ABSTRACT

Accurate prediction of the scour hole depth and dimensions downstream of ski-jump spillways has been an important issue among hydraulic researchers for decades. In recent years, computing methods such as Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs) and Support Vector Regression (SVR) have shown a powerful performance in the prediction of scour characteristics owing to their flexibility and learning nature. In the present paper, a new hybrid approach has been proposed for the first time in order to improve the estimation power of the SVR tool for scour hole geometry prediction below ski-jump spillways. The principal characteristics of the scour hole pattern in the equilibrium phase have been predicted using SVR optimized with Fruitfly Optimization Algorithms (FOAs). The hybrid model is compared with the corresponding simple SVR model. To evaluate the proposed hybrid model further, it is also compared with other machine learning and empirical methods, such as ANNs, ANFISs and regression equations. The results show that the proposed SVR-FOA method performs well, improves remarkably on Support Vector Machines (SVMs) results, estimates scour hole geometrical parameters more accurately than the simple SVR model, and can be applied as an alternative reliable scheme for estimations on which simple SVR and other methods demonstrate shortcomings. The proposed hybrid method improves the precision level for scour depth prediction by about 8% compared with simple SVM in terms of the correlation coefficient.

摘要

数十年来，准确预测滑坡溢洪道下游冲孔深度和尺寸一直是水力研究人员的重要课题。近年来，由于其灵活性和学习性，诸如人工神经网络（ANN），自适应神经模糊推理系统（ANFIS）和支持向量回归（SVR）之类的计算方法在预测冲刷特性方面表现出强大的性能。在本文中，首次提出了一种新的混合方法，以提高SVR工具对跳台溢洪道下面冲刷孔几何形状预测的估计能力。已使用通过Fruitfly优化算法（FOA）优化的SVR预测了平衡阶段冲孔模式的主要特征。将混合模型与相应的简单SVR模型进行比较。为了进一步评估提出的混合模型，还将其与其他机器学习和经验方法（例如ANN，ANFIS和回归方程）进行比较。结果表明，所提出的SVR-FOA方法性能良好，对支持向量机（SVM）的结果有显着改善，比简单的SVR模型更准确地估计冲孔几何参数，并可作为替代可靠方案进行估计简单的SVR和其他方法显示出缺点。与简单的SVM相比，所提出的混合方法在相关系数方面将冲深预测的精度提高了约8％。还将其与其他机器学习和经验方法（例如ANN，ANFIS和回归方程）进行比较。结果表明，所提出的SVR-FOA方法性能良好，对支持向量机（SVM）的结果有显着改善，比简单的SVR模型更准确地估计冲孔几何参数，并可作为替代可靠方案进行估计简单的SVR和其他方法显示出缺点。与简单的SVM相比，所提出的混合方法在相关系数方面将冲深预测的精度提高了约8％。还将其与其他机器学习和经验方法（例如ANN，ANFIS和回归方程）进行比较。结果表明，所提出的SVR-FOA方法性能良好，对支持向量机（SVM）的结果有显着改善，比简单的SVR模型更准确地估计冲孔几何参数，并可作为一种可靠的替代方案进行估计简单的SVR和其他方法显示出缺点。与简单的SVM相比，所提出的混合方法在相关系数方面将冲深预测的精度提高了约8％。估计冲孔几何参数比简单的SVR模型更准确，并且可以用作替代可靠方案来估计简单SVR和其他方法存在的缺点。与简单的SVM相比，所提出的混合方法在相关系数方面将冲深预测的精度提高了约8％。估计冲孔几何参数比简单的SVR模型更准确，并且可以用作替代可靠方案来估计简单SVR和其他方法存在的缺点。与简单的SVM相比，所提出的混合方法在相关系数方面将冲深预测的精度提高了约8％。