Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms
Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms
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预测稳定的砾石河床水力几何形状：新颖、先进的混合数据挖掘算法的测试
DOI：
10.1016/j.envsoft.2021.105165
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发表时间：
2021
影响因子：
4.9
通讯作者：
Khosravi K
中科院分区：
文献类型：
--
作者：
Khosravi K
冲淤平衡的稳定冲积河流水力几何形态的精确预测是河流工程领域中最困难也是最关键的课题之一。数据挖掘算法由于其高性能和灵活性，在这一领域得到了越来越多的关注。然而，缺乏对这些算法提供快速，廉价和准确的水力几何预测的潜力的理解。这项研究提供了这种潜力的第一个量化。使用在一个站点的现场数据，预测的流动深度，水面宽度和纵向水面坡度使用三个独立的数据挖掘技术-，基于实例的学习（IBK），KStar，局部加权学习（LWL）-沿着四种类型的新型混合算法，其中独立的模型是用投票，属性选择分类器（ASC），离散化回归（RBD），和交叉验证参数选择（CVPS）算法（Vote-IBK、Vote-Kstar、Vote-LWL、ASC-IBK、ASC-Kstar、ASC-LWL、RBD-IBK、RBD-Kstar、RBD-LWL、CVPS-IBK、CVPS-Kstar、CVPS-LWL）。通过比较它们的预测性能和驱动变量的敏感性分析，结果表明：（1）屏蔽应力是预测所有几何尺寸的最有效参数;（2）混合模型比独立的数据挖掘模型、经验方程和传统的机器学习算法具有更高的预测能力;（3）Vote-Kstar模型对深度和宽度的预测效果最好，ASC-Kstar模型对坡度的预测效果最好。通过这些算法，任何河流的水力几何形状都可以准确地预测，并且只需使用几个容易获得的流量和通道参数。因此，结果表明，这些模型具有很大的潜力，用于稳定的渠道设计中的数据贫困集水区，特别是在发展中国家的技术建模技能和理解的水力和泥沙过程中发生的河流系统可能缺乏。
Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) - along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-Kstar, Vote-LWL, ASC-IBK, ASC-Kstar, ASC-LWL, RBD-IBK, RBD-Kstar, RBD-LWL, CVPS-IBK, CVPS-Kstar, CVPS-LWL). Through a comparison of their predictive performance and a sensitivity analysis of the driving variables, the results reveal: (1) Shield stress was the most effective parameter in the prediction of all geometry dimensions; (2) hybrid models had a higher prediction power than standalone data mining models, empirical equations and traditional machine learning algorithms; (3) Vote-Kstar model had the highest performance in predicting depth and width, and ASC-Kstar in estimating slope, each providing very good prediction performance. Through these algorithms, the hydraulic geometry of any river can potentially be predicted accurately and with ease using just a few, readily available flow and channel parameters. Thus, the results reveal that these models have great potential for use in stable channel design in data poor catchments, especially in developing nations where technical modelling skills and understanding of the hydraulic and sediment processes occurring in the river system may be lacking.