向着透明的地下水污染风险预测:整合因果发现和贝叶斯图神经网络
1College of Computing, Georgia Institute of Technology, 225 North Avenue NW, Atlanta, GA 30332, USA.
The Science of the total environment
|August 17, 2025
概括
这项研究引入了一个新的AI模型用于地下水污染预测,提供可解释的结果和量化的不确定性. 增强的贝叶斯因果图神经网络 (EBC-GNN) 改善了环境风险评估.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 地下水污染对生态系统,农业和公共卫生构成重大风险.
- 当前的预测建模受到复杂的环境系统和不透明的传统机器学习模型的阻碍.
研究的目的:
- 引入一个新的框架,即增强的贝叶斯因果图神经网络 (EBC-GNN),用于可靠和可解释的地下水污染预测.
- 整合因果发现,时空图神经网络和贝叶斯不确定性量化,以改进环境风险建模.
主要方法:
- 采用了多种来源的数据集:环保署的水质记录,土地覆盖,气候数据和工业设施登记.
- 在贝叶斯框架内使用DYNOTEARS和时空图神经网络的因果发现.
- 包含贝叶斯的不确定性量化,以进行可靠的预测.
主要成果:
- 在加利福尼亚州的Yolo和Tulare县,EBC-GNN实现了70%的R2成功率,超过了传统机器学习基线.
- 确定了地下水污染的环境因果驱动因素.
- 发现了与政策相关的见解,例如湿地的保护作用和与农业用地使用相关的风险.
结论:
- EBC-GNN为环境风险建模和决策支持提供了一个可扩展,透明和可解释的工具.
- 通过将可解释性与监管调整相结合,为可持续地下水管理策略建立了一个新的基准.
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