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使用合成数据和深度学习模型,对河流系统中的水质参数进行实时虚拟传感的可行性研究

Byeongwook Choi¹, Eun Jin Han², KyoungJin Lee³

¹Center for Water Cycle Research, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea; Division of Earth Environmental System Science (Major in Environmental Engineering), Pukyong National University, Busan, 48513, Republic of Korea.

Journal of environmental management

|April 3, 2025

概括

深度学习虚拟传感准确估计水质参数,如总有机碳 (TOC),总 (TN) 和总 (TP) 在小河流. 这种方法为传统的现场测量提供了更快,更有效的数据替代方案.

科学领域:

环境科学环境科学
水资源管理水资源管理
数据科学数据科学数据科学

背景情况:

有效的水质管理对于环境可持续性至关重要.
现实时间监控技术存在,但数据限制影响到小河流系统.
空载数据通常仅限于主要的河流系统,使得较小的河流系统服务不足.

研究的目的:

评估虚拟传感与深度学习 (DL) 结合的可行性,以实时估计小河流系统中的水质参数.
用实际和合成生成的数据来评估DL模型的性能.
为了比较实际数据与合成数据的实用性,用于短期和长期的水质预测.

主要方法:

使用虚拟传感,整合来自九个传感器指标的数据.
多重线性回归模型生成了总有机碳 (TOC),总 (TN) 和总 (TP) 的合成数据.
通过使用实际和合成数据集,训练和验证了两个深度学习模型.

主要成果:

最好的深度学习模型在TOC,TN和TP方面实现了0.4 mg/L以下的预测误差.
实际数据对于短期准确估计来说是优越的.
综合数据显示,它们更适合用于长期趋势预测.
使用DL模型进行参数估计需要不到一分钟,比现场测量要快得多.

结论:

基于深度学习的虚拟传感是一种可行且高效的方法,用于在数据有限的小河流系统中监测水质.
这种方法比传统的现场测量提供了显著的计算速度优势.
使用实际和合成数据的混合方法可以优化不同时间尺度的预测.

关键词:

深度学习是一种深度学习.河流系统河流系统综合数据综合数据虚拟传感是一种虚拟传感.水的质量水的质量

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