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, t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Online Access: | https://www.mdpi.com/2078-2489/14/11/583 |
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| author | Aristeidis Karras Christos Karras Spyros Sioutas Christos Makris George Katselis Ioannis Hatzilygeroudis John A. Theodorou Dimitrios Tsolis |
| author_facet | Aristeidis Karras Christos Karras Spyros Sioutas Christos Makris George Katselis Ioannis Hatzilygeroudis John A. Theodorou Dimitrios Tsolis |
| author_sort | Aristeidis Karras |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:44:49Z |
| format | Article |
| id | doaj.art-4025562608b84921bd54ce038b5076eb |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T16:44:49Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-4025562608b84921bd54ce038b5076eb2023-11-24T14:48:10ZengMDPI AGInformation2078-24892023-10-01141158310.3390/info14110583An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in AquacultureAristeidis Karras0Christos Karras1Spyros Sioutas2Christos Makris3George Katselis4Ioannis Hatzilygeroudis5John A. Theodorou6Dimitrios Tsolis7Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, GreeceDepartment of Computer Engineering and Informatics, University of Patras, 26504 Patras, GreeceDepartment of Computer Engineering and Informatics, University of Patras, 26504 Patras, GreeceDepartment of Computer Engineering and Informatics, University of Patras, 26504 Patras, GreeceDepartment of Fisheries and Aquaculture, University of Patras, 30200 Mesolongi, GreeceDepartment of Computer Engineering and Informatics, University of Patras, 26504 Patras, GreeceDepartment of Fisheries and Aquaculture, University of Patras, 30200 Mesolongi, GreeceDepartment of History and Archaeology, University of Patras, 26504 Patras, GreeceThis 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.https://www.mdpi.com/2078-2489/14/11/583Geographical Information Systemsreinforcement learningQ-LearningMulti-Armed Banditdisease transmissionaquaculture |
| spellingShingle | Aristeidis Karras Christos Karras Spyros Sioutas Christos Makris George Katselis Ioannis Hatzilygeroudis John A. Theodorou Dimitrios Tsolis An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture Information Geographical Information Systems reinforcement learning Q-Learning Multi-Armed Bandit disease transmission aquaculture |
| title | An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture |
| title_full | An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture |
| title_fullStr | An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture |
| title_full_unstemmed | An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture |
| title_short | An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture |
| title_sort | integrated gis based reinforcement learning approach for efficient prediction of disease transmission in aquaculture |
| topic | Geographical Information Systems reinforcement learning Q-Learning Multi-Armed Bandit disease transmission aquaculture |
| url | https://www.mdpi.com/2078-2489/14/11/583 |
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