An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture

This study explores the design and capabilities of a Geographic Information System (GIS) incorporated with an expert knowledge system, tailored for tracking and monitoring the spread of dangerous diseases across a collection of fish farms. Specifically targeting the aquacultural regions of Greece, the system captures geographical and climatic data pertinent to these farms. A feature of this system is its ability to calculate disease transmission intervals between individual cages and broader fish farm entities, providing crucial insights into the spread dynamics. These data then act as an entry point to our expert system. To enhance the predictive precision, we employed various machine learning strategies, ultimately focusing on a reinforcement learning (RL) environment. This RL framework, enhanced by the Multi-Armed Bandit (MAB) technique, stands out as a powerful mechanism for effectively managing the flow of virus transmissions within farms. Empirical tests highlight the efficiency of the MAB approach, which, in direct comparisons, consistently outperformed other algorithmic options, achieving an impressive accuracy rate of 96%. Looking ahead to future work, we plan to integrate buffer techniques and delve deeper into advanced RL models to enhance our current system. The results set the stage for future research in predictive modeling within aquaculture health management, and we aim to extend our research even further.