English(EN) Predicting Groundwater Arsenic Concentrations Using Graph Neural Networks

图神经网络改进地下水砷预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-23 04:00

研究人员开发了图神经网络（GNNs）来预测地下水砷浓度，解决了美国一个重要的公共卫生问题。通过整合来自多个数据库（包括水质门户和矿产资源数据系统）的超过74,000个砷样本数据，他们创建了一个全面的数据集。他们的研究结果表明，GNNs通过有效考虑砷含量的空间依赖性，其性能可以媲美甚至超越梯度提升树等传统方法。 AI

影响增强了环境预测能力，并为改进地下水风险制图和监测奠定了基础。

排序理由该集群包含一篇学术论文，详细介绍了使用机器学习进行环境预测的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · William Xing, Stephanie Yang, Aarush Bandemegal, Anushree Misra, Ananya Kalapatapu, Brennan Lagasse, Kevin Zhu · 2026-07-23 04:00

使用图神经网络预测地下水砷浓度

arXiv:2607.19392v1 Announce Type: new Abstract: Arsenic contamination in groundwater presents a longstanding public health crisis in the United States, especially for households depending on private wells. Accurate and spatially informed prediction of arsenic concentration is vit…

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使用图神经网络预测地下水砷浓度

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