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

abstract

• Springer International Publishing Switzerland 2014. 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.

authors

• Huang, Tingwen

published proceedings

• Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

author list (cited authors)

• Jiang, P., Zeng, Z., Chen, J., & Huang, T.

citation count

• 14

complete list of authors

• Jiang, Ping||Zeng, Zhigang||Chen, Jiejie||Huang, Tingwen

publication date

• January 2014

publisher

• Springer Nature  Publisher

published in

• n1611-3349ISSN  Journal

keywords

• 46 Information And Computing Sciences
• 4611 Machine Learning

Digital Object Identifier (DOI)

• 10.1007/978-3-319-12436-0_59

International Standard Book Number (ISBN) 13

• 9783319124353

start page

• 533

end page

• 541

volume

• 8866

URL

• http://dx.doi.org/10.1007/978-3-319-12436-0_59