Prediction of the scour depth around non-uniformly spaced pile groups (PGs) is one of the most complex problems is hydraulic engineering. Different types of empirical methods have been developed for estimating the scour depth around the PGs. However, the majority of the existing methods are based on simple regression methods and have serious limitations in modelling the highly nonlinear and complex relationships between the scour depth and its influential variables, especially for the non-uniformly spaced pile. Hence, this study combines prediction powers of tree popular machine learning (ML) methods, namely, Gaussian process regression (GPR), random forest (RF), and M5 model tree (M5Tree) using novel Least Least-squares (LS) Boosting Ensemble committee-based data intelligent technique to more accurately estimate local scour depth around non-uniformly spaced pile groups. A total of 353 laboratory experiments data were compiled from published papers. non-dimensional results obtained demonstrated that the ensemble model can more accurately estimate the scour depth than the individual predictions of the GPR, RF, and M5Tree models. The proposed Ensemble model with correlation coefficient (R), root mean square error (RMSE) and mean absolute percentage of error (MAPE) of 0.972, 0.0153 m, and 10.89%, respectively, significantly outperformed all existing empirical models. Furthermore, the sensitivity analysis demonstrated that the pile diameter is the most influential variable in estimating the scour depth.

水力工程是预测非均匀间距桩组（PG）周围冲刷深度的最复杂问题之一。已经开发出不同类型的经验方法来估计PG周围的冲刷深度。但是，大多数现有方法基于简单的回归方法，并且在模拟冲刷深度与其影响变量之间的高度非线性和复杂关系时存在严重局限性，尤其是对于非均匀间距桩而言。因此，这项研究结合了树型流行机器学习（ML）方法的预测能力，即高斯过程回归（GPR），随机森林（RF），和M5模型树（M5Tree），使用基于最小二乘（LS）Boosting Ensemble委员会的新型数据智能技术，可以更准确地估算非均匀间距桩组周围的局部冲刷深度。从已发表的论文中总共收集了353个实验室实验数据。获得的无量纲结果表明，与GPR，RF和M5Tree模型的单个预测相比，集成模型可以更准确地估计冲刷深度。所提出的Ensemble模型的相关系数（R），均方根误差（RMSE）和平均绝对误差百分比（MAPE）分别为0.972、0.0153 m和10.89％，明显优于所有现有的经验模型。此外，敏感性分析表明，在估算冲刷深度时，桩直径是影响最大的变量。