摘要
针对滑坡位移监测较为复杂的问题,基于福利院滑坡处水文地质工程地质条件,将BP小波神经网络预测模型引入滑坡变形监测预报中,预测了福利院滑坡变形趋势,并与BP神经网络的预测结果做了比较。结果表明,BP小波神经网络预测结果明显优于BP神经网络,且训练次数大幅减少、自适应能力强、预测精度高。
In view of the complex problem of landslide deformation monitoring, BP wavelet neural network model is applied to forecast landslide deformation based on hydrogeology and engineering geology conditions of Fuliyuan landslide. And then the deformation trend of Fuliyuan landslide is predicted. Compared with the BP neural network, the results show that the prediction effect of BP wavelet neural network is better than that of BP neural network, and BP wavelet neural network can significantly reduce training times with good adaptive ability and high prediction accuracy.
出处
《水电能源科学》
北大核心
2011年第8期109-111,89,共4页
Water Resources and Power
关键词
小波神经网络
滑坡
深部位移
预测
wavelet neural networks Fuliyuan landslides deep deformations prediction
作者简介
作者简介：崔英明（1974-），男，高级工程师，研究方向为路桥工程的勘察设计，E-mail：ggh2009@126．com
