基于鲸鱼优化混合神经网络的滑坡位移预测
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P642.22

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江西省防震减灾与工程地质灾害探测工程研究中心开放基金项目(SDGD202005);江西省自然科学基金项目(20212BAB203004);江西省教育厅科学技术研究项目(GJJ200727)


Research on Landslide Displacement Prediction Based on Whale Optimization Hybrid Neural Network
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    摘要:

    针对静态机器学习模型难以有效反映滑坡的动态演化特性,且存在时序过长时历史数据遗忘导致位移预测结果不稳定的问题。本文提出了一种基于鲸鱼优化卷积神经网络(convolutional neural networks,CNN)和双向门控循环神经网络(bidirectional gated recurrent neural network,BiGRU)的滑坡位移动态预测方法。首先对滑坡影响因子进行特征筛选,构建数据集,建立CNN-BiGRU网络模型,使用鲸鱼优化算法(whale optimization algorithm,WOA)对模型进行超参数寻优,使用CNN网络模型从滑坡数据中提取潜在的特征向量,将特征向量以时间序列的形式输入到BiGRU模型中,利用其处理时间序列数据的优势,完成滑坡位移预测。结果表明,本文提出的模型得到的滑坡位移预测精度较高,与未优化的CNN-BiGRU相比均方根误差(RMSE)下降了0.0305mm。

    Abstract:

    For the static machine learning model is difficult to reflect the dynamic evolution characteristics of landslide effectively, and there is the problem that the displacement prediction results are unstable due to the forgetfulness of historical data when the time series is too long. In this paper, a dynamic landslide displacement prediction method based on whale-optimized convolutional neural networks (CNN) and bidirectional gated recurrent neural network (BiGRU) is proposed. Firstly, the landslide impact factors are filtered for features and the data set is constructed, the CNN-BiGRU network model is established and the hyperparameter search is performed using the whale optimization algorithm (WOA) , the potential feature vectors are extracted from the landslide data using the CNN network model, and the feature vectors are input to the BiGRU model in the form of time series. BiGRU model, and use its advantage of processing time series data to complete landslide displacement prediction. The results show that the landslide displacement prediction accuracy obtained by the model proposed in this paper is high, and the root mean square error (RMSE) decreases by 0.0305 mm compared with the unoptimized CNN-BiGRU.

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罗超雷,徐哈宁,肖慧,等. 基于鲸鱼优化混合神经网络的滑坡位移预测[J]. 科学技术与工程, 2024, 24(16): 6610-6616.
Luo Chaolei, Xu Haning, Xiao Hui, et al. Research on Landslide Displacement Prediction Based on Whale Optimization Hybrid Neural Network[J]. Science Technology and Engineering,2024,24(16):6610-6616.

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  • 收稿日期:2023-04-22
  • 最后修改日期:2024-03-07
  • 录用日期:2023-10-10
  • 在线发布日期: 2024-06-13
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