College of Engineering, Anhui Agricultural University, Hefei 230061, PR China.
College of Engineering, Anhui Agricultural University, Hefei 230061, PR China.
Water Res. 2024 Sep 1;261:122027. doi: 10.1016/j.watres.2024.122027. Epub 2024 Jul 3.
Depletion of dissolved oxygen (DO) is a significant incentive for biological catastrophic events in freshwater lakes. Although predicting the DO concentrations in lakes with high-frequency real-time data to prevent hypoxic events is effective, few related experimental studies were made. In this study, a short-term predicting model was developed for DO concentrations in three problematic areas in China's Chaohu Lake. To predict the DO concentrations at these representative sites, which coincide with biological abnormal death areas, water quality indicators at the three sampling sites and hydrometeorological features were adopted as input variables. The monitoring data were collected every 4 h between 2020 and 2023 and applied separately to train and test the model at a ratio of 8:2. A new AC-BiLSTM coupling model of the convolution neural network (CNN) and the bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) was proposed to tackle characteristics of discontinuous dynamic change of DO concentrations in long time series. Compared with the BiLSTM and CNN-BiLSTM models, the AC-BiLSTM showed better performance in the evaluation criteria of MSE, MAE, and R and a stronger ability to capture global dependency relationships. Although the prediction accuracy of hypoxic events was slightly worse, the general time series characteristics of abrupt DO depletion were captured. Water temperature regularly affects DO concentrations due to its periodic variations. The high correlation and the universal importance of total nitrogen (TN) and total phosphorus (TP) with DO reveals that point source pollution are critical cause of DO depletion in the freshwater lake. The importance of NTU at the Zhong Miao Station indicates the self-purification capacity of the lake is affected by the flow rate changes brought by the tributaries. Calculating linear correlations of variables in conjunction with a permutation variable importance analysis enhanced the interpretability of the proposed model results. This study demonstrates that the AC-BiLSTM model can complete the task of short-term prediction of DO concentration of lakes and reveal its response features of timing and magnitude of abrupt DO depletion.
溶解氧 (DO) 的消耗是淡水湖中生物灾难性事件的重要诱因。尽管利用高频实时数据预测湖泊中的 DO 浓度以防止缺氧事件是有效的，但相关的实验研究很少。本研究针对中国巢湖的三个问题区域，建立了 DO 浓度的短期预测模型。为了预测这些代表性地点（与生物异常死亡区域相吻合）的 DO 浓度，采用水质指标和水文学气象特征作为输入变量。监测数据在 2020 年至 2023 年间每 4 小时采集一次，并以 8:2 的比例分别用于训练和测试模型。提出了一种新的卷积神经网络 (CNN) 和具有注意力机制 (AM) 的双向长短期记忆 (BiLSTM) 的 AC-BiLSTM 耦合模型，以解决 DO 浓度长时间序列中不连续动态变化的特征。与 BiLSTM 和 CNN-BiLSTM 模型相比，AC-BiLSTM 在均方误差 (MSE)、平均绝对误差 (MAE) 和 R 等评价标准中的表现更好，并且具有更强的捕捉全局依赖关系的能力。尽管对缺氧事件的预测精度稍差，但捕捉到了 DO 急剧消耗的一般时间序列特征。由于水温周期性变化，水温经常影响 DO 浓度。总氮 (TN) 和总磷 (TP) 与 DO 的高度相关性和普遍重要性表明，点源污染是淡水湖中 DO 消耗的关键原因。钟苗站 NTU 的重要性表明，湖泊的自净能力受到支流带来的流速变化的影响。结合排列变量重要性分析计算变量的线性相关性增强了所提出模型结果的可解释性。本研究表明，AC-BiLSTM 模型可以完成湖泊 DO 浓度的短期预测任务，并揭示其 DO 急剧消耗的时间和幅度的响应特征。
