This article proposes a statistical method based on the regularized canonical correlation analysis (RCCA) to improve on the conventional canonical correlation analysis (CCA) method for seasonal climate prediction. The fundamental idea of this method is to combine the regularization principle with the classical CCA to handle high‐dimensional data in which the number of variables is larger than the number of observations. This study focuses on prediction of future precipitation for the boreal summer (June‐July‐August, JJA) on both global and regional scales. We apply the RCCA method to the JJA hindcast/forecast archives for 29 years (1979–2007) obtained from the operational seasonal prediction system at Korea Meteorological Administration (KMA) in order to correct the model biases and provide a more accurate climate prediction. It is observed that the results from the RCCA method demonstrate a more accurate seasonal climate prediction as compared to the results from the general circulation model (GCM) and the CCA method coupled with empirical orthogonal functions (EOF) analysis, which is the modified CCA technique widely used, in terms of both correlation and mean square error. Copyright © 2011 Royal Meteorological Society

The cultivation of abalone, a species with high economic value, faces significant challenges due to its slow growth rate and sensitivity to environmental conditions, resulting in prolonged cultivation periods and increased mortality risks. To address these challenges, we propose a novel probabilistic machine learning approach based on a Bayesian framework to predict abalone growth by modeling key environmental factors, including water temperature, pH, salinity, nutrient supply, and dissolved oxygen levels. The proposed method employs a weighted Bayesian kernel machine regression model, integrating Gaussian processes with a spike-and-slab prior to identify influential variables. This approach accommodates heteroscedasticity, capturing varying levels of variance across observations, and models complex, non-linear relationships between environmental factors and abalone growth. Our analysis reveals that time, dissolved oxygen, salinity, and nutrient supply are the most critical factors influencing growth, while water temperature and pH play relatively minor roles under controlled indoor farming conditions. Interaction analysis highlights the non-linear dependencies among factors, such as the combined effects of salinity and nutrient supply. The proposed model not only improves prediction accuracy compared to baseline methods, but also provides actionable insights into the environmental dynamics that optimize abalone growth. These findings underscore the potential of advanced machine learning techniques in enhancing aquaculture practices and offer a robust framework for managing complex, multi-variable systems in sustainable farming.

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.

Nho JH, et al. J Nutr Health. 2017 Dec;50(6):595-602. https://doi.org/10.4163/jnh.2017.50.6.595