Gross Error Identification of Reservoir and Dam Safety Monitoring Data Based on Outlier Characteristics

2021 ◽  
Author(s):  
Lan Zhang ◽  
Qi Ling ◽  
Lin Pan
Keyword(s):  
Safety Monitoring ◽  
Monitoring Data ◽  
Dam Safety ◽  
Error Identification ◽  
Gross Error ◽  
Dam Safety Monitoring

scholarly journals Application of the Bayesian Belief Nets in dam safety monitoring

2012 ◽  
Vol 44 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Dorota Mirosław-Świątek ◽  
Mariusz Kembłowski ◽  
Władysław Jankowski
Keyword(s):  
Water Pressure ◽  
Safety Monitoring ◽  
Data Interpretation ◽  
Technical Condition ◽  
Monitoring Data ◽  
Earth Dam ◽  
Physical Parameters ◽  
Dam Safety ◽  
Dam Safety Monitoring ◽  
Bayesian Belief Nets

Application of the Bayesian Belief Nets in dam safety monitoring The systems for earth dam monitoring should enable measurements of basic physical parameters describing the behavior of the structure, including: soil water pressure, soil stress, displacements, leaks, and drainage discharges. In the case of earth dam safety assessment, the monitoring data are used to detect any anomalies in dam behavior. In this paper, the dam safety has been analyzed using the Bayesian Nets. Two types of information: water pressure measurements and drainage discharge measurements are used in analyses. The seepage anomalies in the Klimkówka Dam were considered in demonstrate the practical advantages of using the Bayesian Nets for monitoring data interpretation. Presented examples of the Bayesian Nets applications (forward and backward propagation) in analysis of seepage through earth dams show that this method can be an effective tool supporting an assessment of dams technical condition and monitoring of the dam safety.

The Application of a New Compromise Thresholding Method in the Safety-Monitoring of Dam Displacement

2014 ◽  
Vol 584-586 ◽  
pp. 2113-2116
Author(s):  
Gaotang Cai
Keyword(s):  
Prediction Accuracy ◽  
Safety Monitoring ◽  
Horizontal Displacement ◽  
Monitoring Data ◽  
Dam Safety ◽  
Important Work ◽  
Thresholding Method ◽  
Dam Safety Monitoring ◽  
Displacement Based ◽  
Dam Monitoring

How to identify and remove the noise of the dam monitoring data is an important work of dam safety-monitoring. The article makes use of a new compromise thresholding method to denoise the the dam horizontal displacement. Based on the de-noising data, the article uses the PSO-SVM to overfit and predict the dam horizontal displacement and compare the results with multiple regression.The result shows that the combination of the new compromise thresholding method and PSO-SVM has a better prediction accuracy.

scholarly journals Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2517
Author(s):  
Hao Chen ◽  
Yingchi Mao ◽  
Longbao Wang ◽  
Hai Qi
Keyword(s):  
Time Series ◽  
Sensor Network ◽  
Dynamic Change ◽  
Safety Monitoring ◽  
Arch Dam ◽  
Monitoring Data ◽  
Dam Safety ◽  
Network Partition ◽  
Temporal Features ◽  
Dam Safety Monitoring

Many various types of sensors have been installed to monitor the deformation and stress in the dam structure. It is difficult to directly evaluate the operation status of the dam structure based on the massive monitoring data. The sensor network is divided into multiple regions according to the design specifications, simulation data, and engineering experiences. The local results from sub-regions are integrated to achieve overall evaluation. However, it ignores the spatial distribution of sensors and the variation of time series, which cannot meet the real-time evaluation for the dam safety monitoring. If the network partitions can provide the preliminary foundation for analyzing the dynamic change laws of the dam’s working conditions in a real-way, we should consider the similarity of structure and stresses in the local region of the dam and the correlation among the monitoring data. A time-series denoising autoencoder (TSDA) is proposed to represent the spatial and temporal features of the nodes by compressing high-dimensional monitoring data. Then, a network partitioning algorithm (NPA) based on spatial-temporal features based on the TSDA is presented. The NPA ensures that the partition results can support the analysis of the physical change laws by introducing the auxiliary objective variable to optimize the network partition objective function. Experimental results on the public datasets and a real dataset from an arch dam demonstrate that the proposed network partition algorithm NPA can achieve better partition performance than TSDA+K-Means and TSDA+GMM. The NPA can improve the silhouette coefficient by 45.1% and 58.4% higher than the TSDA+K-Means and TSDA+GMM, respectively. The NPA can increase the Calinski-Harabaz Index by 30.8% and 61.6%, respectively.

A three-stage online anomaly identification model for monitoring data in dams

2021 ◽  
pp. 147592172110257
Author(s):  
Ying Xu ◽  
Huibao Huang ◽  
Yanling Li ◽  
Jingren Zhou ◽  
Xiang Lu ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Online Monitoring ◽  
Single Point ◽  
Safety Monitoring ◽  
Monitoring And Evaluation ◽  
Temporal Analysis ◽  
Monitoring Data ◽  
Dam Safety ◽  
Environmental Response ◽  
Dam Safety Monitoring

The monitoring of data anomaly identification is an important basis for dam safety online monitoring and evaluation. In this research, a cluster of anomaly identification models for dam safety monitoring data was constructed, and a three-stage online anomaly identification method was proposed to discriminate outliers. The proposed method combined anomaly detection for measured values based on a single-point time series simulation, measurement error reduction based on remote retesting and spatio-temporal analysis, and environmental response mutation recognition. It brought about efficient and accurate detection for data mutation and online classified identification for its inducement. Additionally, problems such as missing outliers, misjudging normal values induced by the environmental response, and difficulty in online identification for measurement errors were effectively solved. The research productions were applied to the online monitoring system for the safety risk of reservoirs and dams in the Dadu River Basin. The results showed that the proposed method could effectively improve the accuracy of anomaly identification and reduce the misjudgment and omission rate to less than 2%. It could also successfully recognize and subtract nonstructural anomalies such as accidental errors, instrument faults, and environmental responses online, which provided reliable data for online dam safety monitoring.

scholarly journals Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jintao Song ◽  
Shengfei Zhang ◽  
Fei Tong ◽  
Jie Yang ◽  
Zhiquan Zeng ◽  
...  
Keyword(s):  
Panel Data ◽  
Outlier Detection ◽  
Correlation Coefficients ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Structural Safety ◽  
Dam Safety ◽  
Dam Safety Monitoring ◽  
Dam Deformation

A dam is a super-structure widely used in water conservancy engineering fields, and its long-term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long-time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K-means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K-means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping-I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.

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