Abstract The estimation of drag exerted by vegetation is of great interest because of its importance in assessing the impact of vegetation on the hydrodynamic processes in aquatic environments. In the current research, genetic programming (GP), a machine learning (ML) technique based on natural selection, was adopted to search for a robust relationship between the bulk drag coefficient (Cd) for arrays of rigid circular cylinders representing emergent vegetation with blockage ratio (ψ), vegetation density (λ) and pore Reynolds number (Rep) based on published data. We utilize a data set covering a wide range of each parameter involved to cover all possible dependencies. A new predictor, which shares the same form with the Ergun-derived formula, was obtained without any pre-specified forms before searching. The dependence of the two parameters in Ergun equation on vegetation characteristics was also estimated by GP. This new Cd predictor for emergent vegetation with a relatively concise form exhibits a considerable improvement in terms of prediction ability relative to existing predictors.

中文翻译：

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基于遗传规划的明渠流中挺水植被阻力系数模型

摘要 植被所施加的阻力的估计在评估植被对水生环境中的水动力过程的影响方面具有重要意义。在当前的研究中，遗传编程 (GP)，一种基于自然选择的机器学习 (ML) 技术，被用来寻找代表具有阻塞的新兴植被的刚性圆柱体阵列的体积阻力系数 (Cd) 之间的稳健关系比率 (ψ)、植被密度 (λ) 和孔隙雷诺数 (Rep) 基于已发布的数据。我们利用一个数据集，涵盖了所涉及的每个参数的广泛范围，以涵盖所有可能的依赖关系。在搜索之前没有任何预先指定的形式，获得了一个新的预测器，它与 Ergun 派生的公式具有相同的形式。Ergun方程中的两个参数对植被特征的依赖性也由GP估计。这种形式相对简洁的新兴植被 Cd 预测器在预测能力方面相对于现有预测器有相当大的改进。