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.

中文翻译：

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一种基于全局特征和增强局部特征解释的新型日径流预测模型


人工智能的发展为水文预报领域引入了新的视角。然而，仍然缺乏关于有效识别径流序列的物理特征和开发考虑全局和局部序列特征的预测模型的研究。本研究提出了一种名为 IMCAEN （Integrated Multi-Feature Causal Dilated Convolutional Attention Encoder Network） 的并行计算预测模型来解决这些问题。与现有模型不同，此模型可以监控时间序列中的波动和异常。结合 CDC-AA （Causal Dilated Convolutional Network with Aggregation Attention） 和编码器结构，可以捕获局部序列变化和全局突然异常，从而全面关注序列特征。在预测三种不同水文条件的径流数据时，IMCAEN 模型分别实现了 0.98、0.97 和 0.88 的 NSEC（Nash-Sutcliffe Efficiency Coefficient）值，在其他评价指标上也优于基准模型。鉴于 AI 模型中特征分布过程的不透明性，SHAP （SHapleyAdditive explanations） 分析和特征分布的空间表达式用于评估每个特征变量对径流长期趋势的贡献，并验证每个模块中训练的特征分布。所提出的 IMCAEN 模型通过并行计算和共享功能有效地捕获径流演变过程中的局部和全局信息，从而实现准确的径流预测，并为及时预警和预测提供关键参考。