College of Mining Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China; Institute of Remote Sensing and Geographic Information, Peking University, Beijing, 100871, China.
College of Environmental Science and Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China.
J Environ Manage. 2023 Sep 15;342:118283. doi: 10.1016/j.jenvman.2023.118283. Epub 2023 Jun 6.
Quantitative prediction by unmanned aerial vehicle (UAV) remote sensing on water quality parameters (WQPs) including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a (Chl-a), total suspended solids (TSS), and turbidity provides a flexible and effective approach to monitor the variation in water quality. In this study, a deep learning-based method integrating graph convolution network (GCN), gravity model variant, and dual feedback machine involving parametric probability analysis and spatial distribution pattern analysis, named Graph Convolution Network with Superposition of Multi-point Effect (SMPE-GCN) has been developed to calculate concentrations of WQPs through UAV hyperspectral reflectance data on large scale efficiently. With an end-to-end structure, our proposed method has been applied to assisting environmental protection department to trace potential pollution sources in real time. The proposed method is trained on a real-world dataset and its effectiveness is validated on an equal amount of testing dataset with respect to three evaluation metrics including root of mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R). The experimental results demonstrate that our proposed model achieves better performance in comparison with state-of-the-art baseline models in terms of RMSE, MAPE, and R. The proposed method is applicable for quantifying seven various WQPs and has achieved good performance for each WQP. The resulting MAPE ranges from 7.16% to 10.96% and R ranges from 0.80 to 0.94 for all WQPs. This approach brings a novel and systematic insight into real-time quantitative water quality monitoring of urban rivers, and provides a unified framework for in-situ data acquisition, feature engineering, data conversion, and data modeling for further research. It provides fundamental support to assist environmental managers to efficiently monitor water quality of urban rivers.
利用无人机 (UAV) 遥感对水质参数 (WQPs) 进行定量预测，包括磷、氮、化学需氧量 (COD)、生化需氧量 (BOD)、叶绿素 a (Chl-a)、总悬浮物 (TSS) 和浊度等，提供了一种灵活有效的监测水质变化的方法。在这项研究中，开发了一种基于深度学习的方法，该方法结合了图卷积网络 (GCN)、重力模型变体和涉及参数概率分析和空间分布模式分析的双反馈机，称为多点效应叠加图卷积网络 (SMPE-GCN)，用于通过无人机高光谱反射率数据高效计算大尺度水质参数的浓度。我们提出的方法具有端到端结构，已应用于协助环境保护部门实时追踪潜在污染源。该方法是在真实数据集上进行训练的，并在相同数量的测试数据集上进行了有效性验证，涉及三个评估指标，包括均方根误差 (RMSE)、平均绝对百分比误差 (MAPE) 和决定系数 (R)。实验结果表明，与最先进的基线模型相比，我们提出的模型在 RMSE、MAPE 和 R 方面具有更好的性能。该方法适用于量化七种不同的 WQPs，并且每种 WQP 的性能都很好。所有 WQPs 的 MAPE 范围为 7.16%至 10.96%，R 范围为 0.80 至 0.94。该方法为城市河流的实时定量水质监测带来了新的系统视角，并为原位数据采集、特征工程、数据转换和数据建模提供了一个统一的框架，以进行进一步的研究。它为协助环境管理者有效监测城市河流的水质提供了基本支持。
