Anomaly recognition and early warning of monitoring data are of great significance in the field of modern dam safety management. Multidimensional least-squares regression model with the Pauta criterion is a well-known traditional method, but it is easy to misjudge the normal value and miss the outliers. Thereby, an online robust recognition and early warning model combining robust statistics and confidence interval is proposed to detect outliers. The threshold $3S_T+D$ is set based on the derived confidence interval $D$ and the scale estimator $S_T$ (derived from the location M-estimator). Monitoring data obtained from a gravity dam and a rockfill dam were taken as examples to demonstrate the robust recognition and early warning model. The results show that the proposed method can effectively improve the reliability of anomaly recognition and early warnings, which is valuable in engineering applications.

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.

Constructing a long-term deformation monitoring model for earth–rock dams that integrates multisource monitoring information is highly important for enhancing the safety state evaluation and monitoring effectiveness of such dams. In this paper, we propose a new health monitoring model named the deformation–seepage–water level multimeasurement point health monitoring (DSW-MPHM) model for earth–rock dams based on deep graph feature fusion. This model fuses coupled seepage, deformation, and water level features from different monitoring sites of the dam body, base, and shoulder. To achieve this goal, we first establish a new module to fuse spatial and temporal features using graph convolutional networks and long short-term memory. Seepage features and water level features are then extracted using graph attention mechanisms. Subsequently, we employ the feature fusion technique, which incorporates principal component analysis and gated fusers, to construct the DSW-MPHM model, which effectively fuses information from multiple sources. This novel approach successfully addresses the issues of information redundancy and the limited reliability of monitoring models. To verify the validity of the model, it is applied to an endoscopic deformation monitoring program of a panel rockfill dam with a height of 185.5 m. The results demonstrate the superior stability and effectiveness of the proposed method compared to those of 10 baseline prediction models. Additionally, the characterization of the seepage and water level features extracted from the model is verified for its reasonableness. Thus, our proposed model is well suited for practical engineering applications.

Common anomaly recognition methods are easy to misjudge and miss outliers for the online monitoring data. This is a bottleneck problem that needs to be overcome in dam safety management moving toward informatization. Based on the data of nine hydropower stations along Dadu River Basin, this paper analyzed existing problems of the common anomaly identification method and an algorithm was proposed based on improved M-robust regression recognition. In this algorithm, the AR factor was introduced to avoid the defect that the traditional model cannot simulate random variables. The extreme value method and robust estimation were utilized to avoid the leverage effect. The model collapse caused by maximum measured value was avoided through improving the residual calculation model of M-robust and optimizing the weight distribution function. The maximum of the three values, residual quartile difference, discrete quartile difference, and measurement accuracy, was used as an anomaly recognition criterion to improve the evaluation criteria. The algorithm compiled was used in the Dadu River Company since 2017. The statistics showed that for the 150,000 measured values per day, the evaluation time could be within 15 min, the missed judgment rate was 0%, and the misjudgment rate was less than 2%. The proposed algorithm achieved a great improvement and can meet the needs of online outlier recognition in dam safety management.

The hydropower project had comprehensively benefited people from aspects of the economy, society, ecology, etc. The comprehensive benefit is a key indicator for evaluating a project’s performance. However, the existing studies only evaluated the comprehensive benefit and ignored the relationship among different benefits, which is of great significance for the sustainable development of a project. Therefore, in the framework of the complex system composed of economic, social, and ecological benefit subsystems, a synergy degree evaluation method is constructed based on the evaluation index system of the comprehensive benefits, and the compound weight is determined by using the non-linear model and the Lagrange function. Thus, the changing rules of the order degree and the synergy degree for the subsystems in different years can be obtained. The proposed method is applied to a gate dam (named SG) to appraise the relationship among benefits. The results show that the economic, social, and ecological benefits of the SG dam from 2011 to 2018 are gradually to be a better state, but the synergy degrees of the complex system belong to “bottom synergy” and “moderate synergy” level, which indicates that there is no close cooperation among the three benefit subsystems.

Monitoring data is essential information that directly reflects the deformation and stability of slopes. However, existing research on dam slope safety monitoring mainly focuses on individual aspects, lacking overall consideration and comprehensive application, and the importance of monitoring data and its role in structural safety early warning have not been fully reflected. This study integrates data perception, detection, and security risk assessment techniques. By utilizing an online intelligent application platform, intelligent perception, detection, and application of monitoring data have been achieved. This platform adopts advanced perception technology, which can intelligently select the best measurement timing, consider meteorological and atmospheric refraction errors, obtain high-precision deformation monitoring data, identify mutation data through an improved cloud model that replaces the original data with the daily change rate of measured values, construct deformation statistical regression models and slope finite element models to achieve real-time and rapid prediction and warning of slope deformation and safety stability. Taking PB monitoring data as an example, the practical effectiveness of the platform was verified. The results show that the application platform fully meets the needs of dam slope prediction and early warning, with high data accuracy and prediction accuracy, providing necessary Technical support for intelligent monitoring and evaluation of slopes.