A novel daily runoff forecasting model based on global features and enhanced local feature interpretation

Abstract

The development of artificial intelligence has introduced new perspectives to the field of hydrological forecasting. However, there is still a lack of research on efficiently identifying the physical characteristics of runoff sequences and developing prediction models that consider global and local sequence features. This study proposes a parallel computing prediction model called IMCAEN (Integrated Multi-Feature Causal Dilated Convolutional Attention Encoder Network) to address these issues. Unlike existing models, this model can monitor fluctuations and anomalies in time series. Incorporating the CDC-AA (Causal Dilated Convolutional Network with Aggregation Attention) and encoder structure captures both local sequence variations and global abrupt anomalies, allowing for comprehensive attention to sequence features. When predicting runoff data from three different hydrological conditions, the IMCAEN model achieved NSEC (Nash-Sutcliffe Efficiency Coefficient) values of 0.98, 0.97, and 0.88, respectively, and outperformed benchmark models in other evaluation indicators as well. Given the opacity of the feature distribution process in AI models, SHAP (SHapleyAdditive exPlanations) analysis and spatial expression of feature distribution are used to assess the contribution of each feature variable to the long-term trend of runoff and to verify the distribution of features trained in each module. The proposed IMCAEN model efficiently captures local and global information in the runoff evolution process through parallel computing and shared features, enabling accurate runoff forecasting and providing critical references for timely warnings and predictions.