Abstract Dissolved oxygen in water is an important ecological factor in ensuring the healthy growth of aquatic products, as hypoxic stress is known to restrict the growth of aquatic products. The accurate monitoring and prediction of dissolved oxygen is the key to precise regulation and control of pond aquaculture water quality. The current dissolved oxygen prediction model has some limitations, such as a short prediction period and inadequate prediction accuracy for actual production demands. Therefore, a prediction model of dissolved oxygen in pond culture was proposed based on K-means clustering and Gated Recurrent Unit (GRU) neural network. Firstly, the key factors affecting the changes in dissolved oxygen were selected by principal component analysis (PCA). The dissolved oxygen time series was then subjected to K-means clustering, and the dissolved oxygen prediction model was constructed using GRU. To improve the clustering effect, we enhanced the similarity calculation for the time series based on the variation of dissolved oxygen. This process combined the Euclidean distance with the dynamic time-warping distance. The proposed method can predict the dissolved oxygen content of aquaculture water over different time intervals according to the demands of real-world scenarios. The average absolute error of the 30-min interval model was 0.264, and the mean absolute percentage error was 3.5 %. Experimental results indicated that the proposed method achieves higher prediction accuracy and flexibility than the conventional approach.

中文翻译：

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基于K-means聚类和门控循环单元神经网络的池塘养殖水中溶解氧预测

摘要 水中溶解氧是保证水产品健康生长的重要生态因素，众所周知，缺氧胁迫会限制水产品的生长。溶解氧的准确监测和预测是池塘养殖水质精准调控的关键。目前的溶解氧预测模型存在预测周期短、对实际生产需求的预测精度不够等局限性。因此，提出了一种基于K-means聚类和门控循环单元（GRU）神经网络的池塘养殖溶解氧预测模型。首先，通过主成分分析（PCA）筛选出影响溶解氧变化的关键因素。然后对溶解氧时间序列进行 K 均值聚类，并使用GRU构建溶解氧预测模型。为了提高聚类效果，我们增强了基于溶解氧变化的时间序列的相似度计算。该过程将欧几里得距离与动态时间扭曲距离相结合。所提出的方法可以根据实际场景的需求预测不同时间间隔内水产养殖水的溶解氧含量。30 分钟间隔模型的平均绝对误差为 0.264，平均绝对百分比误差为 3.5%。实验结果表明，该方法比传统方法具有更高的预测精度和灵活性。我们基于溶解氧的变化增强了时间序列的相似性计算。该过程将欧几里得距离与动态时间扭曲距离相结合。所提出的方法可以根据实际场景的需求预测不同时间间隔内水产养殖水的溶解氧含量。30 分钟间隔模型的平均绝对误差为 0.264，平均绝对百分比误差为 3.5%。实验结果表明，该方法比传统方法具有更高的预测精度和灵活性。我们基于溶解氧的变化增强了时间序列的相似性计算。该过程将欧几里得距离与动态时间扭曲距离相结合。所提出的方法可以根据实际场景的需求预测不同时间间隔内水产养殖水的溶解氧含量。30 分钟间隔模型的平均绝对误差为 0.264，平均绝对百分比误差为 3.5%。实验结果表明，该方法比传统方法具有更高的预测精度和灵活性。所提出的方法可以根据实际场景的需求预测不同时间间隔内水产养殖水的溶解氧含量。30 分钟间隔模型的平均绝对误差为 0.264，平均绝对百分比误差为 3.5%。实验结果表明，该方法比传统方法具有更高的预测精度和灵活性。所提出的方法可以根据实际场景的需求预测不同时间间隔内水产养殖水的溶解氧含量。30 分钟间隔模型的平均绝对误差为 0.264，平均绝对百分比误差为 3.5%。实验结果表明，该方法比传统方法具有更高的预测精度和灵活性。