Dam behavior prediction is a classic problem in the monitoring of dam structure. To obtain accurate results, different researchers have established various models. However, the models of predecessors rarely studied the nonlinear characteristics of dam displacement data and the abnormal values of monitoring data. It means that abnormal values will contaminate data set, and consequently reduce the accuracy of model predictions. In this paper, an improved Random Forest (RF) model was proposed for analyzing dam displacement prediction and was coupled with a sliding time window strategy. The proposed model is developed by the following steps. First, for the purpose of alleviating the time-lag effect of impact factor phenomenon, a sliding time window strategy was introduced into the RF model to improve the time sensitivity. Second, aiming to determine the hyperparameters, Grid Search (GS) was introduced into RF model to improve the global optimization ability. This paper takes masonry arch dam in China as an example, and adopts the horizontal displacement recorded by Global Navigation Satellite Systems (GNSS) as the research object. The accuracy and validity of the proposed model are verified and evaluated based on the evaluation criteria. The simulation results demonstrate that the proposed model could capture the longterm characteristics and provide better prediction based on short-term monitoring data. It also has strong robustness on the abnormal data series, has simpler structures and less parameters, and requires less time for model training, so it can be a potential tool for actual monitoring tasks.

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

During its long service life, an arch dam affected by a combination of factors exhibits a typical time-varying characteristic in terms of its structure and material properties, and the deformation in the dam structure can directly and reliably reflect the health and service status of dams. Therefore, an accurate deformation prediction is an important part of dam safety monitoring. However, due to multiple factors, dam deformation data often tend to be highly volatile, and most existing deformation estimation techniques employ a single algorithm, which may not effectively capture the potential change process. A hybrid model for dam deformation prediction has been proposed to overcome this problem. First, dam deformation data are decomposed into three components by seasonal and trend decomposition using loess. Second, a convolutional neural network–gated recurrent unit (GRU) hybrid model, which optimizes hyperparameters using the sparrow search algorithm, is used to capture the nonlinear relationships that exist in each component. Finally, the final prediction result of dam deformation is the comprehensive output of multiple submodules. The deformation monitoring data (period: 2009–2019) of a parabolic variable-thickness double-curved arch dam located in China are considered as the survey target. The test results indicate that the proposed model is suitable for short-term and long-term prediction and outperforms other models in terms of higher robustness to abnormal sequences than other conventional models (R² differs by 5.50% and 7.87%, respectively, in short-term and long-term predictions for different measurement points, while other models differ by 9.78% to reach 15.71%, respectively). Among the models studied, the GRU shows better robustness to abnormal series than the LSTM with good prediction accuracy, fewer parameters, and a simpler structure. Hence, the GRU can be employed for dam deformation prediction in practical engineering.

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