通过使用多点效应叠加的图形卷积网络从超光谱图像中检索水质参数来监测水质:在毛州河的一个案例研究
Yishan Zhang1, Xin Kong2, Licui Deng3
1College of Mining Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China; Institute of Remote Sensing and Geographic Information, Peking University, Beijing, 100871, China.
Journal of environmental management
|June 8, 2023
概括
本研究引入了使用无人机 (UAV) 超谱数据进行高效水质监测的深度学习模型. 多点效应叠加的图形卷积网络 (SMPE-GCN) 准确地预测了关键的水质参数.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 水质监测对于城市河流健康至关重要.
- 传统的方法往往是耗时和劳动密集的.
- 无人机 (UAV) 遥感提供了一个灵活的替代方案.
研究的目的:
- 开发一种高效的深度学习方法,用于使用无人机高光谱数据对水质参数 (WQPs) 的定量预测.
- 为了实现实时监测和城市河流污染源的追踪.
- 为获取和建模水质数据提供统一的框架.
主要方法:
- 开发了一种新的深度学习模型:具有多点效应叠加的图形卷积网络 (SMPE-GCN).
- 集成图形卷积网络 (GCN),一种重力模型变体,以及双反机.
- 利用无人机的高光谱反射率数据进行大规模的WQP预测.
主要成果:
- 该SMPE-GCN模型在预测七个WQPs方面取得了很高的准确性.
- 与基线模型相比,性能指标显示出更好的结果 (MAPE:7.16%-10.96%,R2:0.80-0.94).
- 该模型在现实世界数据集中证明了WQP量化的有效性.
结论:
- 拟议的SMPE-GCN方法为实时定量水质监测提供了一种新且系统的方法.
- 该框架支持环境管理人员有效评估城市河流水质量.
- 这项研究为未来的水质评估现场数据采集和建模研究提供了基础.
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