Clear-water scour depth prediction in long channel contractions: Application of new hybrid machine learning algorithms
Clear-water scour depth prediction in long channel contractions: Application of new hybrid machine learning algorithms
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DOI：
10.1016/j.oceaneng.2021.109721
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发表时间：
2021-10
影响因子：
5
通讯作者：
K. Khosravi;M. Safari;J. Cooper
中科院分区：
文献类型：
--
作者：
K. Khosravi;M. Safari;J. Cooper
冲刷深度预测与防治是航道设计中的重要问题之一。然而，先进的机器学习（ML）算法提供冲刷深度模型的潜力还有待探索。这项研究首次量化了一系列独立和混合机器学习模型的预测能力。使用先前从实验室水槽实验中收集的冲刷深度数据，评估了最近开发的五种独立机器学习技术的性能-Isotonic回归（ISOR），顺序最小优化（SMO），迭代分类器优化器（ICO），局部加权学习（LWL）和最小二乘回归（LMS），沿着它们的混合版本与Daging（DA）和随机子空间（RS）算法。主要结论有五个方面。首先，DA-ICO模型具有最高的预测能力。其次，混合模型比独立模型具有更高的预测能力。第三，所有算法都低估了最大冲刷深度，除了DA-ICO几乎完美地预测冲刷深度。第四，冲刷深度对密度颗粒弗劳德数最敏感，其次是无量纲收缩宽度、收缩内流深、泥沙几何标准差、进场流速和中值粒径。第五，大多数算法在所有输入参数都参与模型构建时表现最好。一个重要的例外是最好的执行模型，只需要四个输入参数：密度颗粒弗劳德数，无量纲收缩宽度，收缩和沉积物的几何标准偏差内的流动深度。总的来说，结果表明，混合机器学习算法提供了更准确的预测冲刷深度比经验公式和传统的ML算法。特别是，DA-ICO模型不仅创建了最准确的预测，而且使用了最少的易于测量的输入参数。因此，这种类型的模型可能是真实的有益的执业工程师需要估计最大冲刷深度时，设计在渠道结构。
Scour depth prediction and its prevention is one of the most important issues in channel and waterway design. However the potential for advanced machine learning (ML) algorithms to provide models of scour depth has yet to be explored. This study provides the first quantification of the predictive power of a range of standalone and hybrid machine learning models. Using previously collected scour depth data from laboratory flume experiments, the performance of five types of recently developed standalone machine learning techniques - the Isotonic Regression (ISOR), Sequential Minimal Optimization (SMO), Iterative Classifier Optimizer (ICO), Locally Weighted learning (LWL) and Least Median of Squares Regression (LMS) - are assessed, along with their hybrid versions with Dagging (DA) and Random Subspace (RS) algorithms. The main findings are five-fold. First, the DA-ICO model had the highest prediction power. Second, the hybrid models had a higher prediction power than standalone models. Third, all algorithms underestimated the maximum scour depth, except DA-ICO which predicted scour depth almost perfectly. Fourth, scour depth was most sensitive to densimetric particle Froude number followed by the non-dimensionalized contraction width, flow depth within the contraction, sediment geometric standard deviation, approach flow velocity and median grain size. Fifth, most of the algorithms performed best when all the input parameters were involved in the building of the model. An important exception was the best performing model that required only four input parameters: densimetric particle Froude number, non-dimensionalized contraction width, flow depth within the contraction and sediment geometric standard deviation. Overall the results revealed that hybrid machine learning algorithms provide more accurate predictions of scour depth than empirical equations and traditional ML-algorithms. In particular, the DA-ICO model not only created the most accurate predictions but also used the fewest easily and readily measured input parameters. Thus this type of model could be of real benefit to practicing engineers required to estimate maximum scour depth when designing in-channel structures.