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

Longqin Xu1,2,3, Wenjun Liu1,2,3, Cai Chengqing1,2,3

  • 1College of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China.

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|July 9, 2025
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Summary

This study introduces a new AI model for estimating dissolved oxygen (DO) in aquaculture, improving accuracy and real-time monitoring. The CNN-SA-BiSRU model offers a significant advancement for water quality management in the fishery industry.

Keywords:
BiSRUCNNNon-linearSelf-Attention mechanism

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Area of Science:

  • Aquaculture
  • Water Quality Management
  • Artificial Intelligence in Environmental Science

Background:

  • Dissolved oxygen (DO) is critical for aquatic life and aquaculture success.
  • Traditional DO monitoring methods are inaccurate, slow, and lack real-time capabilities.
  • Existing AI models struggle with complex aquatic ecosystem data patterns.

Purpose of the Study:

  • To develop an accurate and efficient AI model for real-time DO estimation in aquaculture.
  • To overcome the limitations of traditional methods and existing AI approaches for DO monitoring.
  • To evaluate the proposed model's performance in a real-world intensive aquaculture setting.

Main Methods:

  • Proposed a novel AI model combining Convolutional Neural Network (CNN), Self-Attention (SA), and Bidirectional Simple Recurrent Unit (BiSRU).
  • Utilized 1D CNN for feature extraction, SA for emphasizing crucial information, and BiSRU for enhanced accuracy.
  • Evaluated the model on DO data from an intensive aquaculture base in Nansha, Guangzhou City, China.

Main Results:

  • The CNN-SA-BiSRU model achieved excellent performance with MSE of 0.0022, MAE of 0.0341, RMSE of 0.0471, and R² of 0.9765.
  • Demonstrated high accuracy in DO estimation with minimal error fluctuations.
  • Significantly improved short-term prediction accuracy compared to existing methods.

Conclusions:

  • The proposed CNN-SA-BiSRU model shows high potential for accurate DO-level monitoring in aquaculture.
  • This methodology offers a significant advancement for water quality management in the fishery industry.
  • The model's ability to handle complex data patterns enhances its applicability in real-world aquaculture operations.