A Novel Efficient Method of Estimating Suspended-To-Total Sediment Load Fraction in Natural Rivers

Sediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended-to-total load fraction using dimensionless hydro-morphological variables. Two prominent variable combinations were identified using the recursive feature elimination for support vector regression (SVR): (a) width-to-depth ratio, dimensionless particle size, flow Reynolds number, densimetric Froude number, and falling particle Reynolds number, and (b) flow Reynolds number, Froude number, and densimetric Froude number. The explicit relations between the suspended-to-total load fraction and the two combinations were revealed by two modern symbolic regression methods: multi-gene genetic programming and Operon. The five-variable SVR model showed the best performance. Clustering analyses using a self-organizing map and Gaussian mixture model, respectively, identified the underlying relationships between dimensionless variables. Subsequently, the one-at-a-time sensitivity of the input variables of the empirical models was investigated. The suspended-to-total load fraction is positively related to the flow Reynolds number and is inversely related to the densimetric Froude number. The models developed in this study are practical and easy to implement in other suspended sediment monitoring methods because they only require basic measurable hydro-morphological variables, such as velocity, depth, width, and median bed material size.

Automated summary

Sediment transport load monitoring is important in civil and environmental engineering. This study proposes estimation models for the suspended-to-total load fraction using dimensionless hydro-morphological variables. The models were developed through feature elimination and symbolic regression methods. Clustering analysis and sensitivity analysis were also conducted for a deeper understanding of variable relationships.