摘要
针对大坝变形时间序列预测问题,考虑多测点变形相关性,建立变形量时空多维输入矩阵,提出一种基于K-means聚类融合多元时空信息的Informer-AD大坝变形预测模型。首先,采用K-means聚类对变形测点进行分区;其次,引入面板数据回归模型分析分区结果;最后,提出融合多元时空信息的Informer-AD大坝变形预测模型。利用该模型对空间特征序列进行学习,通过全连接层整合空间特征,输出预测的大坝变形值。将上述预测模型运用于CT混凝土重力坝,结果表明,本文所提出的考虑时空关联性的预测方法充分挖掘大坝变形整体性态与测点空间分布特性的关系,能够更好地捕捉变形时空特性,进而提高预测精度。
For the time series prediction issue of dam deformation,a spatiotemporal multi-dimensional input matrix of deformation is derived considering the correlation of deformation at multiple measuring points;an Informer-AD dam deformation prediction model is constructed that integrates multi-dimensional spatiotemporal information based on K-means clustering.We use the K-means clustering to partition rationally the deformation measuring points,then apply a panel data regression model to integrate the analysis of spatiotemporal dimensions and partition results.Finally,we develop an Informer-AD dam deformation prediction model to integrate multi-dimensional spatiotemporal information.This model is used to learn spatial feature sequences and integrate spatial features through a fully connected layer to output predicted dam deformation values.Its application to a concrete gravity dam shows that our prediction method,considering spatiotemporal correlation,can fully explore the relationship of the overall state of dam deformation versus the spatial distribution characteristics of measuring points.It better captures the spatiotemporal characteristics of deformation values and thus improves prediction accuracy,which implies that our model has a high accuracy and satisfactory applicability,useful for engineering application.
作者
苏燕
黄姝璇
林川
李伊璇
付家源
郑志铭
SU Yan;HUANG Shuxuan;LIN Chuan;LI Yixuan;FU Jiayuan;ZHENG Zhiming(Civil Engineering College,Fuzhou University,Fuzhou 350108,China;Fujian Water Conservancy and Hydropower Survey and Design Institute,Fuzhou 350001,China)
出处
《水力发电学报》
CSCD
北大核心
2023年第11期101-113,共13页
Journal of Hydroelectric Engineering
基金
国家自然科学基金项目(52109118)
水利部重大科技项目(SKS-2022151)
福建省自然科学青年基金项目(2020J05108)。
