中国地质灾害与防治学报2025，Vol.36Issue(3)：1-8,8.DOI:10.16031/j.cnki.issn.1003-8035.202311014
基于动态串联PSO-BiLSTM的滑坡变形速率预测方法研究
Research on landslide deformation rate prediction method based on dynamic serial PSO-BiLSTM
摘要
Abstract
This paper proposed a method for predicting landslide deformation rates using a dynamic serial PSO-BiLSTM approach,aiming to overcome the limitation such as insufficient accuracy and low computational efficiency found in existing methods.Initially,the deformation rate of landslides is captured through a dynamic sliding window technique,and the resulting sequence is decomposed using ensemble empirical mode decomposition(EEMD)to extract trend and periodic components.Subsequently,the deformation rate prediction sequences of trend and periodic components were obtained through polynomial fitting and a periodic component of PSO-BiLSTM network,respectively.After several cycles that produce residual deformation rate sequences,these are integrated with the initial prediction sequences to establish a comprehensive PSO-BiLSTM prediction network that yields the total predicted deformation rate.The method was validated with a landslide monitoring case in Sichuan Province,achieving a MAE of 0.28,a MAPE of 5.41％,an RMSE of 0.57,and an R2 of 0.98,with a computation time of 380.22 seconds,thus ensuring high accuracy and computational efficiency.关键词
PSO/双向长短时记忆神经网络/集合经验模态分解/变形速率预测Key words
PSO/bi-directional long short-term memory(Bi-LSTM)network/EEMD/deformation rate prediction分类
地质学引用本文复制引用
唐宇峰,何俚秋,曹睿..基于动态串联PSO-BiLSTM的滑坡变形速率预测方法研究[J].中国地质灾害与防治学报,2025,36(3):1-8,8.基金项目
四川省科技厅科技支撑项目(2022NSFSC1154) （2022NSFSC1154）
企业信息化与物联网测控技术四川省高校重点实验室开放基金项目(2023WYJ04) （2023WYJ04）
四川轻化工大学科研创新团队计划项目(SUSE652A004) （SUSE652A004）
