A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem

Hydrological models play a crucial role in water planning and decision making. Machine Learning-based models showed several drawbacks for frequent high and a wide range of streamflow records. These models also experience problems during the training process such as over-fitting or trapping in searching for global optima To overcome these limitations, the current study attempts to hybridize the recently developed physics-inspired metaheuristic algorithms (MHAs) such as Equilibrium Optimization (EO), Henry Gases Solubility Optimization (HGSO), and Nuclear Reaction Optimization(NRO) with Multi-layer Perceptron (MLP). These models' accuracy will be inspected to solve the streamflow forecasting problem where the streamflow dataset was collected through 130 years from a station located on the High Aswan Dam (HAD). The performance of proposed models then will be compared with two traditional neural network models(MLP and RNN), and nine well-known hybrid MLP-based models belong to the different branches of the metaheuristic field (evolutionary group, swarm group, and physics group). The internal parameters of the proposed models will be initialized and optimized. Different performance metrics will be used to examine the performance of the proposed models. The stability of the proposed models and the convergence speed will be evaluated. Finally, ranking these models based on different performance evaluations will be carried out. The results show that the models in the group of Physics-MLP are more reliable in capturing the streamflow patterns, followed by the Swarm-MLP group and then by the Evolutionary-MLP group. Finally, among the all employed methods, the NRO has the best accuracy with the lowest RMSE(2.35), MAE(1.356), MAPE(16.747), and the highest WI(0.957), R(0.924), and confidence in forecasting the streamflow of Aswan High Dam. It can be concluded that augmenting the NRO algorithm with MLP can be a reliable tool in forecasting the monthly streamflow with a high level of precision, speed convergence, and high constancy level. (C) 2021 Elsevier B.V. All rights reserved.