摘要
鉴于预测边坡位移变化对边坡稳定性的重要意义,利用布谷鸟优化算法(COA)对最小二乘支持向量机(LSSVM)的核函数参数和惩罚因子进行寻优,从而建立了边坡位移时序预测的COA-LSSVM模型,并将该模型应用于锦屏一级水电站左岸高边坡变形预测中。与PSO-LSSVM模型的预测结果对比表明,COA-LSSVM模型具有更高的预测精度,预测结果更接近于实际的监测数据。
As the prediction of slope displacement has great significance of the slope stability, cuckoo optimization algorithm（COA） is used to optimize the parameters of LSSVM and COA-LSSVM prediction model based on the slope displacement time-series is established. This model is applied to the left bank slope in Jinping I Hydropower Station. Compared with the forecasting results of PSO-LSSVM model, it shows that the proposed model has higher forecast precision and the forecast results are closer to the actual monitor values than that of PSO-LSSVM.
出处
《水电能源科学》
北大核心
2014年第5期105-108,100,共5页
Water Resources and Power
基金
国家重点基础研究发展计划(973项目)(2011CB013504)
国家自然科学基金项目(11172090)
江苏省高校研究生科研创新计划(CXLX12_0242)
关键词
边坡
预测
位移时序
最小二乘支持向量机
布谷鸟优化算法
slope
prediction
displacement time series
least squares support vector machine
cuckoo optimization algorithm
