposed sub-sequences,respectively.Finally,the reconstruction of forecasted sub-sequences was used as the final predicted result,to forecast the maximum earthquake magnitude in China's mainland next year.The comparison of SVM with ANN method was made.The forecasted results show that our method possesses higher prediction precision and excellent forecasting effect,and prove that the method to combine wavelet transform with SVM is feasible to forecast time series of strong earthquakes.
引文
[1]Vapnik V.统计学习理论[M].许建华,张学工译.北京:电子工业出版社,2004.
[2]许建华,张学工,李衍达.支持向量机的新发展[J].控制与决策,2004,19(5):481-485.
[3]宇缨,李清华.统计学习理论和支持向量机[J].沈阳大学学报,2005,17(4):42-47.
[4]王景雷,吴景社,孙景生,等.支持向量机在地下水位预报中的应用研究[J].水利学报,2003,49(5):124-128.
[5]崔万照,朱长纯,保文星,等.混沌时间序列的支持向量机预测[J].物理学报,2004,53(10):3 304-3 309.
[6]孙德山,吴今培,侯振挺,等.基于SVR的混沌时间序列预测[J].计算机工程应用,2004,54(2):54-56.
[7]王炜,刘悦,李国正,等.中国大陆强震时间序列预测的支持向量机方法[J].地震,2005,25(4):26-32.
[8]裴韬,周成虎,汪闽,等.用二进小波分析方法对华北地区强震活动期的研究[J].地震研究,2004,27(11):37-42.
[9]飞思产品研发中心.小波分析理论与MATLAB 7实现[M].北京:电子工业出版社,2005.
[10]宋超,黄民翔,叶剑斌.小波分析方法在电力系统短期负荷预测中的应用[J].电力系统及其自动化学报,2002,11(3):8-12.
[11]徐军华,刘天琪.基于小波分解和人工神经网络的短期负荷预测[J].电网技术,2004,28(8):30-33.
[12]王炜,谢端,宋先月,等.使用人工神经网络进行我国大陆强震时间序列预测[J].西北地震学报,2002,24(4):315-319.
[13]国家地震局震害防御司.中国历史强震目录[M].北京:地震出版社,1995.
[14]国家地震局震害防御司.中国近代地震目录[M].北京:中国科技出版社,1999.
[2]许建华,张学工,李衍达.支持向量机的新发展[J].控制与决策,2004,19(5):481-485.
[3]宇缨,李清华.统计学习理论和支持向量机[J].沈阳大学学报,2005,17(4):42-47.
[4]王景雷,吴景社,孙景生,等.支持向量机在地下水位预报中的应用研究[J].水利学报,2003,49(5):124-128.
[5]崔万照,朱长纯,保文星,等.混沌时间序列的支持向量机预测[J].物理学报,2004,53(10):3 304-3 309.
[6]孙德山,吴今培,侯振挺,等.基于SVR的混沌时间序列预测[J].计算机工程应用,2004,54(2):54-56.
[7]王炜,刘悦,李国正,等.中国大陆强震时间序列预测的支持向量机方法[J].地震,2005,25(4):26-32.
[8]裴韬,周成虎,汪闽,等.用二进小波分析方法对华北地区强震活动期的研究[J].地震研究,2004,27(11):37-42.
[9]飞思产品研发中心.小波分析理论与MATLAB 7实现[M].北京:电子工业出版社,2005.
[10]宋超,黄民翔,叶剑斌.小波分析方法在电力系统短期负荷预测中的应用[J].电力系统及其自动化学报,2002,11(3):8-12.
[11]徐军华,刘天琪.基于小波分解和人工神经网络的短期负荷预测[J].电网技术,2004,28(8):30-33.
[12]王炜,谢端,宋先月,等.使用人工神经网络进行我国大陆强震时间序列预测[J].西北地震学报,2002,24(4):315-319.
[13]国家地震局震害防御司.中国历史强震目录[M].北京:地震出版社,1995.
[14]国家地震局震害防御司.中国近代地震目录[M].北京:中国科技出版社,1999.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心