通过图形信号处理增强可解释的人工智能:在水分系统中的应用
Bruno M Brentan1, Andrea Menapace2, Martin Oberascher3
1Hydraulics and Water Resources Department, Universidade Federal de Minas Gerais, Av. Presidente Antonio Carlos, 6947, Belo Horizonte, 31555250, Minas Gerais, Brazil.
Water research
|July 10, 2025
概括
这项研究引入了一个透明的人工智能 (AI) 框架,用于水分系统 (WDS). 它使用可解释的人工智能 (XAI) 和图形信号处理来提高对WDS操作的理解和实时管理.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 水分系统 (WDS) 需要先进的监控和运营效率.
- 人工智能 (AI) 提供解决方案,但往往缺乏透明度,阻碍了采用.
- 可解释人工智能 (XAI) 对于理解关键基础设施中人工智能决策至关重要.
研究的目的:
- 开发一个新的框架,将XAI与图形信号处理集成为WDS中可解释的AI.
- 提高人工智能模型的透明度和理解,应用于WDS中的液压状态.
- 为实时WDS管理和弹性提供可扩展和高效的工具.
主要方法:
- 模拟多层感知子作为动态,加权,定向图.
- 利用自中心性作为图表度量来识别AI预测的关键驱动因素.
- 使用液压状态估计元模型和现实世界WDS基准验证框架.
主要成果:
- 拟议的XAI框架显著提高了WDS的AI模型的可解释性.
- 自主中心性有效地识别了影响液压状态预测的关键因素.
- 与SHAP和IG相比,该框架的处理时间超过70倍,可实现实时应用.
结论:
- 图形理论和XAI的整合为WDS提供了一个可扩展和透明的解决方案.
- 该方法支持传感器优先级和维护,以提高系统弹性.
- 这种方法通过增强人工智能解释性来促进可持续的城市水资源管理.
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