Scour around bridge piers is a complex phenomenon and it is essential to assess or predict the scour hazard around bridge piers in tandem with completely understanding its mechanism. To date, there is no exact method for the estimation of scour depth. Nowadays, machine learning techniques are being recognized as effective tools for the prediction of scour depth using experimental data. In the present study, gradient tree boosting (GTB) technique was used for the prediction of scour depth around various pier shapes under different streambed conditions. Sediment size, sediment quantity, velocity, and flow time were used as input parameters to predict the scour depth under clear-water and live-bed scour conditions. The scour depth was predicted for different pier shapes such as, circular, rectangular, round-nosed and sharp-nosed shaped. The GTB model predicted scour depth values were compared with that of the group method of data handling (GMDH) technique. The performance of GTB and GMDH models were then evaluated based on statistical indices such as RRMSE, NNSE, WI, MNE, SI, and KGE. The study concludes that the GTB model performance was relatively superior to that of GMDH in the prediction of scour depth around different pier shapes.

桥墩周围的冲刷是一个复杂的现象，在完全了解其机理的同时，对桥墩周围的冲刷危害进行评估或预测至关重要。迄今为止，还没有准确的方法来估计冲刷深度。如今，机器学习技术被认为是使用实验数据预测冲刷深度的有效工具。在本研究中，梯度树提升（GTB）技术被用于预测不同河床条件下各种桥墩周围的冲刷深度。沉积物大小、沉积物数量、速度和流动时间被用作输入参数来预测清水和活床冲刷条件下的冲刷深度。预测冲刷深度适用于不同的墩形状，例如圆形、矩形、圆头和尖头形状。将 GTB 模型预测的冲刷深度值与组数据处理方法 (GMDH) 技术的值进行比较。然后根据 RRMSE、NNSE、WI、MNE、SI 和 KGE 等统计指标评估 GTB 和 GMDH 模型的性能。研究得出结论，GTB模型在预测不同桥墩形状周围冲刷深度方面的性能相对优于GMDH。