Abstract
Landslide deformation prediction has significant practical value that can provide guidance for preventing the disaster and guarantee the safety of people’s life and property. In this paper, a method based on recurrent neural network (RNN) for landslide prediction is presented. The results show that the prediction accuracy of RNN model is much higher than the feedforward neural network model for Baishuihehe landslide. Therefore, the RNN model is an effective and feasible method to further improve accuracy for landslide displacement prediction.
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Chen, H., Zeng, Z., Tang, H. (2012). Study on Landslide Deformation Prediction Based on Recurrent Neural Network under the Function of Rainfall. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_83
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DOI: https://doi.org/10.1007/978-3-642-34478-7_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34477-0
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