[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128107-en":3,"doc-seo-128107-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128107,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Acoustic Fish Telemetry and Machine Learning in Ocean Farm 1 - Master’s thesis","Despite high-efficiency salmon production, further optimization requires collecting more process data at both population and individual levels. A thesis framework for Precision Fish Farming (PFF) is applied through Acoustic Fish Telemetry (AFT), enabling acquisition of individual behavioral data using acoustic sensor transmitter tags and a receiver system. The study performs exploratory data analysis and builds machine learning models relating acoustic telemetry to key environmental parameters, revealing strong behavior-environment correlations.","Master’s thesis  \nNT NU  \nNorwegian Un iversity of Science and Technology  \nFaculty of Information Techno logy and Electrical Engineering Department of Engineering Cybernetics  \nØyvind K. Høgseth  \nAcoustic Fish Telemetry and Machine Learning in Ocean Farm 1  \nMaster’s thesis in Industrial Cybernetics Supervisor: Professor Jo Arve Alfredsen June 2024  \nØyvind K. Høgseth  \nAcoustic Fish Telemetry and Machine Learning in Ocean Farm 1  \nMaster’s thesis in Industrial Cybernetics Supervisor: Professor Jo Arve Alfredsen June 2024  \nNorwegian University of Science and Technology  \nFaculty of Information Technology and Electrical Engineering Department of Engineering Cybernetics  \nAbstract  \nDespite the high efficiency of current salmon production practices, substantial potential for further optimization and improvement remains. Achieving this requires gathering more data and insights about processes at the population and individual levels. A detailed understanding of individual responses to various management regimes, operations, and environmental conditions, along with the variations in these responses, could enable more precise farm management practices, enhancing both growth and welfare. Precision Fish Farming (PFF) offers a framework that applies control-engineering principles to fish production. One promising PFF application is Acoustic Fish Telemetry (AFT), which acquires individual behavioral data from fish using acoustic sensor transmitter tags and a receiver system.  \nThis master’s thesis investigates the integration of acoustic fish telemetry and machine learning techniques within Ocean Farm 1 to enhance the understanding of fish behavior in aquaculture. The primary objective was to conduct a comprehensive Exploratory Data Analysis (EDA) and develop machine learning models to analyze acoustic telemetry data alongside various environmental parameters to find relationships and gain insights into salmon behavior.  \nSignificant behavioral patterns were identified from the EDA, revealing strong correlations between salmon behavior and environmental conditions. Key findings indicated that current strength and direction influenced horizontal positioning, while wave height significantly affected vertical positioning. Dimensionality reduction techniques, such as PCA and UMAP, were utilized to identify clusters within the data, further analyzed using K-means clustering, revealing distinct day and night behavior patterns.  \nThe predictive model was a Random Forest Regressor, which demonstrated promising capabilities in forecasting behavioral changes. The model successfully captured 87 % of the variance in the salmon depth but was less effective for horizontal positioning, capturing only 16 % of the variance. This discrepancy highlights the challenges in predicting 2D positions due to the complex interactions between multiple environmental parameters and the expansive area of OF1 .  \nThe analysis clearly emphasized individual differences among salmon, with the model’s performance varying significantly across different fish. Critical environmental parameters, such as oxygen saturation, current vectors, temperature, and wave height, were crucial for predicting horizontal and vertical behaviors.  \nThe thesis underscores the potential of integrating acoustic telemetry with machine learning to enable real-time monitoring and predictive analytics in aquaculture. This approach may facilitate early intervention and more informed decision-making, contributing to improved efficiency, sustainability, and fish welfare in the aquaculture environment. Future research should focus on incorporating larger datasets with additional environmental parameters and leveraging advanced deep-learning techniques to enhance these models’ predictive performance.  \nSammendrag  \nTil tross for den høye effektiviteten i dagens lakseproduksjon, gjenstår det betydelig potensial for videre optimalisering og forbedring. For å oppnå dette kreves det innsamling av mer data","cbCaidGWOBbzPugG","https://ap.wps.com/l/cbCaidGWOBbzPugG","pdf",22036553,4,1,163,"English","en",105,"# Abstract\n## Exploratory data analysis (EDA)\n## Modeling approach and dimensionality reduction\n## Key findings and predictive performance\n## Implications and future work","[{\"question\":\"What is the main goal of the thesis in Ocean Farm 1?\",\"answer\":\"To integrate acoustic fish telemetry and machine learning to analyze salmon behavior using exploratory data analysis and predictive models linked with environmental parameters.\"},{\"question\":\"Which environmental factors most strongly influenced salmon positioning?\",\"answer\":\"Current strength and direction affected horizontal positioning, while wave height significantly affected vertical positioning.\"},{\"question\":\"How effective was the Random Forest Regressor for prediction?\",\"answer\":\"It captured about 87% of variance in salmon depth but only about 16% for horizontal positioning, highlighting difficulties predicting 2D positions from multiple interacting environmental parameters.\"}]","Acoustic Fish Telemetry and Machine Learning in Ocean Farm 1 - 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