In this study, data-driven methods (DDMs) including different kinds of group method of data handling (GMDH) hybrid models with particle swarm optimization (PSO) and Henry gas solubility optimization (HGSO) methods, and simple equations methods were applied to simulate the maximum hydro-suction dredging depth (h_s). Sixty-seven experiments were conducted under different hydraulic conditions to measure the h_s. Also, 33 data samples from three previous studies were used. The model input variables consisted of pipeline diameter (d), the distance between the pipe inlet and sediment level (Z), the velocity of flow passing through the pipeline (u₀), the water head (H), and the medium size of particles (D₅₀). Data-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better than the PSO algorithm, whereas the PSO algorithm trained simple simulation equations more precisely. Among all used DDMs, the integrative GMDH-HGSO algorithm provided the highest accuracy (RMSE = 7.086 mm). The results also showed that the integrative GMDHs enhance the accuracy of polynomial GMDHs by ∼14.65% (based on the RMSE).

在本研究中，数据驱动方法 (DDM) 包括不同种类的数据处理组方法 (GMDH) 混合模型与粒子群优化 (PSO) 和亨利气体溶解度优化 (HGSO) 方法，并应用简单方程方法来模拟最大水吸挖泥深度（h _s）。在不同的水力条件下进行了 67 次实验来测量h _s。此外，还使用了来自之前三项研究的 33 个数据样本。模型输入变量包括管道直径（d）、管道入口与沉积物液位之间的距离（Z）、通过管道的流速（u ₀）、水头（H )，以及中等大小的颗粒 ( D ₅₀ )。数据驱动的仿真结果表明，HGSO 算法比 PSO 算法更准确地训练 GMDH 方法，而 PSO 算法更精确地训练简单的仿真方程。在所有使用的 DDM 中，综合 GMDH-HGSO 算法提供了最高的精度 ( RMSE = 7.086 mm)。结果还表明，综合 GMDH 将多项式 GMDH 的准确性提高了约 14.65%（基于RMSE）。