Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.

ABSTRACT: To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemente

SUBMITTER: Gul E 

PROVIDER: S-EPMC8500418 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature