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深度学习提取时空特征的堆石坝变形预测模型 被引量:5

Rockfill dam deformation prediction model based on deep learning-extracted spatiotemporal features
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摘要 当前堆石坝变形智能预测模型较少关注多测点变形时间序列在时空特征上的不均衡性,因此限制了变形预测精度的进一步提高。为了解决该问题,本文提出了一种结合卷积神经网络、注意力机制和长短期记忆神经网络的堆石坝变形预测模型(CTSA-ConvLSTM),该模型可以提取变形时空特征,对不同时刻和不同位置的测点赋予不同的权重系数,实现对堆石坝整体变形规律的自适应学习。以水布垭面板堆石坝为例,采用该模型和最大断面所有测点的变形监测数据,验证了模型的有效性。模型预测效果优于Holt-Winters等常规时序预测模型,预测精度也优于笔者提出的基于LSTM的变形预测模型。通过深度学习提取监测数据时空特征,进一步提高了大坝变形预测精度,为大坝安全监控模型提供了新的思路。 Previous models for intelligent prediction of rockfill dam deformation,lacking attention to the uneven distribution of deformation time series over multiple measuring points,are limited to low accuracy.This paper develops a rockfill dam deformation prediction model,CTSA-ConvLSTM,to combine a convolutional neural network(CNN),the attention mechanism,and a long short-term memory(LSTM)neural network.This model extracts the temporal and spatial characteristics of deformation and generates different weights for the measurements taken at different instants and different locations,so that it realizes the adaptive learning of global deformation patterns of a rockfill dam.In the case study of the Shuibuya dam,the model is verified against the deformation data from all the measuring points at the maximum dam section.It performs better than Holt-Winters and other conventional time series prediction models,and its prediction accuracy is higher than that of a LSTM-based deformation model developed by the authors.By extracting the spatiotemporal characteristics of monitoring data through deep learning,it improves the accuracy and provides a new idea for improving dam safety monitoring models.
作者 陈颖 马刚 周伟 吴继业 邹全程 CHEN Ying;MA Gang;ZHOU Wei;WU Jiye;ZOU Quancheng(State Key Laboratory of Water Resources Engineering and Management,Wuhan University,Wuhan 430072,China;Institute of Water Sciences,Wuhan University,Wuhan 430072,China)
出处 《水力发电学报》 CSCD 北大核心 2023年第5期120-132,共13页 Journal of Hydroelectric Engineering
基金 国家重点研发计划项目(2022YFC3005505) 国家自然科学基金资助项目(52179141,U1865204)。
关键词 堆石坝变形预测 时空相关性 卷积神经网络(CNN) 注意力机制 卷积长短期记忆网络(ConvLSTM) deformation prediction of rockfill dam spatial and temporal correlation convolutional neural network(CNN) attention mechanism convolutional long short-term memory network(ConvLSTM)
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