Roughness Inversion of Water Transfer Channels from a Data-Driven Perspective

Abstract

Manning’s roughness coefficient (nc) is an important parameter characterizing the flow capacity of water transfer channels, and it is also an important and sensitive parameter in one-dimensional (1D) flow simulation. This study focused on the roughness inversion for datasets with different sequence lengths, time steps and anomalous data points. A case study was performed with the datasets of the Shandong Jiaodong Water Transfer Project under steady-state conditions. For sequence lengths, the datasets of 6, 12, 24, 40, 88, and 142 h were selected, all with a time step of 1 min. Subsequently, the time step was changed to 5, 10, 15, 30, 60, and 120 min for the 40 h dataset mentioned above. Finally, the flow data point under a certain moment was selected and changed by 10%, 20%, 30%, and 40% respectively. The results show that there is a quadratic relationship between the nc value and the objective function value and the optimal nc value is nc=−b/2a. It is recommended that the nc value retains four decimal places and is inverted using high-frequency and cleaned datasets.

Keywords

• data-driven
• inverse characterization
• roughness
• water transfer channels