一种滑动检测算法下的滑坡位移时序分解方法

冯谕; 曾怀恩; 涂鹏飞

doi:10.11988/ckyyb.20221323

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长江科学院院报 ›› 2024, Vol. 41 ›› Issue (3) : 126-133. DOI: 10.11988/ckyyb.20221323

工程安全与灾害防治

一种滑动检测算法下的滑坡位移时序分解方法

冯谕¹, 曾怀恩^1,2,3, 涂鹏飞^1,3

作者信息 +

A Time Series Decomposition Method for Landslide Displacement Based on Sliding Detection Algorithm

FENG Yu¹, ZENG Huai-en^1,2,3, TU Peng-fei^1,3

Author information +

文章历史 +

摘要

针对“阶跃式”滑坡位移时序分解模型力学解释性不强的缺陷,根据西原蠕变本构模型与自适应改进遗传算法模型,提出滑动R_nl阶跃点检测方法与改进加权移动平均修正阶跃项位移方法,并将该方法应用于白水河滑坡位移时序分解。将滑动R_nl阶跃点检测结果与MK检验结果、滑动t检验结果以及Bayes检测结果作对比。结果表明,滑动R_nl阶跃点检测结果更加准确与适用;同时将新型滑坡位移时序分解结果与二次移动平均时序分解结果、三次指数平滑时序分解结果以及VMD时序分解结果作对比。结果表明,新型滑坡位移时序分解方法解决了滑坡趋势项位移无规律、无力学解释性的问题,且在时序分解加法模式中单独引入滑坡位移预测中最重要的阶跃项位移,分析预测更具有针对性。因此,新型时序分解模型有一定的工程价值与时序预测借鉴价值。

Abstract

To address the issue of weak mechanical interpretation in the time-series decomposition model of step-type landslide displacement, we propose a decomposition method incorporating sliding R_nl step-point detection and improved weighted moving average method to modify step-term displacement. Both the Nishihara creep constitutive model and a self-adaptive improved genetic algorithm model were utilized. The proposed method was applied to decompose the displacement time series of Baishuihe landslide. The results of the proposed method were compared with those of the MK Test, sliding t test, and the Bayes test, demonstrating that the sliding R_nl step-point detection yields more accurate and applicable results. Furthermore, the displacement time series decomposition results were also compared with those obtained from quadratic moving average time series decomposition, cubic exponential smoothing time series decomposition, and VMD time series decomposition. The findings reveal that our proposed decomposition method effectively addresses irregular displacement and enhances the mechanical interpretation of the landslide trend term. Additionally, the introduction of the most critical step-term displacement in landslide displacement prediction enhances the specificity of analysis and prediction. In conclusion, our decomposition model holds significant engineering value and serves as a valuable reference for time series prediction.

导出引用

冯谕, 曾怀恩, 涂鹏飞. 一种滑动检测算法下的滑坡位移时序分解方法[J]. 长江科学院院报. 2024, 41(3): 126-133 https://doi.org/10.11988/ckyyb.20221323

FENG Yu, ZENG Huai-en, TU Peng-fei. A Time Series Decomposition Method for Landslide Displacement Based on Sliding Detection Algorithm[J]. Journal of Changjiang River Scientific Research Institute. 2024, 41(3): 126-133 https://doi.org/10.11988/ckyyb.20221323

中图分类号： P642

参考文献

[1] 陈曦.基于新的滑坡时序分解和时滞LSTM的滑坡位移预测研究[C]//第十三届中国卫星导航年会论文集:S01卫星导航行业应用.北京:中国卫星导航学术年会组委会,2022:186-191.(CHEN Xi. Research on Landslide Displacement Prediction Based on New Landslide Time Series Decomposition and Time-lag LSTM[C]//Proceedings of the Thirteenth Annual Conference of China’s Satellite Navigation: Application of S01 Satellite Navigation. Beijing: Organizing Committee of Academic Annual Conference of China’s Satellite Navigation, 2022:186-191.(in Chinese))
[2] 曹博, 汪帅, 宋丹青, 等. 基于蚁群算法优化极限学习机模型的滑坡位移预测[J]. 水资源与水工程学报, 2022, 33(2): 172-178.(CAO Bo, WANG Shuai, SONG Dan-qing, et al. Landslide Displacement Prediction Based on Extreme Learning Machine Optimized by Ant Colony Algorithm[J]. Journal of Water Resources and Water Engineering, 2022, 33(2): 172-178.(in Chinese))
[3] 周超, 殷坤龙, 曹颖, 等. 基于诱发因素响应与支持向量机的阶跃式滑坡位移预测[J]. 岩石力学与工程学报, 2015, 34(增刊2): 4132-4139.(ZHOU Chao, YIN Kun-long, CAO Ying, et al. Step Landslide Displacement Prediction Based on Induced Factor Response and Support Vector Machine[J]. Chinese Journal of Rock Mechanics and Engineering, 2015, 34(Supp.2): 4132-4139.(in Chinese))
[4] 黄发明, 殷坤龙, 杨背背, 等. 基于时间序列分解和多变量混沌模型的滑坡阶跃式位移预测[J]. 地球科学, 2018, 43(3): 887-898.(HUANG Fa-ming, YIN Kun-long, YANG Bei-bei, et al. Step-like Displacement Prediction of Landslide Based on Time Series Decomposition and Multivariate Chaotic Model[J]. Earth Science, 2018, 43(3): 887-898.(in Chinese))
[5] 赵力学, 黄解军, 程学军, 等. 基于VMD-BP模型的河流流量预测方法[J]. 长江科学院院报, 2020, 37(7): 47-52.(ZHAO Li-xue, HUANG Jie-jun, CHENG Xue-jun, et al. A Method of River Flow Prediction Based on VMD-BP Model[J]. Journal of Yangtze River Scientific Research Institute, 2020, 37(7): 47-52.(in Chinese))
[6] 胡成雨, 张焕明, 朱家明. Cox-Stuart及Wilcoxon符号秩检验在产品质检中的应用[J]. 齐齐哈尔大学学报(自然科学版), 2020, 36(2): 80-85.(HU Cheng-yu, ZHANG Huan-ming, ZHU Jia-ming. Application of Cox-Stuart and Wilcoxon Symbol Rank Test in Product Quality Inspection[J]. Journal of Qiqihar University (Natural Science Edition), 2020, 36(2): 80-85.(in Chinese))
[7] 邵骏, 杜涛, 郭卫, 等. 金沙江上游河段水温变化规律及其影响因素探讨[J]. 长江科学院院报, 2022, 39(8): 17-22, 28.(SHAO Jun, DU Tao, GUO Wei, et al. Water Temperature Variation and Its Influencing Factors in the Upper Jinsha River[J]. Journal of Yangtze River Scientific Research Institute, 2022, 39(8): 17-22, 28.(in Chinese))
[8] 欧阳卫,王筱,周维博.1956—2016年沣河径流量变化特征分析[J].水资源与水工程学报,2021,32(3):118-123.(OUYANG Wei,WANG Xiao,ZHOU Wei-bo.Variation Characteristics of Fenghe River Runoff from 1956-2016[J].Journal of Water Resources and Water Engineering,2021,32(3):118-123.(in Chinese))
[9] 封国林, 龚志强, 董文杰, 等. 基于启发式分割算法的气候突变检测研究[J]. 物理学报, 2005, 54(11): 5494-5499.(FENG Guo-lin, GONG Zhi-qiang, DONG Wen-jie, et al. Abrupt Climate Change Detection Based on Heuristic Segmentation Algorithm[J]. Acta Physica Sinica, 2005, 54(11): 5494-5499.(in Chinese))
[10] 傅丽昕. 近57年来和丰县气温和降水量的趋势性及突变特征[J]. 南水北调与水利科技, 2014, 12(4): 38-41, 110.(FU Li-xin. Tendency and Mutation Analysis of Annual Temperature and Precipitation of Hefeng County in Recent 57 Years[J]. South-to-North Water Transfers and Water Science & Technology, 2014, 12(4): 38-41, 110.(in Chinese))
[11] 张建军, 周后福, 翟菁. 合肥气温和降水的突变特征分析[J]. 安徽农业科学, 2007, 35(9): 2724-2726.(ZHANG Jian-jun, ZHOU Hou-fu, ZHAI Jing. Analysis of the Character of Temperature and Precipitation Variation in Hefei City[J]. Journal of Anhui Agricultural Sciences, 2007, 35(9): 2724-2726.(in Chinese))
[12] 隋学深, 杨忠海. 沪深两市股指时间序列突变点贝叶斯检测模型研究[J]. 商业研究, 2007(2): 41-43.(SUI Xue-shen, YANG Zhong-hai. The Research on Bayesian Measure Model of Change Points in Shanghai and Shenzhen Stock Index Time Series[J]. Commercial Research, 2007(2): 41-43.(in Chinese))
[13] 杨背背, 殷坤龙, 杜娟. 基于时间序列与长短时记忆网络的滑坡位移动态预测模型[J]. 岩石力学与工程学报, 2018, 37(10): 2334-2343.(YANG Bei-bei, YIN Kun-long, DU Juan. A Model for Predicting Landslide Displacement Based on Time Series and Long and Short Term Memory Neural Network[J]. Chinese Journal of Rock Mechanics and Engineering, 2018, 37(10): 2334-2343.(in Chinese))
[14] XU T, XU Q, TANG C A, et al. The Evolution of Rock Failure with Discontinuities Due to Shear Creep[J]. Acta Geotechnica, 2013, 8(6): 567-581.
[15] 董秀军,许强,唐川,等.滑坡位移-时间曲线特征的物理模拟试验研究[J].工程地质学报,2015,23(3):401-407.(DONG Xiu-jun,XU Qiang,TANG Chuan,et al. Characteristics of Landslide Displacement-time Curve by Physical Simulation Experiment[J]. Journal of Engineering Geology,2015,23(3):401-407.(in Chinese))
[16] 陈浩,杨春和,任伟中.蠕动滑坡变形机制的理论分析与模型试验研究[J].岩石力学与工程学报,2008,27(增刊2):3705-3711.(CHEN Hao,YANG Chun-he,REN Wei-zhong.Theoretical Analysis and Model Test Study on Deformation Mechanism of Creeping Landslide[J]. Chinese Journal of Rock Mechanics and Engineering,2008,27(Supp.2):3705-3711.(in Chinese))
[17] 何云明,吴德伦.岩质边坡蠕变模型及其蠕变机理研究[C]//中国岩石力学与工程学会.第八次全国岩石力学与工程学术大会论文集:西部大开发中的岩石力学与工程问题.北京:科学出版社,2004:723-728.(HE Yun-ming, WU De-lun. Study on the Creep Model and Creep Mechanism of Rock Slope[C]//Chinese Society for Rock Mechanics and Engineering. Proceedings of the Eighth National Conference on Rock Mechanics and Engineering: Rock Mechanics and Engineering Problems in the Great Development of China’s Western Region. Beijing: Science Press, 2004:723-728.(in Chinese))
[18] 齐亚静,姜清辉,王志俭,等.改进西原模型的三维蠕变本构方程及其参数辨识[J].岩石力学与工程学报,2012,31(2):347-355.(QI Ya-jing,JIANG Qing-hui,WANG Zhi-jian,et al. 3D Creep Constitutive Equation of Modified Nishihara Model and Its Parameters Identification[J]. Chinese Journal of Rock Mechanics and Engineering,2012,31(2):347-355.(in Chinese))
[19] 张世强. 曲线回归的拟合优度指标的探讨[J]. 中国卫生统计,2002,19(1):9-11.(ZHANG Shi-qiang. Approach on the Fitting Optimization Index of Curve Regression[J]. Chinese Journal of Health Statistics,2002,19(1): 9-11.(in Chinese))
[20] 孙桂凯, 石锐, 刘思怡, 等. 基于长期与中长期嵌套的水库优化调度[J]. 长江科学院院报, 2022, 39(8): 23-28.(SUN Gui-kai, SHI Rui, LIU Si-yi, et al. Optimal Scheduling of Reservoir Based on Long-term and Medium Long-term Nested Model[J]. Journal of Yangtze River Scientific Research Institute, 2022, 39(8): 23-28.(in Chinese))
[21] 刘颖, 郑荣伟, 齐艳芳, 等. 基于SVMs-GA模型的微灌系统管网优化设计[J]. 长江科学院院报, 2022, 39(5): 71-75, 88.(LIU Ying, ZHENG Rong-wei, QI Yan-fang, et al. Optimizing Design of Pipe Network in Micro-irrigation System Using SVMs-GA[J]. Journal of Yangtze River Scientific Research Institute, 2022, 39(5): 71-75, 88.(in Chinese))
[22] 张艳, 宦飞. 一种应用遗传算法的彩色图像分割方法[J]. 计算机应用与软件, 2011, 28(3): 237-239, 256.(ZHANG Yan, HUAN Fei. A Colour Image Segmentation Method Using Genetic Algorithm[J]. Computer Applications and Software, 2011, 28(3): 237-239, 256.(in Chinese))
[23] 陈雯祎, 李琪. 一种基于轮盘赌选择的改进遗传算法[J]. 福建电脑, 2016, 32(5): 50-51.(CHEN Wen-yi, LI Qi. An Improved Genetic Algorithm Based on Roulette Wheel Selection[J]. Fujian Computer, 2016, 32(5): 50-51.(in Chinese))
[24] 辛馡,朱鳌鑫.遗传算法的适应度函数研究[J].系统工程与电子技术,1998(11):58-62.(XIN Fei,ZHU Ao-xin.Research on Fitness Function in Genetic Algorithms[J].Systems Engineering and Electronics,1998(11):58-62.(in Chinese))
[25] 李德毅, 杜鹢. 不确定性人工智能[M]. 2版. 北京: 国防工业出版社, 2014.(LI De-yi, DU Yi. Artificial Intelligence with Uncertainty[M]. Edition 2. Beijing: National Defense Industry Press, 2014.(in Chinese))
[26] 唐秋生, 张笑语. 基于云遗传算法的信号交叉口相序相位优化[J]. 交通科技与经济, 2021, 23(1): 19-25.(TANG Qiu-sheng, ZHANG Xiao-yu. Optimization of Signal Sequence Phase Sequence Based on Cloud Genetic Algorithm[J]. Technology & Economy in Areas of Communications, 2021, 23(1): 19-25.(in Chinese))
[27] 李麟玮. 三峡库区库岸堆积层滑坡位移预测与稳定性评价方法研究[D]. 武汉: 中国地质大学, 2021.(LI Lin-wei. Displacement Prediction and Stability Evaluation Methods of Reservoir Colluvial Landslides in Three Gorges Reservoir Area[D].Wuhan: China University of Geosciences, 2021.(in Chinese))