PDF(Pubmed)
Abstract：
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.
摘要：
准确的水质估算对于水环境监测和水资源管理至关重要，并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，获得它们的高度精确的空间模式仍然具有挑战性。以化学需氧量为例,本研究提出了一种新的估算方法，用于生成高精度的鄱阳湖化学需氧量场。具体来说,根据鄱阳湖不同的水位和监测点，首先建立了一个最优的虚拟传感器网络。通过考虑环境因素，开发了一种基于泰勒展开的空间相关性和空间异质性集成方法。最优的虚拟传感器网络，和现有的监测站。使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，该方法在鄱阳湖化学需氧量场估算中表现出良好的性能。平均绝对误差分别提高了8%和33%，分别,平均而言,与经典的插值器和遥感方法相比。此外,虚拟传感器的应用提高了所提出方法的性能，平均绝对误差和均方根误差值在12个月内减少20%至60%。所提出的方法为估算化学需氧量浓度的高度精确空间场提供了有效的工具，并且可以应用于其他水质参数。
