多变量混合密度网络用于基于紫外线可见光谱仪网络的小水域非点源污染的实时监测,每估计不确定性评估
Yongxin Liu1, Lifu Zhang1, Xuejian Sun1
1National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Water research
|October 25, 2025
概括
本研究使用紫外可见光谱仪网络和多变量混合密度网络来准确监测非点源污染 (NPSP). 该方法可以实时评估不确定性,改善小流域的水质管理.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 水资源管理 水资源管理
背景情况:
- 在小型流域中,非点源污染 (NPSP) 监测受到不经常和稀疏的数据收集的阻碍.
- 传统的方法很难准确地测量NPSP的关键参数,如总 (TP) 和总 (TN) 使用紫外线可见 (UV-Vis) 光谱.
- 对获取的水质数据缺乏实时不确定性评估,这复杂化了有效的管理.
研究的目的:
- 开发一个具有成本效益和强大的UV-Vis光谱测量网络,以加强NPSP监测.
- 创建一个多变量混合密度网络 (多变量MDN) 以共同检索光学活性和非活性NPSP参数.
- 为实时NPSP监控评估实施每估计不确定性评估方法.
主要方法:
- 建立了一个由20个UV-Vis光谱仪组成的网络,用于四个月的现场监测.
- 开发了多个MDN以建模光谱数据和水质参数 (TN,TP,COD) 之间的共变.
- 提出并验证了用于实时错误检测和模型诊断的每估计不确定性评估方法.
主要成果:
- 使用多MDN实现了TN (R2=0.92),TP (R2=0.84) 和CODMn (R2=0.90) 的高检索精度.
- 每估计不确定性方法有效地识别了检索错误和光谱仪异常.
- 在Pizhou的河流中使用实时监测数据揭示了NPSP的时空变化.
结论:
- 开发的UV-Vis光谱仪网络和多MDN为NPSP监控提供了准确,强大和具有成本效益的解决方案.
- 实时不确定性评估提高了光谱监测数据的可靠性和实用性.
- 改善了NPSP数据的时空覆盖,支持更好的流域管理决策.
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