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.

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Water stored in reservoirs has a lot of crucial function, including generating hydropower, supporting water supply, and relieving lasting droughts. During floods, water deliveries from reservoirs must be acceptable, so as to guarantee that the gross volume of water is at a safe level and any release from reservoirs will not trigger flooding downstream. This study aims to develop a well-versed assessment method for managing reservoirs and pre-releasing water outflows by using the machine learning technology. As a new and exciting AI area, this technology is regarded as the most valuable, time-saving, supervised and cost-effective approach. In this study, two data-driven forecasting models, i.e., Regression Tree (RT) and Support Vector Machine (SVM), were employed for approximately 30 years’ hydrological records, so as to simulate reservoir outflows. The SVM and RT models were applied to the data, accurately predicting the fluctuations in the water outflows of a Bhakra reservoir. Different input combinations were used to determine the most effective release. For cross-validation, the number of folds varied. It is found that quadratic SVM for 10 folds with seven different parameters would give the minimum RMSE, maximum R2, and minimum MAE; therefore, it can be considered as the best model for the dataset used in this study.