Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS

Accurate forecast of short-term to long-term streamflow prediction is of great importance for water resources management. However, with the advent of novel hybrid machine learning methods, it remains unclear whether these hybrid models can outperform the traditional streamflow forecast models. Therefore, in this study, we trained and tested the performance of several evolutionary algorithms, including Fire-Fly Algorithm(FFA), Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) hybridized with ANFIS. Three forecast horizons, short-term (Daily), mid-term (Weekly and Monthly) and long-term (Annual) with fifteen input-output combinations, a total of 90 models, were developed and tested. A Monte Carlo Simulation (MCS) framework is used for uncertainty analysis. Daily inflow to the Karun III dam, located in the southeast of Iran, for the period of June 2005 to December 2016 were used. Results indicated that: 1) All developed hybrid algorithms significantly outperformed the traditional ANFIS model performance for all prediction horizons. The best hybrid models were ANFIS-GWO1, ANFIS-GWO7 and ANFIS-GWO11 such that the values of R², RMSE, NSE, and RAE were improved by 12%, 10%, 18.5% and 14.3% for the short-term forecasts, 15%, 13%, 20% and 21.1% for the mid-term forecasts, and 10.3%, 7.5%, 10.5% and 14% for the long-term forecasts; 2) Uncertainty analysis indicates that nearly all hybrid models have significantly reduced uncertainty levels compared to the traditional ANFIS model; and 3) A simple explicit equation based on the hybrid ANFIS results was provided for streamflow forecasting, which is a major advantage compared to the classical blackbox machine learning models.