State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China.
Sensors (Basel). 2023 May 14;23(10):4739. doi: 10.3390/s23104739.
Accurate water quality estimation is important for water environment monitoring and water resource management and has emerged as a pivotal aspect of ecological rehabilitation and sustainable development. However, due to the strong spatial heterogeneity of water quality parameters, it is still challenging to obtain highly accurate spatial patterns of them. Taking chemical oxygen demand as an example, this study proposes a novel estimation method for generating highly accurate chemical oxygen demand fields in Poyang Lake. Specifically, based on the different water levels and monitoring sites in Poyang Lake, an optimal virtual sensor network was first established. A Taylor expansion-based method with integration of spatial correlation and spatial heterogeneity was developed by considering environmental factors, the optimal virtual sensor network, and existing monitoring stations. The proposed approach was evaluated and compared with other approaches using a leave-one cross-validation process. Results show that the proposed method exhibits good performance in estimating chemical oxygen demand fields in Poyang Lake, with mean absolute error improved by 8% and 33%, respectively, on average, when compared with classical interpolators and remote sensing methods. In addition, the applications of virtual sensors improve the performance of the proposed method, with mean absolute error and root mean squared error values reduced by 20% to 60% over 12 months. The proposed method provides an effective tool for estimating highly accurate spatial fields of chemical oxygen demand concentrations and could be applied to other water quality parameters.
准确的水质估计对于水环境监测和水资源管理至关重要，已成为生态恢复和可持续发展的关键方面。然而，由于水质参数具有很强的空间异质性，因此仍然难以获得它们的高精度空间格局。以化学需氧量（COD）为例，本研究提出了一种新的估计方法，用于生成鄱阳湖高精度的 COD 场。具体而言，根据鄱阳湖的不同水位和监测站点，首先建立了最优虚拟传感器网络。通过考虑环境因素、最优虚拟传感器网络和现有监测站，开发了一种基于泰勒展开的方法，该方法结合了空间相关性和空间异质性。采用留一交叉验证过程对所提出的方法进行了评估，并与其他方法进行了比较。结果表明，该方法在估计鄱阳湖的 COD 场方面表现出良好的性能，与经典插值器和遥感方法相比，平均而言，COD 场的均方根误差提高了 8%和 33%。此外，虚拟传感器的应用提高了所提出方法的性能，在 12 个月内，均方根误差和平均绝对误差值降低了 20%至 60%。该方法为估计高精度的 COD 浓度空间场提供了有效的工具，并可应用于其他水质参数。
