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一个新的基于物理知识的AI框架,用于评估和预测室内度和风险分类

¹Department of Earthquake and Risk Management, Muğla Metropolitan Municipality, Muğla, Türkiye.

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|March 4, 2026

概括

一个新的物理信息神经网络 (PINN) 模型准确地预测室内气水平,这是一个主要的肺癌风险. 这种人工智能方法通过识别有针对性的减风险的高风险家庭来帮助公共卫生.

科学领域:

环境科学环境科学
地质物理学地质物理学
人工智能的人工智能

背景情况:

室内气是肺癌的主要环境原因.
由于测量挑战,难以准确评估室内度.
现有的方法缺乏地质和建筑因素的全面整合.

研究的目的:

开发和验证一个新的物理信息神经网络 (PINN) 框架,用于预测室内度.
将地质源和建筑特征集成到机器学习模型中.
建立一个具有成本效益和强大的工具,用于风险评估和公共卫生规划.

主要方法:

开发了一种使用PINN的地质信息评估 (GIRA) 模型.
来自地质 (Qg),断层 (Qf),建筑材料 (Qb) 和建筑孔隙性的纳入贡献.
在土耳其西部的957个建筑物上验证了模型.

主要成果:

该PINN模型显著超过了传统的机器学习方法.
获得了52Bq/m3的平均绝对误差和0.96.9的R2.
成功确定了15.3%的结构是高风险的 (>300 Bq/m3).

结论:

基于PINN的GIRA模型为室内评定提供了一个强大的,可解释的,且具有成本效益的解决方案.
这种方法可以实现自动化风险分类,并支持积极的公共卫生战略.
通过基于物理的机器学习,在环境危害评估中建立了一个新的范式.

关键词:

环境健康中的人工智能地质性风危险地质性风危险室内子预测室内子预测基于物理学的神经网络.风险分类风险分类风险分类

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