A mobile bed 1D numerical model to support Red River (VietNam) basin management

Bernardi, Dario; Schippa, Leonardo

doi:10.1201/b17133-229

Cited by 2 publications

(1 citation statement)

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“…Therefore, for the design, operation, and management of ecofriendly and sustainable rivers, it is instrumental to have a sufficient understanding of the discharge of inflow and sediment transport and quantitatively identify and predict the sediment yield [6,7]. To solve erosion and sediment related problems, various models have been proposed for the estimation of the sediment yield or sediment transport rate [8][9][10]. Especially, numerous mathematical formulas based on statistical methods have focused on sediment yield quantification [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Validation of Estimated Sediment Yield and Transport Model Developed with Model Tree Technique

2022

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This study evaluated the applicability of existing sediment yield and transport estimation models developed using data mining classification and prediction techniques and validated them. Field surveys were conducted by using an acoustic Doppler current profiler and laser in situ scattering and transmission at measuring points in the main stream of the Nakdong River located where the tributaries of the Geumho, Hwang, and Nam Rivers join. Surveys yielded estimations of water velocity, discharge, and suspended sediment concentrations were measured. In contrast with models based on the general watershed characteristics factors, some models based on hydraulic explanatory flow variables demonstrated an excellent predictability. This is because the selected submodels for validation, which provided excellent prediction results, were based on a large number of calibration data. It indicates that a sufficient number of reliable data is required in developing a sediment yield estimation model using data mining. For practical applications of data mining to extant sediment yield estimation models, comprehensive considerations are required, including the purpose and background of model development, and data range. Furthermore, the existing models should be periodically updated with the consideration of temporal and spatial lumping problems.

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