[1]王朝义,朱明鑫,张 剑,等.采用PSO-SVM算法的时间序列模型的滑坡体位移预测分析研究[J].华南地震,2026,46(05):184-191.[doi:10.13512/j.hndz.2026.05.22]
　WANG Chaoyi,ZHU Mingxin,ZHANG Jian,et al.Prediction and Analysis of Landslide Displacement Using Time Series model Based on PSO-SVM Algorithm[J].,2026,46(05):184-191.[doi:10.13512/j.hndz.2026.05.22]
点击复制

采用PSO-SVM算法的时间序列模型的滑坡体位移预测分析研究()

华南地震[ISSN:1006-6977/CN:61-1281/TN]

卷:
46
期数:
2026年05期
页码:
184-191
栏目:
地震与地质灾害
出版日期:
2026-09-07

文章信息/Info

Title:
Prediction and Analysis of Landslide Displacement Using Time Series model Based on PSO-SVM Algorithm
文章编号:
1001-8662（2026）05-0184-08
作者:
王朝义; 朱明鑫; 张 剑; 王 崇; 潘俊杰
河南省核技术应用中心，郑州 450044
Author(s):
WANG Chaoyi; ZHU Mingxin; ZHANG Jian; WANG Chong; PAN Junjie
Henan Nuclear Technology Application Center, Zhengzhou 450044, China
关键词:
位移预测; 滑坡体位移; PSO-SVM; 时间序列模型; 滑坡体
Keywords:
Displacement prediction;  Landslide displacement;  PSO-SVM;  Time series model;  Landslide
分类号:
P642
DOI:
10.13512/j.hndz.2026.05.22
文献标志码:
A
摘要:
为解决多时间尺度信息处理困难，导致滑坡体位移预测精度较低的问题，提出基于PSO-SVM算法和时间序列模型的滑坡体位移预测方法。以时间戳为指标建立各类影响因素的数据时间序列，并采用灰色关联分析确定各影响因素与滑坡体位移的关联度；根据数据时间序列，采用差分自回归移动平均模型构建时间序列预测模型，设置逼近预测精度的目标函数，添加滑坡移动过程的限制条件；创新性地在时间序列预测模型中引入支持向量机的回归函数，采用支持向量机（SupportVectorMachine,SVM）算法优化模型，改进位移预测的目标函数，引入粒子群优化算法（ParticleSwarmOptimization,PSO）迭代求解模型，获得最优模型解，实现滑坡体位移预测。实验结果表明：该方法对于滑坡体位移的预测均方根误差均值仅为0.98mm，预测精度较高。
Abstract:
To solve the problem of low accuracy in landslide displacement prediction caused by difficulties in pro‐cessing information at multiple time scales, a landslide displacement prediction method based on PSO-SVM algo‐rithm and time series model is proposed. The data time series of various influencing factors were established with timestamp as indicators, and the correlation degree between each influencing factor and landslide displacement was determined by grey correlation analysis. Based on the data time series, an autoregressive integrated moving average (ARIMA) model was used to construct the time series prediction model, the objective function was set for approach‐ing the prediction accuracy, and the constraints conditions were added to the landslide movement process. The re‐gression function of support vector machine was innovatively introduced into the time series prediction model. The support vector machine (SVM) algorithm was used to optimize the model and improve the objective function of dis‐placement prediction. The particle swarm optimization (PSO) algorithm was introduced to iteratively solve the model to obtain the optimal model solution and realize the displacement prediction of landslide. The experimental results show that the average root mean square error of this method for predicting landslide displacement is only 0.98 mm, indicating high prediction accuracy.

参考文献/References:

[1]罗超雷,徐哈宁,肖慧,等.基于鲸鱼优化混合神经网络的滑坡位移预测[J].科学技术与工程,2024,24(16):6610-6616.
[2]陈朗,陈娱,何俊霖,等.基于前期累积降雨和高斯过程回归模型的滑坡位移预测[J].岩石力学与工程学报,2024,43 (S1):3491-3497.
[3]任帅,纪元法,孙希延,等.基于改进灰狼优化与支持向量回归的滑坡位移预测[J].计算机应用,2024,44(3):972-982.
[4] Zhang M Y, Han Y, Yang P, et al. Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network[J]. Journal of Mountain Science, 2023, 20 (3): 637-656.
[5]唐宇峰,胡光忠,周帅.动态残差修正LSTM算法的突发型滑坡位移预测[J].中国安全科学学报,2023,33(8):109-116.
[6]郭子正,杨玉飞,何俊,等.考虑注意力机制的新型深度学习模型预测滑坡位移[J].地球科学,2024,49(5):1665-1678.
[7]瞿伟,刘祥斌,李久元,等.改进哈里斯鹰优化算法与BP神经网络组合的滑坡位移高精度预测模型[J].地球科学与环境学报,2023,45(3):522-534.
[8]熊超,孙红月.基于多因素-多尺度分析的阶跃型滑坡位移预测[J]. 吉林大学学报(地球科学版),2023,53(4):1175-1184.
[9]贺汪延,张巍,李厚芝,等.知识图谱优化卡尔曼滤波预测库岸滑坡位移[J].高校地质学报,2023,29(3):372-381.
[10]唐菲菲,周海莲,唐天俊,等.融合动静态变量的滑坡多步位移预测方法[J].吉林大学学报(工学版),2023,53 (6):1833-1841.
[11]胡华,黄维.基于ICEEMDAN分解和MHA-LSTM神经网络的滑坡位移预测[J].安全与环境工程,2023,30(3):152-160.
[12]纪佑军,熊军,蒋国斌,等.考虑应变软化的鸡场镇降雨型滑坡数值分析[J].水文地质工程地质,2024,51(4):178-188.
[13]姚杰,李秀珍,郑玲静.基于一维黏塑性运动模型的大型慢速滑坡变形预测研究[J].自然灾害学报,2023,32(1):208-216.
[14]阳清青,余秋兵,张廷斌,等.基于GDIV模型的大渡河中游地区滑坡危险性评价与区划[J].中国地质灾害与防治学报,2023,34(5):130-140.
[15]李达,瞿伟,张勤,等.融合多层感知机和优化支持向量回归的滑坡位移预测模型[J].武汉大学学报(信息科学版),2023,48(8):1380-1388.
[16]任帅,纪元法,孙希延,等.基于改进灰狼优化与支持向量回归的滑坡位移预测[J].计算机应用,2024,44(3):972-982.
[17]罗超雷,徐哈宁,肖慧,等.基于鲸鱼优化混合神经网络的滑坡位移预测[J]. 科学技术与工程,2024,24(16):6610-6616.
[18]艾骁,张健,付济民,等.不同特征选择方法于区域地震滑坡危险性预测结果的差异性分析——以汶川地震区为例[J].华南地震,2024,44(2):39-51.
[19]陈龙,葛澄,戴颖超,等.基于深度学习的高分辨率遥感影像滑坡体识别方法研究[J].华南地震,2025,45(2):66-74.

备注/Memo

备注/Memo:
收稿日期：2025-10-20
作者简介：王朝义（1988-），男，工程师，主要从事地质灾害调查评价与防治、地质灾害预警监测研究。E-mail: c595743526@163.com
更新日期/Last Update: 2026-09-20