Sediment transport modeling has been known as an essential issue and challenging task in water resources and environmental engineering. In order to minimize the adverse impacts of the continues sediment deposition that is known as a main source of pollution in the urban area, the self-cleansing method is widely utilized for designing the sewer pipes to create a condition to keep the bottom of channel clean from sedimentation. In the present study, an extensive data range is utilized for modeling the sediment transport in non-deposition with clean bed condition. Regarding the effective parameters involved, four different scenarios are considered for the modeling. To this end, four standalone methods including the M5P, reduced error pruning tree (REPT), random forest (RF) and random tree (RT) and two hybrid models based on rotation forest (ROF) and weighted instances handler wrapper (WIHW) techniques are developed and result compared with three empirical equations. Based on the results, the hybrid WIHW-RT and WIHW-RF models provide better performance in particle Froude number estimation in comparison to other standalone and hybrid models. Performances of the most of the models are found accurate except RT and REPT standalone models. The outcomes revealed that the empirical models have considerable overestimation. Generally, hybrid data mining methods yield more precise estimations of sediment transport in contrast to the regression equations and standalone models. Particularly, both WIHW-RT and WIHW-RF models provide almost the same performances however, as WIHW-RT can better capture the extreme particle Froude number values, it slightly outperforms WIHW-RF. Promising findings of the current study may encourage the implementation of the recommended approaches in alternative hydrological problems.

在水资源和环境工程中，泥沙迁移模型已被认为是必不可少的问题和具有挑战性的任务。为了最大程度地减少被认为是城市主要污染源的持续沉积物的不利影响，自洁方法被广泛用于下水道的设计，从而创造了保持河道底部清洁的条件从沉淀。在本研究中，广泛的数据范围被用于模拟在非沉积条件下无尘床条件下的泥沙运移。关于所涉及的有效参数，考虑了四个不同的场景进行建模。为此，有四种独立方法，包括M5P，精简错误修剪树（REPT），开发了随机森林（RF）和随机树（RT）以及基于旋转森林（ROF）和加权实例处理程序包装器（WIHW）技术的两个混合模型，并与三个经验方程进行了比较。根据结果​​，与其他独立模型和混合模型相比，混合WIHW-RT和WIHW-RF模型在粒子Froude数估计中提供了更好的性能。除了RT和REPT独立模型外，大多数模型的性能均被认为是准确的。结果表明，经验模型有相当高的估计。通常，与回归方程和独立模型相比，混合数据挖掘方法可以更精确地估算沉积物的运移。尤其是WIHW-RT和WIHW-RF模型都提供几乎相同的性能，由于WIHW-RT可以更好地捕获极端粒子弗洛德数值，因此它的性能略好于WIHW-RF。当前研究的有希望的发现可能会鼓励在替代性水文问题中实施推荐的方法。