Hybrid deep learning framework for real-time DO prediction in aquaculture

Longqin Xu; Wenjun Liu; Cai Chengqing; Tonglai Liu; Xuekai Gao; Ferdous Sohel; Murtaza Hasan; Mansour Ghorbanpour; Shahbaz Gul Hassan; Shuangyin Liu

doi:10.1038/s41598-025-10786-5

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Hybrid deep learning framework for real-time DO prediction in aquaculture

Journal article

Open access

Peer reviewed

Hybrid deep learning framework for real-time DO prediction in aquaculture

Longqin Xu, Wenjun Liu, Cai Chengqing, Tonglai Liu, Xuekai Gao, Ferdous Sohel, Murtaza Hasan, Mansour Ghorbanpour, Shahbaz Gul Hassan and Shuangyin Liu

Scientific reports, Vol.15(1), 24643

2025

DOI: https://doi.org/10.1038/s41598-025-10786-5

PMID: 40634584

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Abstract

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Humanities and Social Sciences

multidisciplinary

Science

Science (multidisciplinary)

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.

Details

Title: Hybrid deep learning framework for real-time DO prediction in aquaculture
Authors/Creators: Longqin Xu - Zhongkai University of Agriculture and Engineering
Wenjun Liu - Zhongkai University of Agriculture and Engineering
Cai Chengqing - Zhongkai University of Agriculture and Engineering
Tonglai Liu - Zhongkai University of Agriculture and Engineering
Xuekai Gao - Zhongkai University of Agriculture and Engineering
Ferdous Sohel - Murdoch University
Murtaza Hasan - Islamia University of Bahawalpur
Mansour Ghorbanpour - Arak University
Shahbaz Gul Hassan - Zhongkai University of Agriculture and Engineering
Shuangyin Liu - Zhongkai University of Agriculture and Engineering
Publication Details: Scientific reports, Vol.15(1), 24643
Publisher: Nature Publishing Group UK
Number of pages: 13
Identifiers: 991005794850107891
Murdoch Affiliation: Centre for Crop and Food Innovation; School of Information Technology
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 3 Agriculture, Environment & Ecology; 3.91 Contamination & Phytoremediation; 3.91.1064 Sediment Metal Risks
Web Of Science research areas: Multidisciplinary Sciences
ESI research areas: Environment/Ecology