首页|基于观测数据可靠性辨识与挖掘的混凝土坝位移时序预测方法

基于观测数据可靠性辨识与挖掘的混凝土坝位移时序预测方法

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针对大坝观测数据中异常值导致时间序列可靠性不高引发的位移监测模型训练效果不佳与预报精度不高等问题,提出了基于观测数据可靠性辨识与挖掘的混凝土坝位移时序预测方法.通过比对分析混凝土坝位移监测中异常数据的处置效果,证实了辨识大坝观测时序中异常值并加以有效挖掘补缺的必要性,引入变分模态分解算法将剔除异常值的时间序列分解为多个特征突出子序列,将提取出的时序数据经双向长短时记忆网络模型学习训练再重构以获取缺失数据的替代值,构建了基于深度学习的大坝观测数据缺值插补模型,由此建立了混凝土坝观测时序数据缺值插补的位移预报模型.以某混凝土坝变形监测数据为例,运用该方法对观测时序数据异常性与相关性识辨分析,对缺失数据进行处理和填补,进而把缺值插补问题转化为缺值预测问题.对比分析表明,此方法具有强鲁棒性和高精度数据缺值插补与预报能力.
Time Series Prediction Method of Concrete Dam Displacement Based on Reliability Identification and Excavation of Observation Data
The existence of abnormal values in dam observation data tends to reduce its reliability,and is bound to lower the training and prediction accuracy of the displacement monitoring model.In order to solve such problems,a time series prediction method for concrete dam displacement based on reliability identification and excavation of observation data is proposed in this paper.By comparing and analyzing the disposal effect of abnormal data in displacement monitoring,this research proves the necessity of identifying abnormal values within dam observation time series and effectively interpolating them.For this reason,variational mode decomposition algorithm is introduced to decompose the processed time series into several feature-protruding subsequences,and the extracted time series are reconstructed after obtaining the replacement value of lacking data by training the bidirectional long short-term memory network-based model.The deep learning-based interpolation model for concrete dam observation data is constructed,and then the displacement prediction model of concrete dam based on defective observation time series data is established.Taking a concrete gravity dam as an example,this method is utilized to identify the lacking data and correlations of observation time series,and then interpolate the anomalies,so that the problem of interpolating the missing data can be transformed into the problem of predicting the missing values.The comparative analysis shows that this method has strong robustness and high-precision on data missing interpolation and forecasting ability.

interpolation methodfailure datavariational mode decompositionbidirectional long short-term memory networkfeature extractiondeformation monitoring

魏博文、徐文博、袁冬阳、徐富刚

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南昌大学工程建筑学院,江西南昌 330031

水灾害防御全国重点实验室,江苏南京 210098

插补方法 故障数据 变分模态分解 双向长短期记忆神经网络 特征提取 变形监控

国家自然科学基金项目国家自然科学基金项目江西省杰出青年自然科学基金项目中国博士后基金项目江西省自然科学基金项目江西省水利厅重大科技项目

523791255216902520192ACB210222019M65228120192BAB216040202224ZDKT21

2024

10.16058/j.issn.1005-0930.2024.04.011

应用基础与工程科学学报
中国自然资源学会

应用基础与工程科学学报

CSTPCD北大核心
影响因子：0.895
ISSN：1005-0930
年,卷(期)：2024.32(4)
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