Mixed Copula for streamflow simulation based on intelligent knowledge set and parameter calibration using cooperation search algorithm

Streamflow simulation plays a crucial role in the scientific management of reservoir operations and water resources. This study proposes a novel streamflow simulation method using a mixed Copula approach, coupled with an intelligent knowledge set and a cooperation search algorithm for parameter optimization. This method addresses the limitations of traditional Copula-based models, such as limited expressive capability, an excessive number of model parameters, and long computation times. Initially, an intelligent knowledge set is established using a time series model. Next, the cooperation search algorithm is employed to estimate model parameters. Finally, simulated streamflow values are generated via conditional Copula. Theoretical analysis shows significant reduction in computational complexity compared to traditional methods. Engineering applications at multiple hydrological stations validate that the proposed method reduces parameters by over 99%, shortens estimation time to 3–4% of traditional methods, and improves accuracy by 5–15%. This novel method integrates an intelligent knowledge set with parameter optimization to improve the precision of streamflow simulation, providing a valuable technical tool for watershed management.