Analysis of characteristic index and prediction of river bottom tearing scour in the Yellow River

Abstract

River bottom tearing scour (RBTS) has a strong effect on the scouring and moulding of channel in the Yellow River. Due to the special forming conditions, complex influencing factors, and limited observed data, it is difficult to predict whether RBTS will occur accurately. By collecting and disposing of the hydrodynamic, sediment, and initial boundary data of 246 flood events related to RBTS in three typical reaches of the Yellow River basin, the correlation between different characteristic influencing factors and the occurrence and absence of RBTS were analysed, and prediction models based on machine learning algorithms were constructed. The results showed that under the existing data conditions, the maximum sediment concentration Sm, average sediment concentration Sp, flood growth rate ν, and shape coefficient δ were the four key indices to more easily distinguish whether RBTS will occur. The support vector machine algorithm model had the best performance results and exhibited higher accuracy and precision in predicting its occurrence compared with other models under given water and sediment conditions. The method proposed in this study provides a new method for accurately predicting RBTS in the Yellow River. HIGHLIGHTS The maximum sediment concentration, average sediment concentration, flood growth rate, and shape coefficient are the four key indices for distinguishing whether RBTS will occur.; Prediction models of RBTS based on machine learning algorithms were built.; The SVM model showed the best predictive performance in the case study of the middle reach of the Yellow River.;

Keywords

• characteristic index
• machine learning
• prediction model
• river bottom tearing scour