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通过因果机器学习提高灾难应对能力,用于估计洪水损害
Mujungu L Museru1, Rouzbeh Nazari1, Mohammad Reza Nikoo2
1Department of Civil Engineering, The University of Memphis, Memphis, TN 38152, United States.
The Science of the total environment
|July 25, 2025
概括
这项研究引入了因果信息神经网络 (CINN),以改善洪水损害预测. 与传统的机器学习模型相比,CINN模型可以减少22%的预测错误.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 模型对于洪水风险管理至关重要,但与稀缺的数据和相关依赖性作斗争.
- 传统模型容易受到数据分布转移的影响,并且缺乏因果关系理解.
研究的目的:
- 引入一个因果信息神经网络 (CINN) 框架,以加强洪水损害预测.
- 在洪水损害建模中,提高模型适应性,以应对未见的数据分布.
主要方法:
- 使用深度端到端因果推理 (DECI) 识别因果关系并估计平均治疗效果.
- 通过因果权重初始化和规范化,对神经网络进行综合因果洞察.
- 使用卡特里娜风NFIP索赔数据验证了CINN框架,并与六个ML模型进行了对比.
主要成果:
- 发现因果关系与领域知识一致,证实了模型的可信性.
- 与传统的ML模型相比,平均减少了22%的错误.
- 特性归因证实模型决策与已识别的因果关系保持一致,提高可解释性.
结论:
- 在洪水损害建模中,CINN框架表现出卓越的稳定性和预测准确性.
- 将因果关系整合到ML模型中可以提高灾害风险评估的适应性和可靠性.
- 这些发现支持开发更有弹性和可通用的灾害风险框架.
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