...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Landslides >Modeling and predicting reservoir landslide displacement with deep belief network and EWMA control charts: a case study in Three Gorges Reservoir
【24h】

Modeling and predicting reservoir landslide displacement with deep belief network and EWMA control charts: a case study in Three Gorges Reservoir

机译：利用深度信仰网络和EWMA控制图建模与预测水库滑坡位移 - 以三峡库区为例

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The accurate modeling and predicting of landslide deformation is crucial to the prevention of landslide hazard. This paper presents a pioneering study of modeling and predicting the reservoir landslide displacement with deep learning algorithm. A data-driven framework using deep belief network and control chart has been introduced to explore the temporal patterns of displacement and potential of identifying seasonal faster displacement. First, the continuous wavelet analysis has been applied to decompose the time-series precipitation, reservoir water level, and displacement into seasonal and residual components. Second, the deep belief network has been constructed to predict the future displacement. Third, it utilizes the exponentially weighted moving average (EWMA) control chart to derive the boundaries as alarm conditions of seasonal faster displacement. A group of tests are conducted to compare the performance of the deep belief network with other state-of-the-art machine learning algorithms. Computational results demonstrated the effectiveness of the deep belief network in extracting highly non-linear data features. In addition, the advantage of utilizing control charts has been further validated by the accuracy of examining the seasonal faster displacement based on the case study in Baishuihe landslide in Three Gorges Reservoir, China.
机译：准确的建模和预测滑坡变形对于预防滑坡危害至关重要。本文介绍了建模和预测水库滑坡位移与深层学习算法的开拓研究。已经引入了一种数据驱动的框架，使用深度信仰网络和控制图来探讨位移的时间模式和识别季节性更快的位移的潜力。首先，已应用连续小波分析以将时间序列降水，水平和位移分解为季节性和残余组分。其次，建造了深度信仰网络以预测未来的位移。第三，它利用指数加权的移动平均（EWMA）控制图来导出季节性速度速度速度的报警条件的边界。进行一组测试以比较深度信仰网络与其他最先进的机器学习算法的性能。计算结果证明了深度信念网络在提取高度非线性数据特征方面的有效性。此外，利用控制图的优点是通过基于三峡库区Baishuihe滑坡的案例研究来检查季节性更快的位移的准确性进行了进一步验证的。

著录项

  • 来源
    《Landslides》 |2020年第3期|共15页
  • 作者

    Li Huajin; Xu Qiang; He Yusen; Fan Xuanmei; Li Songlin;

  • 作者单位

    Chengdu Univ Technol State Key Lab Geohazard Prevent &

    Geoenvironm Pro 1 Erxianqiao East Rd Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol State Key Lab Geohazard Prevent &

    Geoenvironm Pro 1 Erxianqiao East Rd Chengdu 610059 Sichuan Peoples R China;

    Univ Iowa Dept Ind &

    Syst Engn Iowa City IA 52242 USA;

    Chengdu Univ Technol State Key Lab Geohazard Prevent &

    Geoenvironm Pro 1 Erxianqiao East Rd Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol State Key Lab Geohazard Prevent &

    Geoenvironm Pro 1 Erxianqiao East Rd Chengdu 610059 Sichuan Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 崩塌;
  • 关键词

    Landslide deformation; Wavelet analysis; Deep belief network; EWMA control charts;

    机译：滑坡变形;小波分析;深度信仰网络;EWMA控制图;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号