人民珠江2025，Vol.46Issue(4)：1-8,8.DOI:10.3969/j.issn.1001-9235.2025.04.001
基于CNN-BiLSTM-Attention的重力坝稳定时变安全系数预测模型
Prediction Model for Time-varying Safety Factor for Gravity Dam Stability Based on CNN-BiLSTM-Attention
摘要
Abstract
Under complex working conditions such as high water pressure and high seepage pressure,accurately grasping the time-varying law of the safety factor of gravity dams and effectively predicting it are crucial for the scientific control of the dam's operation status.To this end,a coupled prediction model is proposed based on the CNN-BiLSTM-Attention method of deep learning theory,with the upstream water level,the riverward displacement at the dam crest,and time-dependent effects as independent variables,and the anti-sliding stability coefficient as the dependent variable.Through the analysis of a gravity dam project with a height of 148.0 meters,the model demonstrates a mean absolute error(MAE)and root mean square error(RMSE)of 1.12×10-3 and 1.66×10-3,respectively,prediction errors MAE and RMSE of 3.08×10-3 and 3.53×10-3,respectively.Compared to traditional statistical regression methods,this model has increased the prediction accuracy by 51.80%and 45.44%,and when compared to the SVM algorithm,the prediction accuracy has increased by 16.08%and 10.18%,respectively.This indicates that the proposed model has a better alignment with the finite element calculation result curves and a more remarkable advantage in prediction accuracy.关键词
CNN-BiLSTM-Attention/重力坝/预警指标/预测模型Key words
CNN-BiLSTM-Attention/gravity dam/early warning indicator/prediction model分类
水利科学引用本文复制引用
曹宇鑫,张瀚,尹金超,李亚楠..基于CNN-BiLSTM-Attention的重力坝稳定时变安全系数预测模型[J].人民珠江,2025,46(4):1-8,8.基金项目
四川省科技厅重点研发项目(2022YFS0535) （2022YFS0535）
