使用人工智能工具验证废水数据,并评估其在注释协议方面的表现
Imane Zidaoui1, Cédric Wemmert2, Matthieu Dufresne3
1Department of Fluid Mechanics, ICube Laboratory, 2 Rue Boussingault, Strasbourg 67000, France; 3D EAU, 3 Quai Kléber, Strasbourg 67000, France
概括
使用人工智能自动化废水数据验证对于防止水污染至关重要. 矩阵档案模型有效地检测传感器数据中的异常,提高了人类操作员的准确性和效率.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 废水工程 废水工程
背景情况:
- 精确测量废水排放对于保护水资源至关重要.
- 数据采集系统中的传感器故障可能导致偏差的污染流评估.
- 需要自动数据验证,以确保污染监测的可靠性.
研究的目的:
- 部署人工智能 (AI) 工具,用于自动化废水数据验证.
- 评估人工智能驱动的异常检测在协助人类操作员方面的附加值.
- 将不同异常检测算法的性能与现实数据进行比较.
主要方法:
- 两种最先进的异常检测算法的比较:一类支持向量机 (SVM) 和矩阵配置文件.
- 将算法应用于来自下水道网络的异质和噪音度数据.
- 对算法性能与专家验证和注释者间协议的评估.
主要成果:
- 发现一类SVM模型不适合研究数据的复杂性.
- 矩阵形状模型展示了有希望的结果,检测出大多数异常,只有少数假阳性.
- 使用Matrix Profile的AI辅助验证对象化并加速任务,而不影响性能.
结论:
- 矩阵形状模型是废水监测中自动异常检测的可行工具.
- 人工智能驱动的方法提高了数据验证过程的效率和客观性.
- 与传统的基于专家的方法相比,自动验证可以保持或提高性能.
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