基于CEEMDAN理论的堆积层滑坡位移区间预测模型及仿真

doi:10.13229/j.cnki.jdxbgxb20220076

摘要/Abstract

摘要：

为更好地控制滑坡灾害风险，最大程度降低灾害损失，提出了基于CEEMDAN理论的堆积层滑坡位移区间预测模型及仿真。利用多类型传感器采集滑坡信息，采用自适应完备集合经验模态分解算法计算样本熵，根据相关系数获取噪声所在区间，通过设定的合理阈值去除信号噪声；构建多传感器数据融合分布式架构，提高滑坡数据的全面性；通过构建预测模型，经过样本训练，输出最终预测结果。仿真实验表明：本文模型预测区间覆盖率较高，且区间宽度低，保证了预测精度。

关键词: 自适应经验模态分解, 样本熵, 粒子群算法, 支持向量机, 堆积层滑坡位移

Abstract:

In order to better control the landslide hazard risk and reduce the disaster loss to the greatest extent， a prediction model and simulation of landslide displacement interval of accumulation layer based on CEEMDAN theory were proposed. The landslide information is collected by multi type sensors， the sample entropy is calculated by the adaptive complete set empirical mode decomposition algorithm， the noise range is obtained according to the correlation coefficient， and the signal noise is removed by the set reasonable threshold； Construct a distributed architecture of multi-sensor data fusion to improve the comprehensiveness of landslide data； The final prediction results are output through the construction of prediction model and sample training. The simulation results show that the proposed model has high prediction interval coverage and low interval width， ensure the prediction accuracy.

Key words: adaptive empirical mode decomposition, sample entropy, particle swarm optimization, support vector machine, accumulation landslide displacement

中图分类号:

TP318.2

吴剑,许斌. 基于CEEMDAN理论的堆积层滑坡位移区间预测模型及仿真[J]. 吉林大学学报(工学版), 2023, 53(2): 562-568.

Jian WU,Bin XU. Displacement interval prediction model and simulation of accumulation landslide based on ceemdan theory[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 562-568.

图/表 7

图1

图2

表1

图3

图4

图5

图6

参考文献 20

1	李丽敏,张明岳,温宗周,等.基于奇异谱分析法和长短时记忆网络组合模型的滑坡位移预测[J]. 信息与控制, 2021, 50(4): 459-469, 482.
	Li Li-min, Zhang Ming-yue, Wen Zong-zhou, et al. Landslide displacement prediction based on singular spectrum analysis and a combined long short-term memory neural network model[J]. Information and Control, 2021, 50(4): 459-469, 482.
2	鄢好, 陈骄锐, 李绍红, 等. 基于时间序列和GRU的滑坡位移预测[J]. 人民长江, 2021, 52(1): 102-107, 133.
	Yan Hao, Chen Jiao-rui, Li Shao-hong, et al. Predicting of landslide displacement based on time series and Gated Recurrent Unit[J]. Yangtze River, 2021,52(1): 102-107, 133.
3	尚敏, 熊德兵, 张惠强, 等. 基于时间序列与混合核函数SA-SVR的滑坡位移预测模型研究[J]. 工程地质学报, 2022, 30(2): 575-588.
	Shang Min, Xiong De-bing, Zhang Hui-qiang, et al. Landslide displacement prediction model based on time series and mixed kernel function SA-SVR[J]. Journal of Engineering Geology, 2022, 30(2): 575-588.
4	徐文兵, 王国河, 王生, 等. 基于PSO-SVM算法的乐昌峡鹅公带滑坡体位移预测模型[J]. 中山大学学报: 自然科学版, 2020, 59(5): 57-65.
	Xu Wen-bing, Wang Guo-he, Wang Sheng, et al. Displacement prediction model of Egongdai landslide in Lechangxia based on PSO-SVM algorithms[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2020, 59(5): 57-65.
5	赵久彬, 刘元雪, 何少其, 等. 三峡库区阶跃变形滑坡水平位移与降雨量数学统计模型[J]. 岩土力学, 2020, 41(): 305-311.
	Zhao Jiu-bin, Liu Yuan-xue, He Shao-qi, et al. Mathematical statistical model of horizontal displacement and rainfall of step deformation landslide in the Three Gorges Reservoir Area[J]. Rock and Soil Mechanics, 2020, 41 (Sup.1): 305-311.
6	叶咸, 高瑜, 李果, 等. 滑坡深部位移监测孔合位移计算与分析[J]. 公路, 2020, 65(10): 5-10.
	Ye Xian, Gao Yu, Li Guo, et al. Calculation and analysis of combined displacement of deep displacement monitoring holes in landslide[J]. Highway, 2020,65 (10): 5-10.
7	韩斐, 牛瑞卿, 李士垚, 等. 基于变分模态分解和深度置信神经网络模型的滑坡位移预测[J]. 长江科学院院报, 2020, 37(8): 61-68.
	Han Fei, Niu Rui-qing, Li Shi-yao, et al. Landslide displacement prediction based on variational mode decomposition and deep confidence neural network model[J]. Journal of Yangtze River Scientific Research Institute, 2020, 37(8): 61-68.
8	亓文斌, 刘会林, 王飞飞, 等. 地下采矿引起山体滑坡发展过程数值模拟研究[J]. 矿冶工程, 2021, 41(4): 29-32, 37.
	Qi Wen-bin, Liu Hui-lin, Wang Fei-fei, et al. Numerical simulation of landslide development process caused by underground mining[J]. Mining and Metallurgical Engineering, 2021,41 (4): 29-32, 37.
9	孙浩然, 岳建平. 一种抑制边界效应的EMD方法及其在滑坡监测中的应用[J]. 测绘通报, 2021(5): 77-80, 90.
	Sun Hao-ran, Yue Jian-ping. An EMD method to suppress boundary effect and its application in landslide monitoring[J]. Bulletin of Surveying and Mapping, 2021(5): 77-80, 90.
10	黄金勇,陈志伟,沈艺璇.库水位下降速率对库岸老滑坡间歇性复活的影响[J]. 水电能源科学, 2020, 38(1): 147-150.
	Huang Jin-yong, Chen Zhi-wei, Shen Yi-xuan. Effect of water level decline rate on intermittent revival of old landslide on reservoir bank[J]. Water Resources and Power, 2020, 38(1): 147-150.
11	Zhang J, Tang H, Wen T, et al. A hybrid landslide displacement prediction method based on CEEMD and DTW-ACO-SVR—Cases studied in the three gorges reservoir area[J]. Sensors, 2020, 20(15): 20154287.
12	Wang J, Nie G, Gao S, et al. Landslide deformation prediction based on a GNSS time series analysis and recurrent neural network model[J]. Remote Sensing, 2021, 13(6): 13061055.
13	Xing Y, Yue J, Chen C, et al. Prediction interval estimation of landslide displacement using adaptive chicken swarm optimization-tuned support vector machines[J]. Applied Intelligence, 2021, 51(11): 8466-8483.
14	Li J, Wang W, Han Z. A variable weight combination model for prediction on landslide displacement using AR model, LSTM model, and SVM model: a case study of the xinming landslide in china[J]. Environmental Earth Sciences, 2021, 80(10): 1-14.
15	王军, 徐彦惠, 李莉. 低能耗支持完整性验证的数据融合隐私保护方法[J]. 吉林大学学报: 工学版, 2022, 52(7): 1657-1665.
	Wang Jun, Xu Yan-hui, Li Li. Data fusion privacy protection method with low energy consumption and integrity verification[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1657-1665.
16	Chen W, Li Y. GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models[J]. Catena, 2020, 195: 104777.
17	于向军, 槐元辉, 李学飞, 等. 基于克里金和粒子群算法的装载机铲掘轨迹规划[J]. 吉林大学学报: 工学版, 2020, 50(2): 437-444.
	Yu Xiang-jun, Huai Yuan-hui, Li Xue-fei, et al. Shoveling trajectory planning method for wheel loader based on kriging and particle swarm optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 437-444.
18	院老虎, 连冬杉, 张亮, 等. 基于密集连接卷积网络和支持向量机的飞行器机械部件故障诊断[J]. 吉林大学学报: 工学版, 2021,51(5): 1635-1641.
	Yuan Lao-hu, Lian Dong-shan, Zhang Liang, et al. Fault diagnosis of key mechanical components of aircraft based on densenet and support vector machine[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1635-1641.
19	Nguyen B Q, Kim Y T. Rainfall-earthquake-induced landslide hazard prediction by monte carlo simulation: a case study of MT. umyeon in Sroul, Korea[J]. KSCE Journal of Civil Engineering, 2020, 24(1):73-86.
20	张景杨,王维庆,王海云,等.基于CEEMDAN-SAFA-LSSVM的短期风功率预测[J].计算机仿真,2021, 38(8): 134-138.
	Zhang Jing-yang, Wang Wei-qing, Wang Hai-yun, et al. Short-term wind power prediction based on CEEMDAN-SAFA-LSSVM[J]. Computer Simulation, 2021, 38(8): 134-138.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

参数类型	数值
含水量/%	12
密度/（g·cm3）	2.03
黏聚力/kPa	25.7
内摩擦角度/（°）	24.6

[1]	管仁初,贺宝润,梁艳春,时小虎. 基于亲缘关系选择的粒子群优化算法[J]. 吉林大学学报(工学版), 2022, 52(8): 1842-1849.
[2]	王奎洋,何仁. 基于支持向量机的制动意图识别方法[J]. 吉林大学学报(工学版), 2022, 52(8): 1770-1776.
[3]	陈昭明,邹劲松,王伟,石明全. 改进粒子群神经网络融合有限元分析的铸锻双控动态成型多目标优化[J]. 吉林大学学报(工学版), 2022, 52(7): 1524-1533.
[4]	邢海燕,刘超,徐成,陈玉环,王松弘泽. 基于粒子群优化模糊C焊缝等级磁记忆定量识别模型[J]. 吉林大学学报(工学版), 2022, 52(3): 525-532.
[5]	罗巍,卢博,陈菲,马腾. 基于PSO-SVM及时序环节的数控刀架故障诊断方法[J]. 吉林大学学报(工学版), 2022, 52(2): 392-399.
[6]	李翠玉,胡雅梦,康亚伟,张德良. 应用自适应遗传算法的电动汽车充放电协同调度[J]. 吉林大学学报(工学版), 2022, 52(11): 2508-2513.
[7]	侯春萍,赵春月,王致芃,田海瑞. 基于有效异常样本构造的视频异常检测算法[J]. 吉林大学学报(工学版), 2021, 51(5): 1823-1829.
[8]	院老虎,连冬杉,张亮,刘义. 基于密集连接卷积网络和支持向量机的飞行器机械部件故障诊断[J]. 吉林大学学报(工学版), 2021, 51(5): 1635-1641.
[9]	王康,姚猛,李立犇,李建桥,邓湘金,邹猛,薛龙. 基于月面表取采样触月压痕的月壤力学状态分析[J]. 吉林大学学报(工学版), 2021, 51(3): 1146-1152.
[10]	马芳武,韩丽,吴量,李金杭,杨龙帆. 基于遗传与粒子群算法的隔振平台减振性能优化[J]. 吉林大学学报(工学版), 2020, 50(5): 1608-1616.
[11]	杜常清,曹锡良,何彪,任卫群. 基于混合粒子群算法的双离合变速器参数优化设计[J]. 吉林大学学报(工学版), 2020, 50(5): 1556-1564.
[12]	李雄飞,王婧,张小利,范铁虎. 基于SVM和窗口梯度的多焦距图像融合方法[J]. 吉林大学学报(工学版), 2020, 50(1): 227-236.
[13]	谷远利, 张源, 芮小平, 陆文琦, 李萌, 王硕. 基于免疫算法优化LSSVM的短时交通流预测[J]. 吉林大学学报(工学版), 2019, 49(6): 1852-1857.
[14]	赵宏伟,李明昭,刘静,胡黄水,王丹,臧雪柏. 基于自然性和视觉特征通道的场景分类[J]. 吉林大学学报(工学版), 2019, 49(5): 1668-1675.
[15]	卢洋,王世刚,赵文婷,赵岩. 基于离散Shearlet类别可分性测度的人脸表情识别方法[J]. 吉林大学学报(工学版), 2019, 49(5): 1715-1725.