Study on landslide deformation prediction based on recurrent neural network under the function of rainfall

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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. © 2012 Springer-Verlag.

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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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7666 LNCS, pp. 683–690). https://doi.org/10.1007/978-3-642-34478-7_83

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