水力发电2024，Vol.50Issue(1)：37-41,52,6.
基于CNN-LSTM的大坝变形组合预测模型研究
Research on Deformation Prediction Method of Concrete Face Rockfill Dam Based on CNN-LSTM
王润英 1林思雨 1方卫华 2赵凯文1
作者信息
- 1. 河海大学水利水电学院, 江苏 南京 210024
- 2. 水利部南京水利水文自动化研究所, 江苏 南京 210012||水利部水文水资源监控工程技术研究中心, 江苏 南京 210012
- 折叠
摘要
Abstract
In order to improve the accuracy and generalization ability of dam deformation prediction models a neural network combination prediction model CNN-LSTM is established based on the convolutional neural networks (CNN) and the deep learning long short-term memory.In this model the CNN is firstly used to extract the features of dam deformation monitoring time series and then the LSTM is used to generate the feature description.This model has high accuracy and strong generalization ability.Taking Baiyekou Face Rockfill Dam as an example the CNN-LSTM model is used to calculate the dam deformation and the calculation results are compared with the actual monitoring data.The calculation results of CNN-LSTM model are also compared with that of the LSTM model with the CNN model respectively.The comparisons show that the predicted value of CNN-LSTM model is more close to the actual monitoring data.关键词
大坝变形/卷积神经网络/LSTM 神经网络/变形预测/预测精度/柏叶口水库Key words
dam deformation/convolutional neural network (CNN)/LSTM neural network/deformation prediction/Baiyekou Face Rockfill Dam分类
建筑与水利引用本文复制引用
王润英,林思雨,方卫华,赵凯文..基于CNN-LSTM的大坝变形组合预测模型研究[J].水力发电,2024,50(1):37-41,52,6.
