Weighted instances handler wrapper and rotation forest-based hybrid algorithms for sediment transport modeling

Abstract

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.