Water quality indices (WQIs) are customarily associated with heavy data input demand, making them more rigorous and bulky. Such burdensome attributes are too taxing, time-consuming, and command a significant amount of resources to implement, which discourages their application and directly influences water resource monitoring. It is then imperative to focus on developing compatible, simpler, and less-demanding WQI tools, but with equally matching computational ability. Surrogate models are the best fitting, conforming to the prescribed features and scope. Therefore, this study attempts to provide a surrogate WQI as an alternative water quality monitoring tool that requires fewer inputs, minimal effort, and marginal resources to function. Accordingly, multivariate statistical techniques which include principal component analysis (PCA), hierarchical clustering analysis (HCA) and multiple linear regression (MLR) are applied primarily to determine four proxy variables and establish relevant model coefficients. As a result, chlorophyll-a, electrical conductivity, pondus Hydrogenium and turbidity are the final four proxy variables retained. A vital feature of the proposed surrogate index is that the input parameters qualify for inclusion into remote monitoring systems; henceforth, the model can be applied in remote monitoring programs. Reflecting on the model validation results, the proposed surrogate WQI is considered scientifically stable, with a minimum magnitude of divergence from the ideal water quality values. More importantly, the model displayed a predictive pattern identical to the ideal graph, matching on both index scores and classification values. The established surrogate model is an important milestone with the potential of promoting water resource monitoring and assisting in capturing of spatial and temporal changes in South African river catchments. This paper aims at outlining the methods used in developing the surrogate water quality index and document the results achieved.

中文翻译：

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多元统计分析在制定南非流域替代水质指数 (WQI) 中的应用

水质指数 (WQI) 通常与大量数据输入需求相关联，这使得它们更加严格和庞大。这些繁琐的属性过于繁重、耗时，并且需要大量资源来实施，这阻碍了它们的应用并直接影响水资源监测。因此，必须专注于开发兼容的、更简单的、要求较低的 WQI 工具，但具有同样匹配的计算能力。代理模型是最合适的，符合规定的特征和范围。因此，本研究试图提供替代 WQI 作为替代水质监测工具，它需要更少的投入、最少的努力和边际资源才能发挥作用。因此，包括主成分分析 (PCA) 在内的多元统计技术，层次聚类分析（HCA）和多元线性回归（MLR）主要用于确定四个代理变量并建立相关模型系数。因此，叶绿素-a、电导率、水池氢和浊度是保留的最后四个代理变量。拟议替代指数的一个重要特征是输入参数有资格纳入远程监测系统；今后，该模型可以应用于远程监控程序。反映模型验证结果，建议的替代 WQI 被认为在科学上是稳定的，与理想水质值的偏差最小。更重要的是，该模型显示了与理想图相同的预测模式，匹配指数分数和分类值。已建立的替代模型是一个重要的里程碑，具有促进水资源监测和协助捕捉南非河流集水区时空变化的潜力。本文旨在概述用于制定替代水质指数的方法并记录所取得的结果。