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数据驱动的基于多级变压器的框架,用于智能水质监测

Ramya S¹, S Srinath², Pushpa Tuppad³

¹Department of Computer Science & Engineering, JSS Science and Technology University, Mysuru, India. ramya.shivanagu@gmail.com.

Scientific reports

|November 25, 2025

概括

本研究引入了一个深度学习框架,使用变压器模型准确预测废水质量,帮助可持续发展目标6. 这些模型改善了废水处理厂的数据质量和管理.

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:

获得清洁用水是一个关键的全球性挑战,可持续发展目标6 (SDG6) 旨在实现普遍获得水和环境卫生.
污水处理厂 (WWTP) 需要先进的监测和预测水质的工具,以确保有效的管理和合规性.
有限的培训数据是开发WWTP强大的预测模型的重要障碍.

研究的目的:

开发和评估基于变压器架构的深度学习框架,以准确预测废水质量.
通过生成模型解决WWTP中的数据局限性,并通过异常检测提高数据的可解释性.
为了比较各种基于变压器的模型和组合方法的性能,用于预测水质参数.

主要方法:

引入了TransGAN,一个转换器驱动的生成对抗网络,用于在WWTP中生成合成表格式数据.
提议TransAuto,一个变压器自编码器,用于在多变量时间序列数据中检测异常和特征识别.
评估了基于变压器的模型,包括时间序列变压器 (TST),TimeGPT,以及用于废水质量预测的Informer,Autoformer和FEDformer组合.

主要成果:

时间序列变压器 (TST) 实现了0.0028的平均平方误差 (MSE) 和0.9643.2的R平方 (R2).
结合Informer,Autoformer和FEDformer的整体模型表现出强的性能,MSE为0.0036,根平均平方误差 (RMSE) 为0.0582,平均绝对误差 (MAE) 为0.0438,R2为0.9646.
基于变压器的模型在捕捉复杂的时间动态和准确预测废水质量参数方面被证明是有效的.

结论:

基于变压器的深度学习模型为废水质量预测提供了强大而准确的解决方案.
开发的框架,包括数据增强和异常检测,支持改善水资源管理和遵守可持续发展目标6.
这些发现鼓励在可持续水资源管理的现实应用中采用先进的人工智能技术.

关键词:

自动编码器自动编码器组合学习模式组合学习模型生成性的对抗性网络.可持续发展目标6 可持续发展目标6 基于变压器的架构.废水处理厂废水处理厂

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