基于深度学习的软传感器,用于在分散的数据上进行持续的叶绿素估计.
Judith Sáinz-Pardo Díaz1, María Castrillo1, Álvaro López García1
1Instituto de Física de Cantabria (IFCA), CSIC-UC, Avda. Los Castros s/n, Santander (Cantabria) 39005, Spain.
Water research
|October 23, 2023
概括
这项研究估计了河流中的叶绿素 (Chl) 度,使用数据驱动软传感. 联合学习模型显示出水质监测的优越泛化,优于集中式方法.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 传感器技术 传感器技术
背景情况:
- 实时监测水体对于保护至关重要.
- 目前的传感器技术在实时,高频度测量方面存在局限性,以有效管理风险.
- 估计叶绿素度对于评估水质至关重要.
研究的目的:
- 开发一种数据驱动的软传感方法,用于估计河流支流中的叶绿素度.
- 评估不同神经网络学习方法 (个体化,集中化,联合) 对此估计任务的性能.
- 分析数据减少对模型性能的影响.
主要方法:
- 利用三个水力物理和三个气象特征作为神经网络模型的输入.
- 实施个人,集中和联合学习方法,以进行数据驱动的估计.
- 在各种数据减少场景下研究模型性能.
主要成果:
- 个人学习方法往往产生了最好的培训结果.
- 与其他方法相比,联合学习显示出优越的概括能力.
- 联合学习通常在集中学习中获得的结果上有所改善.
结论:
- 数据驱动软传感为估计叶绿素度提供了可行的替代方案,当直接测量具有挑战性时.
- 联合学习是一种可靠的水质监测方法,提供比集中式方法更好的概括性.
- 这些发现支持使用先进的机器学习技术进行环境监测和保护工作.
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