Work

Prediction of the Dissolved Oxygen Content in Aquaculture Based on the CNN-GRU Hybrid Neural Network

Year: 2024

Type: article

Abstract: The dissolved oxygen (DO) content is one of the important water quality parameters; it is crucial for assessing water body quality and ensuring the healthy growth of aquatic organisms. To enhance the prediction accuracy of DO in aquaculture, we propose a fused neural network model integrating a convolutional neural network (CNN) and a gated recurrent unit (GRU). This model initially employs a CNN to extract primary features from water quality parameters. Subsequently, the GRU captures temporal information and long-term dependencies, while a temporal attention mechanism (TAM) is introduced to further pinpoint crucial information. By optimizing model parameters through an improved particle swarm optimization (IPSO) algorithm, we develop a comprehensive IPSO-CNN-GRU-TAM prediction model. Experiments conducted using water quality datasets collected from Eagle Mountain Lake demonstrate that our model achieves a root mean square error (RMSE) of 0.0249 and a coefficient of determination (R2) of 0.9682, outperforming other prediction models with high precision. The model exhibits stable performance across fivefold cross-validation and datasets of varying depths, showcasing robust generalization capabilities. In summary, this model allows aquaculturists to precisely regulate the DO content, ensuring fish health and growth while achieving energy conservation and carbon reduction, aligning with the practical demands of modern aquaculture.

Source: Water

DOI: 10.3390/w16243547

Authors: Ying Ma, Qiwei Fang, Shengwei Xia, Yu Zhou

Institution: Fujian University of Technology

Language: English

Topic: Water Quality Monitoring Technologies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Zero hunger

Open Access status: gold

Estimated APC paid: $2,955