ForeSWE：基于不确定性感知注意力模型的雪水当量预测
ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model
AI总结：
针对传统雪水当量（SWE）预测未充分利用时空相关性且缺乏不确定性估计的问题，提出融合注意力机制与高斯过程模块的概率时空模型ForeSWE，在512个SNOTEL站点数据上较现有方法在预测精度和区间上显著提升，为水资源管理提供支持。
摘要：
在以积雪为主的流域中，各类复杂的水资源管理决策均基于雪水当量（SWE）的信息——SWE是广泛用于估算积雪层含水量的关键指标。然而，SWE预测具有挑战性，因为它受地形和一系列环境条件等多种因素影响，且具有时空变异性。传统SWE预测方法未能充分利用这些时空相关性，也未提供对决策者具有重要价值的不确定性估计。本文提出ForeSWE，一种融合深度学习与经典概率技术的新型概率时空预测模型。该模型结合注意力机制以整合时空特征及交互作用，同时包含高斯过程模块以提供原则性的预测不确定性量化。我们基于美国西部512个积雪遥测（SNOTEL）站点的数据对模型进行评估，结果显示其在预测精度和预测区间方面均较现有方法有显著提升，还突出了不同方法在不确定性估计上的有效性。这些发现共同为水资源管理界的部署与反馈提供了平台。
英文摘要：
Various complex water management decisions are made in snow-dominant watersheds with the knowledge of Snow-Water Equivalent (SWE) -- a key measure widely used to estimate the water content of a snowpack. However, forecasting SWE is challenging because SWE is influenced by various factors including topography and an array of environmental conditions, and has therefore been observed to be spatio-temporally variable. Classical approaches to SWE forecasting have not adequately utilized these spatial/temporal correlations, nor do they provide uncertainty estimates -- which can be of significant value to the decision maker. In this paper, we present ForeSWE, a new probabilistic spatio-temporal forecasting model that integrates deep learning and classical probabilistic techniques. The resulting model features a combination of an attention mechanism to integrate spatiotemporal features and interactions, alongside a Gaussian process module that provides principled quantification of prediction uncertainty. We evaluate the model on data from 512 Snow Telemetry (SNOTEL) stations in the Western US. The results show significant improvements in both forecasting accuracy and prediction interval compared to existing approaches. The results also serve to highlight the efficacy in uncertainty estimates between different approaches. Collectively, these findings have provided a platform for deployment and feedback by the water management community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
