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Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

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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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Correspondence to Zhigang Zeng.

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We claim that none of the material in the paper has been published or is under consideration for publication elsewhere. Our results are different from the previous work and true.

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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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