Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization ⁠, ant colony (⁠⁠), differential evolution and genetic algorithm (⁠⁠) and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (⁠⁠), Keulegan–Carpenter number (⁠⁠) and embedded depth to diameter of pipe ratio (⁠⁠) are considered as prediction variables. Results indicate that the model (⁠ and ⁠) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination than it is due to the model structure ⁠.

波浪在管道下方的冲刷深度是一种物理上复杂的现象，其可靠的预测可能对管道设计人员构成挑战。这项研究表明自适应神经模糊推理系统（ANFIS）的应用与粒子群优化并入⁠，蚁群（⁠ ⁠），微分进化和遗传算法（⁠ ⁠），并评估在不同的冲深预测的性能和相关的不确定性冲刷条件，包括活床和清水。为此，在无量纲参数盾号（⁠ ⁠），KC数（⁠ ⁠）和嵌入式深度的管直径之比（⁠⁠）被视为预测变量。结果表明，当包括所有提到的三个无量纲输入参数时，模型（⁠和⁠）是两种冲刷条件下最准确的预测模型。此外，该模型显示出比最近开发的模型更好的预测性能。根据不确定性分析结果，冲刷深度的预测特征在于，与模型结构和输入变量组合相关的清水条件下的不确定性要比活床条件下的不确定性大。此外，活床和清水条件下冲刷深度预测的不确定性更多地是由于输入变量组合所致，而不是模型结构所致⁠。