A Hybrid Model for Predicting the Energy Dissipation on the Block Ramp Hydraulic Structures

Block ramps are among the environmentally friendly hydraulic structures used for energy dissipation in rivers and waterways. Modeling the energy dissipation on these structures is ever-challenging in hydraulic engineering. The primary goal of the current study is to propose a novel metaheuristic-based artificial intelligence (AI) framework for energy dissipation prediction on block ramp structures. An improved African Vultures Optimization Algorithm (AVOA) is used to optimize the Adaptive Neuro-Fuzzy Inference System (ANFIS) in this investigation for accurate prediction of the energy dissipation on the block ramps. The performance of the hybrid ANFIS-IAVOA model is compared with an ANFIS and its hybrid versions using original AVOA, honey badger algorithm (ANFIS-HBA), grey wolf optimizer (ANFIS-GWO), monarch butterfly optimization (ANFIS-MBO), and black widow optimization (ANFIS-BWO) models. A dataset of 210 experiments measured at Shahid Chamran University of Ahvaz and 241 experiments collected from literature are used to construct the proposed hybrid models. The results demonstrate the better efficiency of hybrid ANFIS-IAVOA with RMSE of 0.018–0.020 and R² of 0.98–0.98 compared to ANFIS-AVOA (RMSE ~ 0.023–0.25 and R² ~ 0.97–0.97), ANFIS-HBA (RMSE ~ 0.021–0.025 and R² ~ 0.97–0.97), ANFIS-MBO (RMSE ~ 0.022–0.023 and R² ~ 0.97–0.97), ANFIS-GWO (RMSE ~ 0.022–0.024 and R² ~ 0.97–0.97), ANFIS-BWO (RMSE ~ 0.027–0.028 and R² ~ 0.96–0.96), and ANFIS (RMSE ~ 0.029–0.033 and R² ~ 0.954 − 0.951). The statistical measures show that the proposed ANFIS-IAVOA performs better than the other metaheuristic-based and standalone ANFIS-developed models. The impressiveness of the proposed hybrid model demonstrates that it can be used for further investigations on the probabilistic assessment of the block ramp hydraulic structures.