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城市河流环境的监测,通过使用基于超谱遥感的波纹传播图谱网络量化水质参数的分布

Yishan Zhang¹, Lun Wu²

¹College of Geological and Surveying Engineering, Taiyuan University of Technology, Taiyuan, 030024, China; Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, 100871, China.

Environmental pollution (Barking, Essex : 1987)

|August 22, 2025

概括

一个新的混合反波网 (HF-RN) 通过无人机的高光谱数据有效监测城市河水质量. 这种方法减少了对水样的需求,并提高了对关键水质参数的预测准确性.

科学领域:

环境科学
遥感技术
数据科学

背景情况:

城市河流水质监测对于环境保护至关重要.
无人飞行器 (UAV) 的超谱遥感可提供灵活的水质监测.
现有的方法通常需要大量昂贵的水样, 缺乏统一的框架.

研究的目的:

提出一个混合反波网 (HF-RN) 来从无人机的高光谱数据中获取多个水质参数 (WQP).
开发一个统一的预测WQP的框架,减少对广泛的水样采集的依赖.
量化WQP的空间分布,提高预测的准确性和稳定性.

主要方法:

开发了一个混合反波网络 (HF-RN),集成深度学习,空间模式分析和概率统计.
使用无人机的高光谱数据进行WQP检索.
采用波纹传播图谱网络在被采样和未被采样区域之间进行信息共享.

主要成果:

HF-RN成功获取了总 (TP),总 (TN),化学氧气需求 (COD),生物化学氧气需求 (BOD),叶绿素a (Chl-a) 和总悬浮固体 (TSS) 的度.
这种方法表明水样要求降低,预测准确性和稳定性提高.
在现实数据集中,HF-RN实现了7.54%至12.38%的平均绝对百分比误差 (MAPE),表现优于基线模型.

结论:

拟议的HF-RN为城市河流水质监测提供了一种高效且具有成本效益的方法.
这项研究为优化城市河流管理和污水排放控制奠定了基础.
HF-RN 作为一个有价值的可视化工具来评估水质变化.

关键词:

超光谱图像信息传播空间分布分析频谱分离水质参数

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