Clear Water Scour Depth Prediction using Gradient Boosting Machine and Deep Learning

Abstract

The scouring process in adjacent to spur dikes has the potential for compromising the stability of riverbanks. Hence, it is necessary for river engineering to conduct precise measurement of maximum scour depth in the vicinity of spur dikes. Nevertheless, the determination of the maximum scour depth has proven to be a challenging task, primarily due to the complex nature of the scour phenomena associated with these structures. In this study, two data-driven models, namely the Gradient Boost Machine (GBM) and Deep Learning (DL), were developed to predict the clear water scour depth near to a spur dike. A total of 154 distinct observations have been collected from previous literatures. A total of 103 observations were utilized for training the model, while 53 observation were allocated for validation purposes. Several performance assessment measures were employed to evaluate the performance of the models, including the correlation coefficient (CC), root-coefficient of determination (R2), scattered plot, variation plot, and box plot. GBM outperformed the DL on the basis of above-mentioned assessment measures. Sensitivity analysis suggests that l/d50 is the most influences input parameter. Thus, the conclusion suggested that both the data-driven model can be used in the prediction of the clear water scour depth around spur dikes but GBM have highest accuracy.