Xu Longqin, Liu Wenjun, Chengqing Cai, Liu Tonglai, Gao Xuekai, Sohel Ferdous, Hasan Murtaza, Ghorbanpour Mansour, Hassan Shahbaz Gul, Liu Shuangyin
College of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China.
Smart Agriculture Engineering Technology Research Center of Guangdong Higher Education Institutes, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China.
Sci Rep. 2025 Jul 9;15(1):24643. doi: 10.1038/s41598-025-10786-5.
Dissolved oxygen (DO) is a vital parameter in regulating water quality and sustaining the health of aquatic organisms in aquaculture environments. Therefore, estimation and control of DO levels are essential in aquaculture operations. However, traditional chemical and photochemical approaches are limited by inaccuracies, environmental interferences, time consumption, and the inability to provide real-time data. Recently, artificial intelligence techniques have been studied for DO estimation. However, off-the-shelf models, such as Random Forest (RF) and Back Propagation (BP) have demonstrated poor performance due to intricate interactions in aquatic ecosystems, which leads to complex data patterns. This study proposes a water quality estimation model by combining a convolutional neural network (CNN), self-attention (SA), and bidirectional simple recurrent unit (BiSRU). One-dimensional convolution in CNN was employed to extract effective features and input into the SA mechanism to assign weights and emphasise crucial information. The model's accuracy is improved by incorporating BiSRU. This model evaluated the DO levels of the intensive aquaculture base in Nansha, Guangzhou City, Guangdong Province, China. The proposed CNN-SA-BiSRU achieved MSE, MAE, RMSE, and R of 0.0022, 0.0341, 0.0471, and 0.9765, respectively. The results of the experiments showed that the proposed model had a high level of accuracy in estimating the outcomes with minimal fluctuations in estimation errors. Moreover, accuracy for short-term prediction was significantly improved, surpassing the performance of existing methods. The highly accurate results indicate the potential of the proposed methodology for DO-level monitoring in aquaculture and its usage in the fishery industry.
溶解氧（DO）是调节水质和维持水产养殖环境中水生生物健康的重要参数。因此，溶解氧水平的估算和控制在水产养殖作业中至关重要。然而，传统的化学和光化学方法存在准确性不足、受环境干扰、耗时以及无法提供实时数据等局限性。近年来，人们对利用人工智能技术进行溶解氧估算展开了研究。然而，诸如随机森林（RF）和反向传播（BP）等现成模型，由于水生生态系统中复杂的相互作用导致数据模式复杂，其性能表现不佳。本研究提出了一种结合卷积神经网络（CNN）、自注意力机制（SA）和双向简单循环单元（BiSRU）的水质估算模型。CNN中的一维卷积用于提取有效特征，并输入到SA机制中进行加权，以突出关键信息。通过引入BiSRU提高了模型的准确性。该模型对中国广东省广州市南沙区集约化养殖基地的溶解氧水平进行了评估。所提出的CNN - SA - BiSRU模型的均方误差（MSE）、平均绝对误差（MAE）、均方根误差（RMSE）和相关系数（R）分别为0.0022、0.0341、0.0471和0.9765。实验结果表明，所提出的模型在估算结果时具有较高的准确性，估算误差波动极小。此外，短期预测的准确性显著提高，超越了现有方法的性能。这些高精度结果表明了所提出方法在水产养殖溶解氧水平监测中的潜力及其在渔业中的应用前景。
