Eutrophication and sporadic algal blooms occurring in the tributary bays of the Three Gorges Reservoir in Hubei, China, have become major environmental issues following impoundment. However, predicting eutrophication with traditional methods based on monthly monitoring data remains challenging. In order to explore the potential of data-driven models in eutrophication prediction and establish reliable prediction data-driven model based on monthly monitoring data. In this study, two ensemble-learning models, random forests (RF) and gradient boosted decision trees (GBDT), were used to predict eutrophication in Zhuyi Bay. To address the target, three objectives were solved. First, RF and GBDT used to regress chlorophyll-a concentrations showed good model fit across two monitoring data sets, with R² values of 0.809 and 0.822 for RF and 0.824 and 0.828 for GBDT. Second, the relative variable importance plots computed by ensemble-learning models was calculated for selecting monitoring parameters and identify drivers of eutrophication. To improve model fit, it was more important to monitor key parameters of eutrophication (such as water transparency) than to increase sample size. Third, K-Means++ modelling was used to partition eutrophication data into discrete levels. For three eutrophication levels, the classification accuracies of RF and GBDT were 0.8936 and 0.9064, respectively. When using only two eutrophication levels, accuracies for both models increased to 0.9617. This study suggests that ensemble-learning models, and in particular GBDT (firstly used in eutrophication), show excellent fitting ability for eutrophication compared with other machine-learning models and provided reliable eutrophication prediction method based on monthly monitoring data.

中文翻译：

---

三峡水库竹夷湾富营养化预测模型的集成学习性能

湖北三峡水库支流海湾发生富营养化和零星藻华，已成为蓄水后的重大环境问题。然而，使用基于月度监测数据的传统方法预测富营养化仍然具有挑战性。为探索数据驱动模型在富营养化预测中的潜力，建立基于月度监测数据的可靠预测数据驱动模型。在这项研究中，两个集成学习模型，随机森林 (RF) 和梯度增强决策树 (GBDT)，被用于预测竹夷湾的富营养化。为了实现目标，解决了三个目标。首先，用于回归叶绿素a浓度的RF 和 GBDT在两个监测数据集上显示出良好的模型拟合，R²RF 的值为 0.809 和 0.822，GBDT 的值为 0.824 和 0.828。其次，计算由集成学习模型计算的相对变量重要性图，用于选择监测参数和识别富营养化的驱动因素。为了改善模型拟合，监测富营养化的关键参数（如水透明度）比增加样本量更重要。第三，使用 K-Means++ 建模将富营养化数据划分为离散水平。对于三个富营养化水平，RF和GBDT的分类精度分别为0.8936和0.9064。当仅使用两个富营养化水平时，两种模型的准确度都增加到 0.9617。这项研究表明集成学习模型，特别是 GBDT（首先用于富营养化），