Hybrid artificial neural network and cooperation search algorithm for nonlinear river flow time series forecasting in humid and semi-humid regions

Abstract

Accurate river flow forecasting is of great importance for the scientific management of water resources system. With the advantages of easy implementation and high flexibility, artificial neural network (ANN) has been widely employed to address the complex hydrological forecasting problem. However, the conventional ANN method often suffers from some defects in practice, like slow convergence and local minimum. In order to enhance the ANN performance, this study proposes a hybrid river flow forecasting method by integrating the novel cooperation search algorithm (CSA) into the learning process of ANN. In other words, the computational parameters of the ANN network (like threshold and linking weights) are iteratively optimized by the CSA method in the feasible state space. The proposed method is applied to the river flow data collected from two real-world hydrological stations in China. Several Quantitative indexes are chosen to compare the performance of the developed models, while the comprehensive analysis between the simulated and observed flow data are conducted. The experimental results show that in different scenarios, the hybrid method based on ANN and CSA always outperforms the control models and yields superior forecasting results during both training and testing phases. In Three Gorges Project, the presented method makes 11.10% and 5.42% improvements in the Nash–Sutcliffe efficiency and Coefficient correlation values of the standard ANN method in the testing phase. Thus, this interesting finding shows that the performance of the artificial intelligence models in river flow time series forecasting can be effectively improved by metaheuristic algorithm with outstanding global search ability.