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首页> 外文期刊>Journal of Hydroinformatics >Determination of compound channel apparent shear stress: application of novel data mining models
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Determination of compound channel apparent shear stress: application of novel data mining models

机译：复合通道表观剪切应力的测定：新型数据挖掘模型的应用

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摘要

Momentum exchange in the mixing region between the floodplain and the main channel is an essential hydraulic process, particularly for the estimation of discharge. The current study investigated various data mining models to estimate apparent shear stress in a symmetric compound channel with smooth and rough floodplains. The applied predictive models include random forest (RF), random tree (RT), reduced error pruning tree (REPT), M5P, and the distinguished hybrid bagging-M5P model. The models are constructed based on several correlated physical channel characteristic variables to predict the apparent shear stress. A sensitivity analysis is applied to select the best function tuning parameters for each model. Results showed that input with six variables exhibited the best prediction results for RF model while input with four variables produced the best performance for other models. Based on the optimised input variables for each model, the efficiency of five predictive models discussed here was evaluated. It was found that the M5P and hybrid bagging-M5P models with the coefficient of determination (R-2) equal to 0.905 and 0.92, respectively, in the testing stage are superior in estimating apparent shear stress in compound channels than other RF, RT and REPT models.
机译：泛洪叶和主通道之间的混合区域中的动量交换是必需的液压过程，特别是用于放电的估计。目前的研究调查了各种数据挖掘模型，以估计具有光滑和粗糙的洪泛平坦的对称化合物通道中的表观剪切应力。所应用的预测模型包括随机森林（RF），随机树（RT），减少误差修剪树（REPT），M5P和可分辨的混合袋-M5P模型。基于几个相关的物理信道特性变量来构造模型，以预测表观剪切应力。应用灵敏度分析来为每个模型选择最佳函数调整参数。结果表明，具有六个变量的输入表现出RF模型的最佳预测结果，同时输入四个变量的输入为其他模型产生了最佳性能。基于每个模型的优化输入变量，评估此处讨论的五种预测模型的效率。发现M5P和杂交袋M5P模型分别测定系数（R-2）等于0.905和0.92，在测试阶段的估计比其他RF，RT和RT和其他rF和所述复合通道中的表观剪切应力优异Rept模型。

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