中文摘要：

大坝变形预报对大坝运行安全评估起着至关重要的作用。传统模型预报精度不够、模拟效果不稳定;若大坝变形数据有异常值时,传统机器算法模型识别和处理异常值的灵活性很小,导致预报结果有偏差。为了解决这些问题,首次将随机森林算法运用到大坝变形监测领域,将大坝测点根据随机森林相似性矩阵分成若干个子集,针对每一个子集建立随机森林预测模型,分区建立预测模型更符合工程实际情况。选取拱坝变形作为研究对象,验证所建模型的适用性。结果表明,根据随机森林的相似性矩阵对大坝各测点的分区情况符合物理和工程实际意义,对各分区子集测点利用随机森林模型建立的预测模型,与支持向量机、BP神经网络模型相比,预测结果精度较高、模型稳定性好,为大坝变形监测提供了新思路。

英文摘要：

Dam deformation prediction plays an important role in the safety assessment of dam operation.Traditional models lack forecasting precision and the simulation effect is not stable enough.Besides,if abnormal values of dam deformation exist,traditional machine algorithm model lacks the flexibility of dealing with these abnormal data,which will lead to the deviation of the forecasting results.In order to solve these problems,random forest algorithm was introduced to the field of dam deformation monitoring for the first time.Similarity matrix of random forest was applied to divide dam deformation monitoring points into several parts.Random forests prediction model was established for each part,which will avoid the defects of traditional models such as modeling of single point or using the same model for all deformation monitoring points.Establishing forecasting model for different parts of dam was more in line with engineering practice.Deformation data of one arch dam was analyzed and the feasibility of random forest model was verified.The results showed that partition of dam deformation points based on similarity matrix of random forest conformed to the physical and engineering practical significance.Compared with support vector machine and BP neural network model,the prediction model of random forests for each part had the higher prediction precision and stability,which provided a new approach in the area of dam safety monitoring.