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.
This work was supported by the Natural Science Foundation of China under Grant 61125303, National Basic Research Program of China (973 Program) under Grant 2011CB710606, the Program for Science and Technology in Wuhan of China under Grant 2014010101010004, the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant IRT1245. This publication was made possible by NPRP grant # NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation).
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© 2014 Springer International Publishing Switzerland
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Jiang, P., Zeng, Z., Chen, J., Huang, T. (2014). Generalized Regression Neural Networks with K-Fold Cross-Validation for Displacement of Landslide Forecasting. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_59
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DOI: https://doi.org/10.1007/978-3-319-12436-0_59
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