发表机构
Colorado School of Mines（科罗拉多矿业学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对美国西部SNOTEL网络SWE预测，提出用高斯过程去空间相关后训练LSTM，结合保形预测量化不确定性，优于多个基线模型。
AI 中文摘要
在美国西部，融雪对农业产业至关重要，同时也是市政饮用水的主要来源。因此，准确的积雪预测对于水资源政策和管理至关重要。自动雪情遥测（SNOTEL）站提供雪水当量（SWE）的每日精确测量，这些测量在空间和时间上表现出强相关性。我们解决了在SNOTEL网络中预测未来SWE值的问题。具体来说，我们使用基于高斯过程的线性变换来去除空间相关性，然后在去相关的SWE数据上训练长短期记忆（LSTM）神经网络。这种方法使LSTM能够学习每个站点干净的时域信号。我们表明，这种空间和时间成分的分离比多个基线模型产生了更好的预测性能。此外，我们结合了保形预测来量化所得SWE预报中的不确定性，提供了一种无分布的方法，以展示为时空数据建立预测区间的潜在框架。总之，准确的点预测和无分布的量化不确定性为亚季节尺度上的SWE累积预测或利用未来数据预测SWE提供了一个框架，同时激励并支持未来在预测大规模、时空完整的SWE图方面的工作。
英文摘要
In the Western United States, snowmelt is essential to the agricultural industry in addition to being a key source of municipal drinking water. Consequently, accurate snowpack forecasting is critical for water policy and management. Automated Snow Telemetry (SNOTEL) stations provide accurate daily measurements of snow water equivalent (SWE) that exhibit strong correlations in space and in time. We tackle the problem of predicting future SWE values across the SNOTEL network. Specifically, we use a Gaussian Process-based linear transformation to remove spatial correlations before training a long short-term memory (LSTM) neural network on the decorrelated SWE data. This approach allows the LSTM to learn a clean temporal signal at each station. We show that this separation of spatial and temporal components yields better predictive success than multiple baseline models. Furthermore, we incorporate conformal prediction to quantify uncertainty in the resulting SWE forecasts, providing a distribution-free approach to illustrate a potential framework for establishing predictive intervals for spatiotemporal data. Together, accurate point forecasts and distribution-free uncertainty quantification provide a framework for SWE accumulation forecasting on subseasonal scales or projecting SWE with future data while motivating and supporting future work in predicting a large-scale, spatiotemporally complete SWE map.
