This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine–bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.

本研究旨在使用不同的智能预测模型估计弯曲迷宫的溢出能力，即自适应神经模糊推理系统、支持向量机、M5 模型树、最小二乘支持向量机和最小二乘支持向量机-蝙蝠算法（LSSVM-BA）。从迷宫溢流模型的实验室研究结果中提取了 6 个不同国会溢流模型的 355 个经验数据。利用上游水头溢流比、侧壁角和曲率角等参数估计曲径迷宫溢流的流量系数。基于各种统计评价指标，结果表明，这些输入参数可以用来预测流量系数。具体来说，LSSVM-BA 模型在训练和测试阶段表现出最佳的预测精度。这种低成本的预测模型可能具有显着的实际意义，因为它可能是昂贵且耗时的昂贵实验室解决方案的经济替代方案。