Processing Method of Missing Data in Dam Safety Monitoring

Wei, Wei; Gu, Chongshi; Fu, Xiao

doi:10.1155/2021/9950874

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Machine learning techniques are widely used in various practical application fields, such as air pollution monitoring [27], industrial process monitoring [2], dam safety monitoring [28,29], medical data processing [30], and stock price prediction [31]. To address the challenges posed by missing data, several machine learning-based methods have gained significant popularity [12].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset

Gan,

Gong,

et al. 2023

Sensors

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Batch process monitoring datasets usually contain missing data, which decreases the performance of data-driven modeling for fault identification and optimal control. Many methods have been proposed to impute missing data; however, they do not fulfill the need for data quality, especially in sensor datasets with different types of missing data. We propose a hybrid missing data imputation method for batch process monitoring datasets with multi-type missing data. In this method, the missing data is first classified into five categories based on the continuous missing duration and the number of variables missing simultaneously. Then, different categories of missing data are step-by-step imputed considering their unique characteristics. A combination of three single-dimensional interpolation models is employed to impute transient isolated missing values. An iterative imputation based on a multivariate regression model is designed for imputing long-term missing variables, and a combination model based on single-dimensional interpolation and multivariate regression is proposed for imputing short-term missing variables. The Long Short-Term Memory (LSTM) model is utilized to impute both short-term and long-term missing samples. Finally, a series of experiments for different categories of missing data were conducted based on a real-world batch process monitoring dataset. The results demonstrate that the proposed method achieves higher imputation accuracy than other comparative methods.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset

Gan,

Gong,

et al. 2023

Sensors

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“…The behavior of a concrete dam is a nonlinear dynamic evolution process in which materials and structures interact under the synergistic action of multiple factors [1,2]. As a comprehensive effect quantity of the performance of the concrete dam, deformation always attracts more attention as it indicates the transformation of the structural behavior of the dam [3][4][5]. An enormous amount of deformation monitoring data were gathered for fundamental analysis and predictions of dam deformation behavior during the lifespan of a concrete dam [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

Cui

et al. 2023

Mathematics

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To ensure the safety of concrete dams, a large number of monitoring instruments are embedded in the bodies and foundations of the dams. However, monitoring data are often missing due to failure of monitoring equipment, human error and other factors that cause difficulties in diagnosis of dam safety and failure to precisely predict their deformation. In this paper, a new method for imputing missing deformation data is proposed. First, since the traditional deformation increment speed distance index of the deformation similarity index does not take into account the fact that there is little change in deformations occurring in two consecutive days, the denominator of the index tends to be equal to zero. In this paper, an improved index for solving this problem is proposed. A combined weighting method for calculating the deformation similarity comprehensive index and the k-means clustering method is then proposed and used to classify deformation monitoring points. Subsequently, a panel data model that imputes different types of missing data is established. The method proposed in this paper can impute missing concrete dam deformation data more accurately; therefore, it can effectively solve the missing deformation monitoring data problem.

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“…Zhang et al [27] predicted the displacements of the Bazimen and Baishuihe landslides in the Three Gorges, China with a variational mode decomposition-bidirectional LSTM model with optimized features. Wei et al [28] presented a missing data processing method in terms of the partial distance fuzzy C-means model and bidirectional LSTM network. Le et al [29] evaluated the performance of several deep learning models for streamflow forecasting, and the study shows that the LSTM-based models have better performance and stability than the feedforward neural network (FFNN) and convolutional neural network (CNN) models.…”

Section: Introductionmentioning

confidence: 99%

LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station

Wang¹,

Yang²,

Chen³

et al. 2022

Water

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The Danjiangkou hydropower station is a water source project for the middle line of the South-to-North Water Transfer Project in China. The dam is composed of riverbed concrete dam and earth rock dam on both banks, with a total length of 3442 m. Once the dam is wrecked, it will yield disastrous consequences. Therefore, it is very important to evaluate the dam safety behavior in time. Based on the long-term and short-term memory (LSTM) network, the deformation prediction models of the embankment dam of the Danjiangkou hydropower station are constructed. The models contain two LSTM layers, adopt the rectified linear unit function as the activation function and determine the super parameters of the models with Bayesian optimization algorithm. According to the settlement monitoring data of LD12ZT01 measuring point (dam crest 0 + 648) on the left bank of the embankment dam of the Danjiangkou hydropower station from July 2013 to March 2022, the LSTM and bidirectional LSTM models are constructed. In total, 80% of the monitoring data are taken as the training set data and 20% of the monitoring data are taken as the test set data. The mean absolute error, root mean square error and mean square error for the test set are 0.42978, 0.56456 and 0.31873 for partial least squares regression (PLSR), 0.35264, 0.47561 and 0.22621 for LSTM and 0.34418, 0.45400 and 0.20612 for bidirectional LSTM, respectively. The results show that the bidirectional LSTM model can obtain better deformation prediction value than the LSTM model and the PLSR. Then, the bidirectional LSTM model is used to predict the settlement value of LD16YT01 measuring point (dam crest 0 + 658) on the right bank, and the mean absolute error, root mean square error and mean square error for the test set are 0.5425, 0.66971 and 0.4520, respectively. This shows the bidirectional LSTM model can effectively predict the settlement value of the embankment dam of the Danjiangkou hydropower station.

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Processing Method of Missing Data in Dam Safety Monitoring

Cited by 6 publications

References 29 publications

A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset

A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station

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