In the hydrology field, time series forecasting is crucial for efficient water resource management, improving flood and drought control and increasing the safety and quality of life for the general population. However, predicting long-term streamflow is a complex task due to the presence of extreme events. It requires the capture of long-range dependencies and the modeling of rare but important extreme values. Existing approaches often struggle to tackle these dual challenges simultaneously. In this paper, we specifically delve into these issues and propose Distance-weighted Auto-regularized Neural network (DAN), a novel extreme-adaptive model for long-range forecasting of stremflow enhanced by polar representation learning. DAN utilizes a distance-weighted multi-loss mechanism and stackable blocks to dynamically refine indicator sequences from exogenous data, while also being able to handle uni-variate time-series by employing Gaussian Mixture probability modeling to improve robustness to severe events. We also introduce Kruskal-Wallis sampling and gate control vectors to handle imbalanced extreme data. On four real-life hydrologic streamflow datasets, we demonstrate that DAN significantly outperforms both state-of-the-art hydrologic time series prediction methods and general methods designed for long-term time series prediction.

翻译：在水文领域，时间序列预测对于高效水资源管理、改善洪涝与干旱控制、提升公众安全与生活质量至关重要。然而，由于极端事件的存在，长期径流预测是一项复杂任务，需要同时捕捉长期依赖关系和建模罕见但重要的极值。现有方法通常难以同时应对这两重挑战。本文深入探讨这些问题，提出距离加权自正则神经网络（DAN）——一种基于极坐标表示学习增强的极端自适应长期径流预测模型。DAN通过距离加权多损失机制和可堆叠模块，动态优化外生数据中的指示序列，同时利用高斯混合概率建模处理单变量时间序列，增强对极端事件的鲁棒性。我们还引入克鲁斯卡尔-沃利斯采样和门控控制向量来处理不平衡的极端数据。在四个真实水文径流数据集上的实验表明，DAN在性能上显著优于最先进的水文时间序列预测方法及通用长期时间序列预测方法。