Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning based optimization (TLBO) are proposed to predict the maximum depth of scouring based on the different input combinations. Several performance indices and graphical evaluators are employed to estimate the prediction accuracy in the training and testing phase. Results show that the ANFIS-IWO offers the highest prediction performance (RMSE = 0.148) compared to other models in the testing phase, while the ANFIS-BBO (RMSE = 0.411)ANFIS-TLBO-M3 RMSEtesting=0.411, CCtesting~0.00) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability R-factor=1.72has a higher impact on the predicted results than the structure of models. In general, the ANFIS-IWO can be used as a reliable and cost-effective method for predicting the scouring depth downstream of weirs.

中文翻译：

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使用混合智能模型评估堰下游的冲刷深度

考虑到堰下游的冲刷深度是一个具有挑战性的问题，因为它会影响堰的稳定性。提出了自适应神经模糊推理系统（ANFIS）模型，结合优化方法，即文化算法、基于生物地理学的优化（BBO）、侵入性杂草优化（IWO）和基于教学学习的优化（TLBO）来预测基于冲刷的最大深度。在不同的输入组合上。几个性能指标和图形评估器被用来估计训练和测试阶段的预测精度。结果表明，与测试阶段的其他模型相比，ANFIS-IWO 提供了最高的预测性能（RMSE = 0.148），而 ANFIS-BBO（RMSE = 0.411）ANFIS-TLBO-M3 RMSEtesting=0.411，CCtesting~0.00）提供了最低准确率。预测建模不确定性分析的结果表明，输入变量变异性R-factor=1.72对预测结果的影响大于模型结构。一般而言，ANFIS-IWO 可用作预测堰下游冲刷深度的可靠且具有成本效益的方法。