• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Advanced Computational Intelligence >A hybrid predicting model for displacement of multifactor-triggered landslides
【24h】

A hybrid predicting model for displacement of multifactor-triggered landslides

机译：多因素触发滑坡位移的混合预测模型

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents a new hybrid model for land-slide distance prediction. In the model, the cumulative displacement are divided into three parts: the trend term, the period term, and the random noise obtained by the wavelet domain de-nosing method and Hodrick-Prescott (HP) filter. The trend term controlled by the geological conditions is generated using the double exponential smoothing (DES). The period term is predicted by the extreme learning machine (ELM) model, and the dynamic multi-swarm particle swarm optimizer (DMS-PSO) algorithm is applied to obtain optimal parameters of ELM. Case study involving real data collected from the Baishuihe landslide in China is used to verify that the hybrid approach enhances the ability to calculate the period term. Inputs of the proposed model include the period factors extracted from the seasonal triggers and displacement values which enhance excellently the robustness of the prediction model of the period displacement. Extensive experiments are carried out on the Baishuihe landslide dates. Comparing with the predictions obtained by the real original displacement, our model is efficient for predicting the landslide distance of multiple factors induced landslide.
机译：本文提出了一种新的混合模型用于滑坡距离预测。在模型中，累积位移分为三个部分：趋势项，周期项以及通过小波域去噪方法和Hodrick-Prescott（HP）滤波器获得的随机噪声。由地质条件控制的趋势项是使用双指数平滑法（DES）生成的。周期项由极限学习机（ELM）模型预测，并使用动态多群粒子群优化器（DMS-PSO）算法获得ELM的最佳参数。案例研究涉及从中国白水河滑坡收集的真实数据，用于验证混合方法增强了计算周期项的能力。所提出的模型的输入包括从季节触发因素中提取的周期因子和位移值，它们极大地增强了周期位移预测模型的鲁棒性。对白水河滑坡枣进行了广泛的实验。与通过实际原始位移获得的预测结果相比，我们的模型可以有效地预测由多因素引起的滑坡的滑坡距离。

著录项

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