地震前兆综合预测支持向量机模型研究
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摘要
该文介绍了支持向量机算法的原理与回归方法。采用支持向量机中的非线性回归算法与理论公式产生的多维样本,对其进行了数值仿真实验。利用该方法和地震前兆异常建立了最佳地震综合预测模型,对获得的最佳模型进行了内符检验,得出最佳模型的预测结果与实际震例的地震震级基本一致。综合分析认为,支持向量机无论在学习或者预测精度方面不但具有很大的优越性和具有较强的外推泛化能力,而且基于支持向量机回归算法建立的地震前兆综合预测模型是可行的,其获得的知识可较为准确地实现对主震震级的综合预测。
The principle of support vector machine (SVM) and its regression algorithm is introduced in this paper. The multidimensional samples from theoretical formula are tested by using of SVM. The best model for synthetic prediction of seismic precursors is established according to the SVM algorithm and the seismic precursory anomalies, and it is tested by using of the testing samples. It indicates that the forecast result of the best mode and earthquake magnitude of real seismic examples are basically consistent. It shows that the support vector machine algorithm has an obvious superiority whatever on machine learning or prediction accuracy, and the model for synthetic prediction of seismic precursors based on the SVM theory is feasible, and it can forecast the magnitude of main earthquakes more accurately.
The principle of support vector machine (SVM) and its regression algorithm is introduced in this paper. The multidimensional samples from theoretical formula are tested by using of SVM. The best model for synthetic prediction of seismic precursors is established according to the SVM algorithm and the seismic precursory anomalies, and it is tested by using of the testing samples. It indicates that the forecast result of the best mode and earthquake magnitude of real seismic examples are basically consistent. It shows that the support vector machine algorithm has an obvious superiority whatever on machine learning or prediction accuracy, and the model for synthetic prediction of seismic precursors based on the SVM theory is feasible, and it can forecast the magnitude of main earthquakes more accurately.
引文
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[3]平建军,张永仙,张清荣,等.华北地区地震短期综合预测方法研究[J].中国地震,2003,19(4):416-424.
[4]张晓东,傅征祥,张永仙,等.1999—2002年地震预报研究进展[J].地震学报,2003,25(5):479-491.
[5]邓乃扬,田英杰.数据挖掘中的新方法支持向量机[M].北京:科学出版社,2005.
[6]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-43.
[7]陈永义,俞小鼎,高学浩.处理非线性分类和回归问题的一种新方法(Ⅰ)支持向量机方法简介[J].应用气象学报,2004,15(6):345-355.
[8]陶卿,曹进德,刊德敏.基于支持向量机分类的回归方法[J].软件学报,2002,13(5):24-29.
[9]阎威武,邵惠鹤.支持向量机和最小二乘支持向量机的比较及应用研究[J].控制与决策,2003,18(3):358-361.
[2]王炜,蒋春曦,张军,等.BP神经网络在地震综合预报中的应用[J].地震,1999,15(2):118-126.
[3]平建军,张永仙,张清荣,等.华北地区地震短期综合预测方法研究[J].中国地震,2003,19(4):416-424.
[4]张晓东,傅征祥,张永仙,等.1999—2002年地震预报研究进展[J].地震学报,2003,25(5):479-491.
[5]邓乃扬,田英杰.数据挖掘中的新方法支持向量机[M].北京:科学出版社,2005.
[6]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-43.
[7]陈永义,俞小鼎,高学浩.处理非线性分类和回归问题的一种新方法(Ⅰ)支持向量机方法简介[J].应用气象学报,2004,15(6):345-355.
[8]陶卿,曹进德,刊德敏.基于支持向量机分类的回归方法[J].软件学报,2002,13(5):24-29.
[9]阎威武,邵惠鹤.支持向量机和最小二乘支持向量机的比较及应用研究[J].控制与决策,2003,18(3):358-361.
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