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Estimation of the Future Earthquake Situation by Using Neural Networks Ensemble

  • Tian-Yu Liu
  • Guo-Zheng Li
  • Yue Liu
  • Geng-Feng Wu
  • Wei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

Earthquakes will do great harms to the people, to estimate the future earthquake situation in Chinese mainland is still an open issue. There have been previous attempts to solve this problem by using artificial neural networks. In this paper, a novel algorithm named MIFEB is proposed to improve the estimation accuracy by combing bagging of neural networks with mutual information based feature selection for its individuals. MIFEB is compared with the general case of bagging on UCI data sets, then, MIFEB is used to forecast the seismicity of strong earthquakes in Chinese mainland, computation results show that MIFEB obtains higher accuracy than other several methods like bagging of neural networks and single neural networks do.

Keywords

Neural Network Feature Selection Mutual Information Strong Earthquake Feature Selection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tian-Yu Liu
    • 1
    • 2
  • Guo-Zheng Li
    • 1
    • 2
  • Yue Liu
    • 1
  • Geng-Feng Wu
    • 1
  • Wei Wang
    • 3
  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  3. 3.Earthquake Administration of Shanghai MunicipalityShanghaiChina

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