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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2015

01.04.2015

Landslide Deformation Prediction Based on Recurrent Neural Network

verfasst von: Huangqiong Chen, Zhigang Zeng, Huiming Tang

Erschienen in: Neural Processing Letters | Ausgabe 2/2015

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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. Genetic algorithm is used to optimize the initial weights and biases of the network. 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.
Vorheriger Artikel Reconfigurable Neuromorphic Computing System with Memristor-Based Synapse Design
Nächster Artikel Noise-Robust Detection of Symmetric Axes by Self-Correcting Artificial Neural Network

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Metadaten
Titel
Landslide Deformation Prediction Based on Recurrent Neural Network
verfasst von
Huangqiong Chen
Zhigang Zeng
Huiming Tang
Publikationsdatum
01.04.2015
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2015
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-013-9318-5

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