An Improved Random Forest Model for the Prediction of Dam Displacement

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 article, 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 article 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 long-term 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.