欢迎访问《地震》，
地震

地震 ›› 2007, Vol. 27 ›› Issue (1): 9-16.

• • 上一篇    下一篇

地下水观测数据拟合与预测的支持向量机方法

王炜1, 赵利飞1, 吴耿锋2, 马钦忠1, 林命週1   

  1. 1.上海市地震局, 上海 200062;
    2.上海大学计算机工程与科学学院, 上海 200072
  • 收稿日期:2006-08-11 修回日期:2006-10-24 出版日期:2007-01-31 发布日期:2021-10-29
  • 作者简介:王炜(1947-), 男, 江苏南京人, 研究员, 主要从事地震学、 地震预报等研究。

The support vector machine method for analogizing and forecasting groundwater data

WANG Wei1, ZHAO Li-fei1, WU Geng-feng2, MA Qin-zhong1, LIN Ming-zhou1   

  1. 1. Earthquake Administration of Shanghai Municipality, Shanghai 200062;
    2. School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China
  • Received:2006-08-11 Revised:2006-10-24 Online:2007-01-31 Published:2021-10-29

PDF (PC)

摘要： 影响地下水位变化因素有很多, 在正常情况下, 地下水位的变化实际上反应了气压、 固体潮和降雨这些因素的变化, 但是这些影响因子与地下水位之间有着较强的非线性关系。 该文使用支持向量机方法建立起崇明中学观测站地下水位与气压、 固体潮和降雨这些因素之间的非线性关系模型, 并用于地下水观测数据拟合与预测, 得到了较理想的结果, 明显优于逐步回归方法。 研究结果表明, 支持向量机方法在地震前兆数据处理中有着广泛的应用前景。 文中还对支持向量机方法在实际应用中的有关问题进行了讨论。

关键词: 支持向量机, 逐步回归, 固体潮汐, 非线性关系

Abstract: There are many factors affect the variation of groundwater level. In general the variation of groundwater level is influenced by the factors of air pressure, solid tide, rain and et al. But there is strong nonlinear relation between these factors and groundwater level. The nonlinear model of relation between the groundwater level of Chongming Middle School and the factors of air pressure, solid tide and rain is established by support vector machine method. The analogizing and forecasting data of groundwater gained by support vector machine is very good. It is far better than that by stepwise regression method. The results indicate that the support vector machine possesses extensive application prospects in processing earthquake precursory data. Some other problems relating to the practical application of the support vector machine method are also discussed in this paper.

Key words: Support vector machine, Stepwise regression, Solid tide, Nonlinear relation.

中图分类号: