Hybrid deep learning framework for real-time DO prediction in aquaculture
Abstract 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 approach...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10786-5 |
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| author | Longqin Xu Wenjun Liu Cai Chengqing Tonglai Liu Xuekai Gao Ferdous Sohel Murtaza Hasan Mansour Ghorbanpour Shahbaz Gul Hassan Shuangyin Liu |
| author_facet | Longqin Xu Wenjun Liu Cai Chengqing Tonglai Liu Xuekai Gao Ferdous Sohel Murtaza Hasan Mansour Ghorbanpour Shahbaz Gul Hassan Shuangyin Liu |
| author_sort | Longqin Xu |
| collection | DOAJ |
| description | Abstract 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 R2 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. |
| format | Article |
| id | doaj-art-59102d9e54ec4e13bf4532e8ec0d21b4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-59102d9e54ec4e13bf4532e8ec0d21b42025-08-20T03:45:53ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-10786-5Hybrid deep learning framework for real-time DO prediction in aquacultureLongqin Xu0Wenjun Liu1Cai Chengqing2Tonglai Liu3Xuekai Gao4Ferdous Sohel5Murtaza Hasan6Mansour Ghorbanpour7Shahbaz Gul Hassan8Shuangyin Liu9College of Artificial Intelligence, Zhongkai University of Agriculture and EngineeringCollege of Artificial Intelligence, Zhongkai University of Agriculture and EngineeringCollege of Artificial Intelligence, Zhongkai University of Agriculture and EngineeringCollege of Artificial Intelligence, Zhongkai University of Agriculture and EngineeringCollege of Artificial Intelligence, Zhongkai University of Agriculture and EngineeringSchool of Information Technology, Murdoch UniversityFaculty of Chemical and Biological Science, Department of Biotechnology, The Islamia University of BahawalpurDepartment of Medicinal Plants, Faculty of Agriculture and Natural Resources, Arak UniversityCollege of Artificial Intelligence, Zhongkai University of Agriculture and EngineeringCollege of Artificial Intelligence, Zhongkai University of Agriculture and EngineeringAbstract 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 R2 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.https://doi.org/10.1038/s41598-025-10786-5Non-linearCNNBiSRUSelf-Attention mechanism |
| spellingShingle | Longqin Xu Wenjun Liu Cai Chengqing Tonglai Liu Xuekai Gao Ferdous Sohel Murtaza Hasan Mansour Ghorbanpour Shahbaz Gul Hassan Shuangyin Liu Hybrid deep learning framework for real-time DO prediction in aquaculture Scientific Reports Non-linear CNN BiSRU Self-Attention mechanism |
| title | Hybrid deep learning framework for real-time DO prediction in aquaculture |
| title_full | Hybrid deep learning framework for real-time DO prediction in aquaculture |
| title_fullStr | Hybrid deep learning framework for real-time DO prediction in aquaculture |
| title_full_unstemmed | Hybrid deep learning framework for real-time DO prediction in aquaculture |
| title_short | Hybrid deep learning framework for real-time DO prediction in aquaculture |
| title_sort | hybrid deep learning framework for real time do prediction in aquaculture |
| topic | Non-linear CNN BiSRU Self-Attention mechanism |
| url | https://doi.org/10.1038/s41598-025-10786-5 |
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