Author(s): Shanshan Deng; Junqiang Xia; Yueyao Zhou
Linked Author(s): Junqiang Xia
Keywords: Bank erosion; Early-warning; Prediction; Middle Yangtze River
Abstract: Bank erosion can cause serious damages to flood control infrastructures in alluvial rivers, and thus threaten the safety of riparian residents and industry in heavily populated river systems, such as the Middle Yangtze River (MYR), China. The current study proposes a novel framework for the prediction and early-warning of bank erosion. The prediction of bank erosion is implemented through coupling a dynamic model with a data-driven random forest model. For the determination of early-warning level, four indices are proposed corresponding to bank erosion intensity and dangerous degree, respectively. The four indices are combined into a final early-warning level of bank erosion. The proposed framework is applied to the MYR, with its performance being evaluated by the corresponding flow, sediment and topographic measurements. The results show that: (1) the dynamic model reproduces the flow and sediment transport processes well in the MYR, with relative errors being less than 5%, 30% and 6% for the water discharge, sediment discharge and river stage, respectively. The model is also able to capture the major bank erosion sites well; (2) The performance of the data-driven model is obviously increased when the input data groups are balanced, with a recall rate of 0.67 and an accuracy of 0.80 being obtained; (3) the calculated distributions of early-warning sites are generally in accordance with the observations in 2020, and the dangerous areas locate close to the outlet of the Jingjiang Reach of MYR. Besides, model ensemble is probably a better way to improve the prediction accuracy of bank erosion, as compared with solely relying on the refinement of dynamic model. However, some major gaps are also identified for the current framework.
Year: 2024