This study proposes a new hybrid biogeography-based optimization (BBO) technique to achieve a better balance between exploitation and exploration sides of BBO. The proposed hybrid metaheuristic algorithm, namely HBBPSGWO, enhances the exploration ability of BBO by combining it with the exploration side of particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms. The proposed hybrid approach is integrated with two classical machine learning models, namely artificial neural network (ANN) and adaptive neuro-fuzzy system (ANFIS), for 1-day-ahead streamflow prediction in a catchment. Daily rainfall and discharge value from 1979 to 2015 of the catchment Fal at Tregony (United Kingdom) is used to validate the performance efficiency of the proposed hybrid algorithm, the HBBPSGWO along with ANN and ANFIS separately. The results demonstrate that the HBBPSGWO-ANN/HBBPSGWO-ANFIS improves the BBO convergence by avoid the trapping it into local minima and the performance has significantly improved compare to basic BBO-based ANN (BBO-ANN)/BBO-based ANFIS (BBO-ANFIS) and PSO-based ANN (PSO-ANN)/PSO-based ANFIS (PSO-ANFIS). In testing phase, root mean square error (RMSE) of HBBPSGWO-ANN is found lowest (0.901) compare to BBO-ANN (0.949) and PSO-ANN (0.926). Whereas in the case of integrated ANFIS, the RMSE of HBBPSGWO-ANFIS is also found minimum (0.741) compare to BBO-ANFIS (0.805) and PSO-ANFIS (0.828). The finding of this research concludes that the proposed hybrid metaheuristic algorithm has better capability to predict the daily streamflow. Moreover, the convergence of HBBPSGWO requires a smaller number of iterations to run in comparison to BBO and PSO.

This study looks to propose a hybrid soft computing approach that can be used to accurately estimate the shear strength of reinforced concrete (RC) deep beams. Support vector regression (SVR) is integrated with three novel metaheuristic optimization algorithms: African Vultures optimization algorithm (AVOA), particle swarm optimization (PSO), and Harris Hawks optimization (HHO). The proposed models, SVR-AVOA, -PSO, and -HHO, are designed and compared to reference existing models. Multi variables are used and evaluated to model and evaluate the deep beam’s shear strength, and the sensitivity of the selected variables in modeling the shear strength is assessed. The results indicate that the SVR-AVOA outperforms other proposed and existing models for the shear strength prediction. The mean absolute error of SVR-AVOA, SVR-PSO, and SVR-HHO are 43.17 kN, 44.09 kN, and 106.95 kN, respectively. The SVR-AVOA can be used as a soft computing technique to estimate the shear strength of the RC deep beam with a maximum error of ±3.39%. Furthermore, the sensitivity analysis shows that the deep beam’s key parameters (shear span to depth ratio, web reinforcement’s yield strength, concrete compressive strength, stirrups spacing, and the main longitudinal bars reinforcement ratio) are efficiently impacted in the shear strength detection of RC deep beam.

This study adopted a novel biogeography-based optimization (BBO) using an adaptive neuro-fuzzy inference system (ANFIS) technique that is BBO-ANFIS, for one day ahead runoff forecasting. Further, to check robustness of BBO-ANFIS, a comparative study has been done with two well-known hybrid models genetic algorithm-based ANFIS (GA-ANFIS) and firefly-based ANFIS (FA-ANFIS). For model development, two input features (rainfall and runoff), i.e. historical daily accumulated mean rainfall at 1 km resolution and mean daily discharge of three river catchments (specifically, river Fal at Tragony station, river Seaton at Trebrownbridge station and river Kenwyn at Truro) are considered. For the performance evaluation of models, a range of model performance indicators (correlation coefficient (r), mean absolute error (MAE), Nash–Sutcliffe efficiency coefficient) have been used. From results, it was found that the BBO-ANFIS model (r = 0.93, MAE = 0.29, NSE = 0.86 for Fal at Tragony, r = 0.96, MAE = 0.09, NSE = 0.92 for Seaton at Trebrownbridge and r = 0.93, MAE = 0.05, NSE = 0.87 for Kenwyn at Truro) has the best performance than the GA-ANFIS model (r = 91, MAE = 0.29, NSE = 0.82 for Fal at Tragony, r = 0.91, MAE = 0.10, NSE = 0.91 for Seaton at Trebrownbridge and r = 0.92, MAE = 0.06, NSE = 0.85 for Kenwyn at Truro) and the FA-ANFIS model (r = 0.91, MAE = 0.34, NSE = 0.82 for Fal at Tragony, r = 0.95, MAE = 0.10, NSE = 0.91 for Seaton at Trebrownbridge and r = 0.93, MAE = 0.06, NSE = 0.86 for Kenwyn at Truro) for rainfall-runoff (R-R) modelling, This study shows the sensitivity test of standalone machine learning model parameters using three metaheuristic techniques and identifies the best technique that can be efficiently used in hydrological modelling.