Application of machine learning approaches in the computation of energy dissipation over rectangular stepped spillway

Abstract

The stepped spillway of a dam is a crucial element that serves multiple purposes in the field of river engineering. Research related to flood control necessitates an investigation into the dissipation of energy over stepped spillways. Previous research has been conducted on stepped spillways in the absence of baffles, utilizing diverse methodologies. This study employs machine learning techniques, specifically support vector machine (SVM) and regression tree (RT), to assess the energy dissipation of rectangular stepped spillways incorporating baffles arranged in different configurations and operating at varying channel slopes. Empirical evidence suggests that energy dissipation is more pronounced in channels with flat slopes and increases proportionally with the quantity of baffles present. Statistical measures are employed to validate the constructed models in the experimental investigation, with the aim of evaluating the efficacy and performance of the proposed model. The findings indicate that the SVM model proposed in this study accurately forecasted the energy dissipation, in contrast to both RT and the conventional method. This study confirms the applicability of machine learning techniques in the relevant field. Notably, it provides a unique contribution by predicting energy dissipation in stepped spillways with baffle configurations. HIGHLIGHTS The investigation of flood management necessitates a thorough examination of the dissipation of energy across stepped spillways.; The current research endeavours to examine the dissipation of energy across a rectangular steeped spillway featuring diverse baffle configurations at varying channel inclinations.; Machine learning techniques, specifically support vector machines (SVM) and regression trees (RT), are employed for the purpose of forecasting energy dissipation on steeped spillways with rectangular geometry.;

Keywords

• baffle configurations
• channel bottom slope
• rectangular stepped spillways
• regression tree (rt)
• support vector machines (svm)