Abstract
In this paper, a modified method for landslide prediction is presented. This method is based on the back-propagation neural network (BPNN), and we use the combination of genetic algorithm and simulated annealing algorithm to optimize the weights and biases of the network. The improved BPNN modeling can work out the complex nonlinear relation by learning model and using the present data. This paper demonstrates that the revised BPNN modeling can be used to predict and calculate landslide deformation, quicken the learning speed of network, and improve the predicting precision. Applying this thinking and method into research of some landslide in the Three Gorges reservoir, the validity and practical value of this model can be demonstrated. And it also shows that the dynamic prediction of landslide deformation is very crucial.
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Acknowledgments
The work is supported by the Natural Science Foundation of China under Grant 60974021, the 973 Program of China under Grant 2011CB710606, the Fund for Distinguished Young Scholars of Hubei Province under Grant 2010CDA081, and the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant 20100142110021.
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Chen, H., Zeng, Z. Deformation Prediction of Landslide Based on Improved Back-propagation Neural Network. Cogn Comput 5, 56–62 (2013). https://doi.org/10.1007/s12559-012-9148-1
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DOI: https://doi.org/10.1007/s12559-012-9148-1