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[1]方筠,庞旭卿.考虑数据去噪分解的滑坡位移组合预测研究*[J].中国安全生产科学技术,2022,18(3):168-174.[doi:10.11731/j.issn.1673-193x.2022.03.026]
　FANG Yun,PANG Xuqing.Study on combination prediction of landslide displacement considering data denoising decomposition[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(3):168-174.[doi:10.11731/j.issn.1673-193x.2022.03.026]
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考虑数据去噪分解的滑坡位移组合预测研究*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年3期
页码:
168-174
栏目:
职业安全卫生管理与技术
出版日期:
2022-03-31

文章信息/Info

Title:
Study on combination prediction of landslide displacement considering data denoising decomposition
文章编号:
1673-193X(2022)-03-0168-07
作者:
方筠; 庞旭卿
(1.陕西铁路工程职业技术学院，陕西 渭南 714000；
2.西安理工大学 土建学院，陕西 西安 710048)
Author(s):
FANG Yun;  PANG Xuqing
(1.Shaanxi Railway Institute,Weinan Shaanxi 714000,China;
2.School of Civil Engineering and Architecture,Xi’an University of Technology,Xi’an Shaanxi 710048,China)
关键词:
滑坡; 去噪分解; 组合预测; 极限学习机; 递进优化
Keywords:
landslide;  denoising decomposition;  combination prediction;  extreme learning machine;  progressive optimization
分类号:
X935
DOI:
10.11731/j.issn.1673-193x.2022.03.026
文献标志码:
A
摘要:
为准确掌握滑坡位移变化规律，基于滑坡变形监测结果统计，对位移数据进行去噪分解处理，将滑坡位移数据分解为趋势项和误差项，并分别利用优化多核极限学习机和Arima模型构建预测模型，以实现滑坡位移的组合预测。结果表明：Morlet复小波较传统去噪模型分解效果更优，且通过优化处理，能更好地提高其分解能力；通过对多核极限学习机的递进优化处理，能有效提高趋势项的预测精度，且经Arima模型的误差修正预测，能进一步提高整体预测精度，结果的平均相对误差均小于2%，验证该组合预测思路在滑坡位移预测中的适用性；通过外推预测，得到滑坡位移仍将进一步增加，并趋于不利方向发展，因此需加强灾害防治，避免成灾损失。研究结果可为滑坡灾害防治提供理论指导。
Abstract:
In order to accurately grasp the variation law of landslide displacement,based on the statistics of landslide deformation monitoring results,the denoising decomposition processing was conducted on the displacement data to decompose the landslide displacement data into the trend term and error term,and the prediction models were constructed by using the optimized multi-core extreme learning machine and ARIMA model respectively,so as to realize the combination prediction of landslide displacement.The results showed that the Morlet complex wavelet had better decomposition effect than the traditional denoising model,and through the optimization processing,its decomposition ability was better improved,which was suitable for the denoising processing of landslide displacement data.Through the progressive optimization processing of multi-core extreme learning machine,the prediction accuracy of trend term could be effectively improved,and through the error correction prediction of ARIMA model,the overall prediction accuracy could be further improved.The average relative error of the results were all less than 2%,which verified the applicability of the combination prediction idea in landslide displacement prediction.Through the extrapolation prediction,it was concluded that the landslide displacement would still further increase and tend to the adverse direction of development,so it was necessary to strengthen the disaster prevention and control to avoid the disaster losses.The results can provide theoretical guidance for the prevention and control of landslide disasters.

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备注/Memo

备注/Memo:
收稿日期： 2021-04-12
* 基金项目： 国家自然科学基金项目（40772183)；陕西省渭南市科研发展计划项目(ZDYF-JCYJ-221)；陕西铁路工程职业技术学院科研基金项目（KY2019-47）
作者简介： 方筠，硕士，副教授，主要研究方向为铁路轨道、路基方面施工与检测维护。
更新日期/Last Update: 2022-04-18