A comparativestudy of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model

Abstract

Population-based optimization algorithms have been successfully applied to hydrological forecasting recently owing to their powerful ability of global optimization. This paper investigates three algorithms, i.e. differential evolution(DE),artificial bee colony(ABC) and ant colony optimization(ACO),to deter- mine the optimal one for forecasting downstream river flow. A hybrid neural network (HNN) model, which incorporates fuzzy pattern-recognition and a continuity equation into the artificial neural net- work, is proposed to forecast downstream river flow based on upstream river flows and areal pre- cipitation. The optimization algorithm is employed to determine the premise parameters of the HNN model. Daily data from the Altamaha River basin of Georgia is applied in the forecasting analysis. Dis- cussions on the forecasting performances, convergence speed and stability of various algorithms are presented.For completeness' sake,particles warm optimization (PSO) is included as a benchmark case for the comparison of forecasting performances. Results show that the DE algorithm attains the best performance in generalization and forecasting.The forecasting accuracy of the DE algorithm is com- parable to that of the PSO,and yet presents weak superiority over the ABC and ACO.The Diebold– Mariano (DM) test indicates that each pair of algorithms has no difference under the null hypothesis of equal forecasting accuracy.The DE and ACO algorithms are both favorable for searching parameters of the HNN model,including the recession coefficient and initial storage.Further analysis reveals the drawback of slow convergence and time-consumption of the ABC algorithm.The three algorithms present stability and reliability with respect to their control parameters on the whole. It can be concluded that the DE and ACO algorithms are considerably more adaptive in optimizing the forecasting problem for the HNN model.