Daily scale river flow simulation: hybridized fuzzy logic model with metaheuristic algorithms

Abstract

Novel data-intelligence models developed through hybridization of an adaptive neuro-fuzzy inference system (ANFIS) with different metaheuristic algorithms, namely grey wolf optimizer (GWO), particle swarm optimizer (PSO) and whale optimization algorithm (WOA), are proposed for daily river flow prediction of the Taleghan River, which is the major source of potable water for Tehran, the capital of Iran. Gamma test (GT) was used for the determination of input variables for the models. The ANFIS-WOA model was found to exhibit the best performance in prediction of river flow according to root mean square error (RMSE ≈ 3.75 m3.s−1) and Nash-Sutcliffe efficiency (NSE ≈ 0.93). It improved the prediction performance of the classical ANFIS model by 6.5%. The convergence speed of ANFIS-WOA was also found to be higher compared to other hybrid models. The success of the ANFIS-WOA model indicates its potential for use in the simulation of highly nonlinear daily rainfall–runoff relationships.