同济大学学报（自然科学版）2026，Vol.54Issue(9)：1333-1344,12.DOI:10.11908/j.issn.0253-374x.25224
基于贝叶斯优化LSTM‑Transformer模型的热带台风暴雨区库岸边坡变形预测
Reservoir Bank Slope Deformation Prediction in Tropical Typhoon-Induced Rainstorm Regions Based on a Bayesian Optimization LSTM-Transformer Model
摘要
Abstract
A hybrid deep learning prediction model integrating Bayesian optimization(BO)with LSTM-Transformer is proposed for reservoir bank slope deformation prediction.The LSTM network is employed to hierarchically extract local temporal variation features associated with rainfall infiltration and historical deformation,while the multi-head self-attention mechanism of the Transformer is used to capture global dependencies of the deformation field across spatiotemporal scales.Bayesian optimization is introduced to achieve adaptive hyperparameter tuning,and a Pearson correlation coefficient matrix is constructed to perform weighted initialization of the attention mechanism,thereby enhancing the model's ability to focus on key monitoring points with coordinated deformation behavior.The proposed model is validated using a water-related slope of a hydraulic engineering project in Hainan Province as a case study.The experimental results show that,compared with standalone LSTM and Transformer models,the BO-LSTM-Transformer model effectively suppresses error accumulation in long-sequence predictions and significantly improves prediction accuracy during deformation stages.This paper provides a high-precision and interpretable prediction tool for intelligent monitoring of reservoir bank slopes under extreme rainfall conditions.关键词
机器学习/LSTM-Transformer/库岸边坡/边坡变形Key words
machine learning/LSTM-Transformer/reservoir bank slope/slope deformation分类
建筑与水利引用本文复制引用
黄雨,王华进,郑永来,蔡夏琳,欧阳汤池,陈晓翔,熊敏..基于贝叶斯优化LSTM‑Transformer模型的热带台风暴雨区库岸边坡变形预测[J].同济大学学报（自然科学版）,2026,54(9):1333-1344,12.基金项目
国家重点研发计划(2024YFC3012600) （2024YFC3012600）
