The scouring phenomenon is one of the major problems experienced in hydraulic engineering. In this study, an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches, including the ant colony optimization, genetic algorithm, teaching-learning-based optimization, biogeographical-based optimization, and invasive weed optimization for estimating the long contraction scour depth. The proposed hybrid models are built using non-dimensional information collected from previous studies. The proposed hybrid intelligent models are evaluated using several statistical performance metrics and graphical presentations. Besides, the uncertainty of models, variables, and data are inspected. Based on the achieved modeling results, adaptive neuro-fuzzy inference system-biogeographic based optimization (ANFIS-BBO) provides superior prediction accuracy compared to others, with a maximum correlation coefficient (R_test = 0.923) and minimum root mean square error value (RMSE_test = 0.0193). Thus, the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring, thus, contributing to the base knowledge of hydraulic structure sustainability.

冲刷现象是水利工程中遇到的主要问题之一。在这项研究中，自适应神经模糊推理系统与几种进化方法相结合，包括蚁群优化、遗传算法、基于教学的优化、基于生物地理学的优化和侵入性杂草优化，用于估计长收缩冲刷深度. 所提出的混合模型是使用从以前的研究中收集的无量纲信息构建的。使用几种统计性能指标和图形表示来评估所提出的混合智能模型。此外，还检查了模型、变量和数据的不确定性。基于获得的建模结果，R_检验= 0.923）和最小均方根误差值（RMSE_检验= 0.0193）。因此，所提出的 ANFIS-BBO 是一种预测长收缩冲刷的有效且具有成本效益的方法，从而有助于了解水工结构可持续性的基础知识。