Work

Predicting Flatfish Growth in Aquaculture Using Bayesian Deep Kernel Machines

Year: 2025

Type: article

Abstract: Olive flounder (Paralichthys olivaceus) is a key aquaculture species in South Korea, but its production has been challenged by rising mortality under environmental stress from key environmental factors such as water temperature, dissolved oxygen, and feeding conditions. To support adaptive management, this study proposes a Bayesian Deep Kernel Machine Regression (BDKMR) model that integrates Gaussian process regression with neural network-based feature learning. Using longitudinal data from commercial farms, we model fish growth as a function of water temperature, dissolved oxygen, and feed quantity. Model performance is assessed via Leave-One-Out Cross-Validation and compared against kernel ridge regression and Bayesian kernel machine regression. Results show that BDKMR achieves substantially lower prediction errors, indicating superior accuracy and robustness. These findings suggest that BDKMR offers a flexible and effective framework for predictive modeling in aquaculture systems.

Source: Applied Sciences

DOI: 10.3390/app15179487

Authors: Junhee Kim, Seung-Won Seo, Ho-Jin Jung, Hyun-Seok Jang, Han-Kyu Lim, Seongil Jo

Institutions: Inha University, Insilicogen (South Korea), Mokpo National University

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Life below water, Zero hunger

Open Access status: gold

Estimated APC paid: $2,896