变形是评价大坝是否安全的重要指标之一。随着变形监测测点的不断增加,实现对所有测点的分析意味着消耗大量时间,往往会出现预报不及时的问题;另一方面,传统机器学习算法的引入虽然提高了预测精度,但参数选取不佳时对结果影响很大且建模过程十分复杂。引入模糊C-均值聚类(FCM)和极端梯度提升算法(XGBoost),首先对大坝的变形测点根据变化规律的相似性进行分区,然后针对每个分区建立XGBoost变形预测模型。以拱坝垂线径向变形监测资料为例,验证了聚类结果的可靠性,并将XGBoost变形预测模型结果与随机森林模型结果对比。结果表明,XGBoost模型在数据预处理、建模时间及预测精度上,都体现出更大的优势。

Deformation is a crucial indicator to evaluate the safety of dam. With the increase in the number of deformation measuring points, however, prediction often lags because analyzing all the measuring points is time costly. Moreover, despite that traditional machine learning algorithms have ameliorated prediction accuracy, unreasonable selection of parameters has a great impact on prediction results and the process of establishing model is extremely complicated. In view of this, we introduced the fuzzy C-means clustering (FCM) and eXtreme Gradient Boosting algorithm (XGBoost) to partition the deformation measuring points according to the similarity of the change rules, and then established XGBoost prediction model for each partition. Taking the perpendicular line deformation monitoring data of the arch dam as an example, we verified the reliability of the clustering results and compared the XGBoost result with that of random forest prediction model. Result suggest superiority of the XGBoost prediction model in data pretreatment, modeling time, and prediction accuracy.