摘要
位移预测在滑坡预警预报系统中有着重要地位,为了提高位移预测的精度,提出了变分模态分解和二次指数平滑法、神经网络结合的滑坡位移动态预测方法。首先对历史监测位移数据进行变分模态分解,产生多个模态分量,然后利用二次指数平滑法和极限学习机(ELM)模型进行预测。粒子群优化算法被用来优化ELM模型,最后累加各模态的预测值完成预测。以三峡库区白家包滑坡为例,将所建模型与最小二乘支持向量机(LSSVM)和卷积神经网络-门控递归单元(CNN-GRU)预测的周期项进行比较,结果表明:所用DES-PSO-ELM能够有效预测滑坡位移变化,其均方根误差、平均绝对误差、平均绝对百分误差和相关系数值分别为1.293、0.993 mm,0.0080、0.9998,预测误差最小。研究结果可以为滑坡预警监控系统提供技术依据。
Displacement prediction plays an important role in the landslide early warning and forecasting system.In order to improve the accuracy of displacement prediction,a method for dynamic prediction of landslide displacement combining variational modal decomposition(VMD)and double exponential smoothing method(DES)and neural network was proposed.Firstly,the variational modal decomposition was performed on the historical monitoring displacement data to generate multiple modal components,and then the prediction was carried out using the double exponential smoothing method and the extreme learning machine(ELM)model.And the particle swarm optimization algorithm was used to optimize the ELM model to finally accumulate the predicted values of each modal to complete the prediction.Taking the Baijiabao landslide in the Three Gorges reservoir area as an example,the established model was compared with the period terms predicted by least squares support vector machine(LSSVM)and convolutional neural network-gated recurrent unit(CNN-GRU).The results show that the DES-PSO-ELM used can effectively predict the changes of landslide displacement with the of RMSE,root mean square error(RMSE),mean absolute error(MAE),MAPE(mean absolute percentage error)and Pearson correlations of 1.293 mm,0.993 mm,0.0080 and 0.9998,respectively,showing minimum prediction errors.The research results can provide a technical basis for the landslide early warning monitoring system.
作者
南骁聪
刘俊峰
张永选
王育奎
NAN Xiaocong;LIU Junfeng;ZHANG Yongxuan;WANG Yukui(Shandong Expressway Engineering Testing Co.,Ltd,Shandong Key Laboratory of Expressway Technology and Safety Assessment,Jinan 250000,China;Chengdu University of Technology,State Key Laboratory of Geological Disaster Prevention and Geological Environment Protection,Chengdu 610000,China;Shandong Expressway Yantai Development Co.,Ltd.,Yantai 264000,China)
出处
《人民珠江》
2023年第4期54-62,共9页
Pearl River
基金
国家重点研发计划(2019YFC1509602)。
