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基于因果关系的域调整,对预测废水处理厂的异常废水质量波动进行交叉关注
Hanxin Zhang1, Haiping Zhang2, Jia Liu1
1College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, 201210, China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, China.
Water research
|January 17, 2026
概括
精确的废水废水质量预测得到了新的因果告知时间域适应模型 (CA-TDA) 的改进. 这种方法提高了废水处理厂 (WWTP) 的异常检测,尽管数据有限.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 准确预测废水处理厂 (WWTP) 异常废水质量波动对于环境保护至关重要.
- 有限的时间序列数据和不平衡的样本对异常预测构成重大挑战.
- 现有的转移学习方法与WWTP的异质性质作斗争,其特点是影响力变化和过程多样性.
研究的目的:
- 开发一种用于准确时间序列预测WWTP中异常废水质量波动的新型模型.
- 解决异质系统中现有的转移学习方法的局限性.
- 为了提高跨领域废水异常预测的准确性和稳定性.
主要方法:
- 提出了一种因果告知时间域适应模型,增强了交叉注意力 (CA-TDA).
- 使用变化自编码器 (VAE) 提取时间因果特征.
- 采用交叉注意力机制来确定跨领域适应的关键驱动因素,重点关注因果结构对齐,而不仅仅是分布对齐.
主要成果:
- 即使目标域数据有限 (源样本的8.35%),CA-TDA也实现了多个废水质量指标 (EQI) 的优异预测.
- 该模型表现出高预测性能,总 (TN) 的平均R2为0.9669,峰值R2为0.9820.
- 与基线方法相比,在连续异常波动的F1- (29-87%) 和F2-得分 (30-89%) 中观察到显著改善.
结论:
- CA-TDA有效地捕捉了WWTP废水质量的复杂,非静态波动.
- 该模型大大提高了跨领域废水异常预测的准确性和稳定性.
- 通过改善废水管理,CA-TDA显示了通过改善废水管理来保护水生环境的广泛应用潜力.
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