College of Agriculture & Environmental Sciences, Department of Life and Consumer Sciences, University of South Africa, Roodepoort, Gauteng, South Africa.
College of Science, Engineering & Technology, Institute for Nanotechnology & Water Sustainability, University of South Africa, Roodepoort, Gauteng, South Africa.
Environ Monit Assess. 2023 Jul 8;195(8):926. doi: 10.1007/s10661-023-11512-2.
Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.
淡水资源在维持生命和满足各种国内、农业、经济和工业需求方面发挥着关键作用。因此，非常有必要监测这些资源的水质。水质指数（WQI）模型自 20 世纪 60 年代首次引入以来，逐渐流行起来，用于评估和分类水生生态系统的水质。WQI 将复杂的水质数据转化为单一的无量纲数，以便能够方便地传达水资源生态系统的水质状况。为了筛选相关文章，采用了系统评价和荟萃分析（PRISMA）方法来包括或排除文章。最终的论文综合共使用了 17 篇同行评议的文章。在所审查的 WQI 中，只有加拿大环境部长理事会（CCME）指数、爱尔兰水质指数（IEWQI）和 Hahn 指数被用于评估流动和静止生态系统。此外，CCME 指数是唯一的例外，因为它不指定选择参数。除了西爪哇 WQI 和 IEWQI 之外，没有一个审查过的 WQI 进行了敏感性和不确定性分析，以提高 WQI 的可接受性和可靠性。事实证明，WQI 开发的所有阶段都存在一定程度的不确定性，可以使用统计和机器学习工具来确定。极端梯度增强（XGB）已被报道为一种有效的机器学习工具，可用于在参数选择、参数权重建立和确定准确分类方案的过程中处理不确定性。考虑到 IEWQI 模型架构及其在沿海和过渡水域的有效性，本研究建议未来在流动或静止生态系统中的研究除了使用机器学习技术提高预测准确性和稳健性以及增加应用领域之外，还要解决 WQI 模型相关的潜在不确定性问题。
