AI 中文总结
研究针对美国地下水砷污染问题，将其预测设为回归任务，构建整合数据集，运用k-NN、GIS等技术连接测量点。评估多种机器学习模型，发现图神经网络能考虑空间依赖性，超越梯度提升树，为地下水风险测绘和监测提供基础。
AI 中文摘要
美国地下水中的砷污染是长期存在的公共卫生危机，对依赖私人水井的家庭尤为严重。准确且有空间信息的砷浓度预测对识别高风险区域及集中缓解措施至关重要。但缺乏能表示各地区砷浓度连续变化的通用模型。本研究将砷预测设为回归任务，构建空间整合数据集，聚合来自多个数据库的超74000个砷样本。利用多种技术按位置连接美国各地的砷测量点。在此数据集基础上评估多种机器学习模型，结果表明虽然梯度提升树在表格数据领域仍被视为最先进，但图神经网络能进一步考虑空间依赖性，与或超越梯度提升树的结果。这些结果证明基于图和空间信息的学习可增强环境预测，为改进地下水风险测绘和监测奠定基础。
英文摘要
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 vital to identify high-risk areas and focus mitigation efforts. However, there is a lack of generalizable models for representing continuous variation in arsenic concentrations across regions. In this work, we pose arsenic prediction as a regression task and construct a spatially integrated dataset to aggregate over 74,000 arsenic samples from the Water Quality Portal (WQP), Mineral Resources Data System (MRDS), and Gridded National Soil Survey Geographic Database (gNATSGO). Specifically, we use a variety of techniques including kNearest Neighbors (k-NN) and Geographic Information Systems (GIS) to join arsenic measurement points from across the United States by location. Building on this dataset, we evaluate a diverse suite of machine learning models, including tree-based ensemble approaches, multilayer perceptrons, and spatially aware graph neural networks (GNN). Our findings show that while gradient-boosted trees are still considered state-of-the-art in the field of tabular data, GNNs are able to further account for spatial dependence to match or outperform the results of gradient-boosted trees. These results demonstrate that graph-based and spatially informed learning can enhance environmental prediction and provide a foundation for improved groundwater risk mapping and monitoring.
Comments7 pages, 5 figures, 1 table, AAAI '26 AI4ES workshop acceptance
