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Study on Landslide Deformation Prediction Based on Recurrent Neural Network under the Function of Rainfall

机译:降雨作用下基于递归神经网络的滑坡变形预测研究

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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.
机译:滑坡变形预测具有重要的实用价值,可以为预防灾害,保障人民生命财产安全提供指导。提出了一种基于递归神经网络的滑坡预测方法。结果表明,RNN模型的预测精度远高于白水河河滑坡的前馈神经网络模型。因此,RNN模型是进一步提高滑坡位移预测精度的一种有效可行的方法。

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