因果预先嵌入的物理信息的神经网络和对孔隙介质中甲胺传输的案例研究
Qiao Kang1, Baiyu Zhang1, Yiqi Cao1
1The Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, Newfoundland, A1B 3X5, Canada.
Water research
|July 5, 2024
概括
这项研究将实验中的因果先验集成到神经网络中,以更好地制药运输建模. 这种方法提高了环境应用中的模型稳定性和可解释性.
科学领域:
- 环境科学 环境科学
- 计算机建模 计算建模
- 药理学 药理学是指药理学的学科.
背景情况:
- 像甲胺这样的药物是新兴的污染物.
- 在沙性介质中进行运输建模对于了解污染物命运至关重要.
- 神经网络在环境应用中往往缺乏可解释性.
研究的目的:
- 通过将实验衍生的因果先验集成到神经网络中来开发一种新的运输建模方法.
- 提高环境研究中的黑子模型的可解释性和稳定性.
- 量化评估系统参数对甲福林运输的影响.
主要方法:
- 使用Hydrus-1D来估计从甲福林运输实验中无法观察到的参数.
- 构建了一个因果图来识别具有影响力的变量并估计因果动态.
- 在神经网络中嵌入因果先验,使用因果重量初始化和因果先例化.
- 使用AutoML进行超参数调整.
主要成果:
- 确定并量化了1型吸分数 (F),第一阶反应速率 (α) 和输送尺度的适度影响.
- 证明同时使用因果重量初始化和因果先前规范化可以提高模型稳定性.
- 通过结合的嵌入方法达到0.881的峰值R平方值.
- 首次在估计因果关系时考虑混因素.
结论:
- 将实验中获得的因果先验集成到神经网络中,为环境运输建模提供了一个平衡的,可解释的方法.
- 拟议的方法提高了模型稳定性和预测准确性.
- 这种范式推进了环境科学中数据驱动模型的应用.
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