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Generalized Regression Neural Networks with K-Fold Cross-Validation for Displacement of Landslide Forecasting

  • Ping Jiang
  • Zhigang ZengEmail author
  • Jiejie Chen
  • Tingwen Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8866)

Abstract

This paper proposes a generalized regression neural networks (GRNNS) with \(K\)-fold cross-validation (GRNNSK) for predicting the displacement of landslide. Furthermore, correlation analysis is a fundamental analysis to find the potential input variables for a forecast model. Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in the paper. Test on the case study of Liangshuijing (LSJ) landslide in the Three Gorges reservoir in China demonstrate the effectiveness of the proposed approach.

Keywords

Generalized regression neural networks Pearson cross-correlation coefficients Mutual information Landslide 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ping Jiang
    • 1
    • 2
    • 3
  • Zhigang Zeng
    • 1
    • 3
    Email author
  • Jiejie Chen
    • 1
    • 3
  • Tingwen Huang
    • 4
  1. 1.School of AutomationHuazhong University of Science and TechnologyWuhanChina
  2. 2.School of Computer Science and TechnologyHubei PolyTechnic UniversityHuangshiChina
  3. 3.Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhanChina
  4. 4.Texas A&M University at QatarDohaQatar

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