Ensemble of bagged regression trees for concrete dam deformation predicting

Abstract

Tree based ensemble learning algorithms involve generating multiple trees that together produce a consistent prediction and often significantly improving the accuracy of the prediction. In this paper, the technique is further explored, and a dam displacement prediction model based on bootstrap aggregating (bagged) regression trees (BART) is proposed. The case study of concrete dam indicates that the performance of bagged regression trees is better than the conventional regression tree (RT) model in predicting dam displacement. Moreover, the model can also analyze the importance of each variable, which has strong practical value.