An efficient parallel runoff forecasting model for capturing global and local feature information

Artificial intelligence has significantly accelerated the development of hydrological forecasting. However, research on how to efficiently identify the physical characteristics of runoff sequences and develop forecasting models that simultaneously address both global and local features of the sequences is still lacking. To address these issues, this study proposes a new PCPFN (PolyCyclic Parallel Fusion Network) prediction model that leverages the multi-periodic characteristics of runoff sequences and shares global features through a dual-architecture parallel computation approach. Unlike existing models, the PCPFN model can extract both the periodic and trend-based evolution features of runoff sequences. It constructs a multi-feature set in a "sequence-to-sequence" manner and employs a parallel structure of an Encoder and BiGRU (Bidirectional Gated Recurrent Unit) to simultaneously capture changes in both local, adjacent features and global characteristics, ensuring comprehensive attention to the sequence features. When predicting runoff data for three different hydrological conditions, the PCPFN model achieved R² values of 0.97, 0.98, and 0.97, respectively, with other evaluation indicators significantly outperforming the benchmark models. Additionally, due to the opacity in feature distribution processes of AI models, SHAP (Shapley Additive exPlanations) analysis was used to evaluate the contribution of each feature variable to long-term runoff trends. The proposed PCPFN model, during parallel computation, not only utilizes the intrinsic features of sequences and efficiently handles the distribution of local and global features but also shares predictive information in the output module, achieving accurate runoff forecasting and providing crucial references for timely warning and forecasting.