融合时间序列与静态数据，提升干旱预测精度
Drought forecasting using a hybrid neural architecture for integrating time series and static data
- 设计混合神经架构，同时处理动态时序与静态地理数据
- 在DroughtED数据集上实现当前最佳预测效果，准确率显著提升
- 适合需要跨区域通用模型的气候预测研究者使用
可靠的预测对早期预警系统和适应性干旱管理至关重要。以往大多数深度学习方法仅关注同质区域，依赖单一结构数据。本文提出一种混合神经架构，融合时间序列与静态数据，在DroughtED数据集上达到当前最优性能。结果表明，该方法在处理气候相关任务中的异构数据方面具有潜力，并能可靠预测美国干旱监测指数（USDM）等级，该指标由专家定义。此外，本工作验证了DroughtED数据集在支持深度学习模型进行无位置依赖训练方面的可行性。
原文摘要 · Abstract (English)
Reliable forecasting is critical for early warning systems and adaptive drought management. Most previous deep learning approaches focus solely on homogeneous regions and rely on single-structured data. This paper presents a hybrid neural architecture that integrates time series and static data, achieving state-of-the-art performance on the DroughtED dataset. Our results illustrate the potential of designing neural models for the treatment of heterogeneous data in climate related tasks and present reliable prediction of USDM categories, an expert-informed drought metric. Furthermore, this work validates the potential of DroughtED for enabling location-agnostic training of deep learning models.
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