Abstract Watershed area and a bunch of relief, land use, and wastewater characteristics for 32 upland and 33 lowland small river courses are generated. Based on these characteristics, logistic binary regression models are trained to predict if the river achieves the good physico-chemical status, and discriminant analysis models are trained to predict the physico-chemical status class on a five-class scale. Univariate models revealed that elevation (for upland rivers), the share of artificial surfaces (for lowland rivers) along with forests, and wastewater quality variables such as biochemical oxygen demand, chemical oxygen demand, and phosphorus are the most significant predictors. Discriminant analysis models performed better on upland than on lowland rivers. Achievement of good status could be predicted with an accuracy of ~90% (with 2 to 4 variable logit models), whereas the status class with an accuracy of 63/48% (with 2 to 4 variable discriminant analysis models) for upland and lowland rivers, respectively. This contribution uses Hungary as a case study.

中文翻译：

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两种多元统计方法从流域特征预测小水道理化状态

摘要 产生流域面积和32条高地和33条低地小河道的救济、土地利用和废水特征。基于这些特征，训练逻辑二元回归模型以预测河流是否达到良好的理化状态，并训练判别分析模型以在五级尺度上预测理化状态等级。单变量模型显示，海拔（对于高地河流）、人工地表（对于低地河流）与森林的比例以及废水质量变量（如生化需氧量、化学需氧量和磷）是最重要的预测因子。判别分析模型在高地比在低地河流上表现更好。可以以约 90% 的准确度（使用 2 到 4 个变量 logit 模型）预测良好状态的实现，而高地和低地的准确度为 63/48%（使用 2 到 4 个变量判别分析模型）的状态等级河流，分别。本文稿使用匈牙利作为案例研究。