摘要
为了对公路路基沉降监测数据进行准确分析,本文依据当前监测数据对未来某段时间的变形趋势进行预测,结合最小二乘支持向量机(Least Square Support Vector Machines,LSSVM)模型与改进的粒子群优化算法(Improved Particle Swarm Optimization,IPSO)在数据预测与参数寻优中的优势,构建新的IPSO-LSSVM组合预测模型。该组合预测模型通过IPSO算法不断优化LSSVM模型中的惩罚因子C与核函数参数σ,避免参数选取随意造成预测精度不高的问题。最后使用实测某公路路基沉降数据对本文提出模型的有效性与优越性进行检验,结果表明,经IPSO算法优化的LSSVM模型预测精度更高,稳定性更好,可为变形预测提供一定参考。
In order to accurately analyze the monitoring data of highway subgrade settlement and predict the deformation trend in a certain period of time in the future according to the current monitoring data,this paper combines the advantages of least square support vector machines(LSSVM)model and improved particle swarm optimization(IPSO)algorithm in data prediction and parameter optimization,build a new IPSO-LSSVM combined prediction model.The combined prediction model continuously optimizes the penalty factor C and kernel function parameters in LSSVM model through IPSO algorithm to avoid the problem of low prediction accuracy caused by the randomness of parameter selection.The effectiveness and superiority of the proposed model are tested by using the measured settlement data of a highway subgrade.The results show that the LSSVM model optimized by IPSO algorithm has higher prediction accuracy and better stability,which can provide a certain reference value for deformation prediction.
作者
宋向荣
SONG Xiangrong(Ningbo Branch of CCCC Third Harbor Engineering Bureau Co.,Ltd.,Ningbo 315200,China)
出处
《测绘与空间地理信息》
2023年第12期177-180,共4页
Geomatics & Spatial Information Technology
关键词
公路路基
沉降预测
最小二乘支持向量机
改进粒子群优化算法
highway subgrade
settlement prediction
least squares support vector machine
improved particle swarm optimization algorithm
