English(EN) A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph

AmazonSWE数据集和模型改进了水面高程插补

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一个名为AmazonSWE的新数据集和一个相应的模型，旨在改进水面高程数据的插补。该数据集覆盖了亚马逊流域超过19,000个河段，历时十年，整合了包括SWOT传感器数据在内的卫星测高测量数据，并且比现有基准数据稀疏得多。提出的双向选择性状态空间模型与当前最先进的方法相比，表现出优越的性能，将与实测水位计的RMSE降低了18-39%，并为所有河段提供了预测。 AI

影响这项工作可以通过改进的数据插补技术来加强洪水预报和水资源管理。

排序理由该集群包含一篇详细介绍特定科学应用新数据集和模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Ruben Cartuyvels, Karim Douch, Gabriele Bertoli, Mounia El Baz, Artemis Vrettou, S\'ebastien Lef\`evre, Diego Fernandez Prieto · 2026-09-11 04:00

用于大型且极度稀疏时空图水面高程插补的数据集和模型

arXiv:2609.11580v1 Announce Type: new Abstract: Continuous monitoring of water surface elevation across river networks is critical for flood forecasting, water resource management, and understanding the global water cycle. Yet, the scarcity of in situ gauges across much of the gl…

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报道来源 [1]

用于大型且极度稀疏时空图水面高程插补的数据集和模型

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