Parallel cooperation search algorithm and artificial intelligence method for streamflow time series forecasting

• Published in: Journal of hydrology (Amsterdam) Vol. 606; p. 127434
• Main Authors: Feng, Zhong-kai, Shi, Peng-fei, Yang, Tao, Niu, Wen-jing, Zhou, Jian-zhong, Cheng, Chun-tian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022
• Subjects: algorithms; Artificial intelligence; China; Evolutionary computation; hydrologic data; Hydrological time series forecasting; Parallel computing; prediction; stream flow; time series analysis; China; Parallel computing; Artificial intelligence; Hydrological time series forecasting; Evolutionary computation
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2022.127434

•Parallel cooperation search algorithm is proposed for parameter optimization.•Evolutionary method and parallel computing improve the ELM performance.•Real-world applications demonstrate the superiority of the presented model. Reliable streamflow prediction is an important productive information in the hydrology and water resources management fields. As used to forecast the nonlinear streamflow time series, the conventional artificial intelligence model may suffer from local convergence defect and fail to track the dynamic changes of the hydrological process when the model parameters and network structure are not well identified. Thus, this research develops a practical hydrological forecasting model based on parallel cooperation search algorithm (PCSA) and extreme learning machine (ELM), where the standard ELM method is chosen as the basic forecasting model, and then the PCSA method using several smaller and independent subswarms for parallel computation is used to determine satisfying input-hidden weights and hidden biases of the ELM model. The proposed model is used to forecast the nonlinear streamflow time series of several real-world hydrological stations in China. The results demonstrate that the proposed model outperforms the standard ELM model in various evaluation indicators. Thus, the key contributions of this study lie in two aspects: (1) for the first time, the parallel computing technique is developed to improve the global search ability and resources utilization efficiency of the emerging cooperation search algorithm; (2) an artificial intelligence model coupled with parallel evolutionary optimizer is proposed to improve the prediction accuracy of hydrological time series.