Due to simplicity and low costs, waste stabilisation ponds (WSPs) have become one of the most popular biological wastewater treatment systems that are applied in many places around the globe. Increasingly, pond modelling has become an interesting tool to improve and optimise their performance. Unlike process-driven models, generalised linear models (GLMs) can deliver considerable practical values in specific case studies with limited resources of time, data and mechanistic understanding, especially in the case of pond systems containing vast complexity of many unknown processes. This study aimed to investigate the key driving factors of dissolved oxygen variability in Ucubamba WSP (Ecuador), by applying and comparing numerous GLMs. Particularly, using different data partitioning and cross-validation strategies, we compared the predictive accuracy of 83 GLMs. The obtained results showed that chlorophyll a had a strong impact on the dissolved oxygen (DO) level near the water surface, while organic matter could be the most influential factor on the DO variability at the bottom of the pond. Among the 83 models, the optimal models were pond- and depth-specific. Specifically, among the ponds, the models of MPs predicted DO more precisely than those of facultative ponds; while within a pond, the models of the surface performed better than those of the bottom. Using mean absolute error (MAE) and symmetric mean absolute percentage error (SMAPE) to represent model predictive performance, it was found that MAEs varied in the range of 0.22–2.75 mg L−1 in the training period and 0.74–3.54 mg L−1 in the validation period; while SMAPEs were in the range of 2.35–38.70% in the training period and 10.88–71.62% in the validation period. By providing insights into the oxygen-related processes, the findings could be valuable for future pond operation and monitoring.

中文翻译：

---

预测废物稳定池中溶解氧的广义线性模型

由于简单和低成本，废物稳定池 (WSP) 已成为最受欢迎的生物废水处理系统之一，在全球许多地方得到应用。池塘建模越来越成为改善和优化其性能的有趣工具。与过程驱动模型不同，广义线性模型 (GLM) 可以在时间、数据和机制理解有限的特定案例研究中提供可观的实用价值，尤其是在包含许多未知过程的巨大复杂性的池塘系统的情况下。本研究旨在通过应用和比较众多 GLM 来研究 Ucubamba WSP（厄瓜多尔）中溶解氧变化的关键驱动因素。特别是，使用不同的数据分区和交叉验证策略，我们比较了 83 个 GLM 的预测准确性。所得结果表明，叶绿素a对水面附近的溶解氧（DO）水平有很强的影响，而有机物可能是池塘底部溶解氧变化的最大影响因素。在 83 个模型中，最佳模型是特定于池塘和深度的。具体而言，在池塘中，MPs 模型比兼性池塘模型更准确地预测 DO；而在池塘中，表面模型的性能优于底部模型。使用平均绝对误差 (MAE) 和对称平均绝对百分比误差 (SMAPE) 来表示模型预测性能，发现 MAE 在训练期间的变化范围为 0.22-2.75 mg L-1 和 0.74-3.54 mg L- 1在有效期内；而 SMAPE 在 2.35-38 的范围内。训练期为 70%，验证期为 10.88-71.62%。通过深入了解与氧气相关的过程，这些发现可能对未来的池塘操作和监测很有价值。