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

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Aristeidis Karras, Christos Karras, Spyros Sioutas, Christos Makris, George Katselis, Ioannis Hatzilygeroudis, John A. Theodorou, Dimitrios Tsolis
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引用次数: 0

Abstract

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.
基于gis的水产养殖疾病传播有效预测强化学习方法
本研究探讨了与专家知识系统相结合的地理信息系统(GIS)的设计和功能,该系统专为跟踪和监测危险疾病在一系列养鱼场的传播而设计。该系统专门针对希腊的水产养殖区,获取与这些养殖场相关的地理和气候数据。该系统的一个特点是它能够计算单个网箱和更广泛的养鱼场实体之间的疾病传播间隔,为传播动态提供重要的见解。然后,这些数据作为我们专家系统的入口点。为了提高预测精度,我们采用了各种机器学习策略,最终专注于强化学习(RL)环境。这一RL框架得到了多臂班迪(MAB)技术的加强,作为有效管理农场内病毒传播流的强大机制而脱颖而出。经验性测试强调了MAB方法的效率,在直接比较中,它始终优于其他算法选项,达到了令人印象深刻的96%的准确率。展望未来的工作,我们计划整合缓冲技术,并深入研究先进的强化学习模型,以增强我们现有的系统。研究结果为水产养殖健康管理预测建模的未来研究奠定了基础,我们的目标是进一步扩展我们的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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