Abstract
Complexity of analysis of landslide hazard is due to uncertainty. In this study, a novel approach multi-gene genetic programming based on separable functional network (MGGPSFN) is presented for predicting landslide displacement. Moreover, Pearson's cross-correlation coefficients and mutual information are adopted to look for the potential input variables for a forecast model in the paper. The performance of new model is verified through one case study in Baishuihe landslide in the Three Gorges Reservoir in China. In addition, we compared it with two methods, back-propagation neural network and radial basis function, and MGGPSFN got the best results in the same measurements.
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Abbreviations
- MGGPSFN:
-
Multi-gene genetic programing based on separable functional network
- MGGP:
-
Multi-gene genetic programing
- SFN:
-
Separable functional network
- PCC:
-
Pearson's cross-correlation coefficients
- MI:
-
Mutual information
- BSH:
-
Baishuihe
- BPNN:
-
Back-propagation neural network
- RBF:
-
Radial basis function
- FNs:
-
Functional networks
- ANNS:
-
Artificial neural network
- GP:
-
Genetic programing
- PGP:
-
Parisian genetic programming
- CEP:
-
Cartesian genetic programming
- GEP:
-
Gene genetic programming
- GPS:
-
Global positioning system
- A:
-
Relation between displacement and reservoir
- B:
-
Relation between displacement and rainfall
- C:
-
Relation between reservoir level and rainfall
- KPSS:
-
Kwiatkowski–Phillips–Schmidt–Shin
- RMSE:
-
Root mean square error
- MAE:
-
Mean absolute error
- R:
-
Correlation coefficient
- RE:
-
Relative error
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Acknowledgments
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 and the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant IRT1245.
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Chen, J., Zeng, Z., Jiang, P. et al. Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction. Neural Comput & Applic 27, 1771–1784 (2016). https://doi.org/10.1007/s00521-015-1976-y
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DOI: https://doi.org/10.1007/s00521-015-1976-y