[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119333-en":3,"doc-seo-119333-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119333,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Applying Supervised Machine Learning for Radiation Dose Accumulation - Master of Science (Tech) Thesis","Predicting radiation doses in nuclear power plants supports radiation safety by helping prevent overly high exposures for workers. This thesis develops a previously unexplored visit-based supervised machine learning approach using time-relational visit data from OL1 and OL2, including measured doses, to predict dose-interval classes. Five models are evaluated (Random Forest, Balanced Random Forest, XGBoost, LightGBM, Easy Ensemble with AdaBoost). LightGBM performs best, but limitations arise from class imbalance and limited dataset descriptiveness. The work analyzes regulatory requirements, radiation exposure characteristics, and deployment prerequisites for production use.","Applying Supervised Machine Learning for Radiation Dose Accumulation  \nUniversity of Turku Department of Computing Master of Science (Tech) Thesis Health Technology  \nFebruary 2025 Valtteri Puumalainen  \nSupervisors:  \nJari Björne, PhD. (University of Turku) Jussi Nieminen, M.Sc. (Teollisuuden Voima Oyj) Eemeli Härmälä, M.Sc. (Teollisuuden Voima Oyj)  \nThe originality of this thesis has been checked in accordance with the University of Turku quality assurance system  \nusing the Turnitin OriginalityCheck service.  \nUNIVERSITY OF TURKU Department of Computing  \nValtteri Puumalainen: Applying Supervised Machine Learning for Radiation Dose Accumulation  \nMaster of Science (Tech) Thesis, 77 p.  \nHealth Technology February 2025  \nPredicting radiation doses in nuclear power plants is a challenging problem for maintaining the radiation safety of workers whilst ensuring that high exposures do not occur. These radiation doses can be predicted using different sensors, measurements or by manually reviewing the dose history of personnel. However, in this study, a previously unexplored visit-based machine learning approach for predicting radiation doses was developed. This approach utilises time relational data on personnel visits to the controlled area of OL1 and OL2 (Olkiluoto Unit 1 and 2) nuclear power plants, including radiation doses measured during these visits. This allows us to predict visits for different interval classes depending on the radiation dose received. To provide a comprehensive foundation for machine learning modeling, we also examined the regulations governing current activities and analysed the nature of radiation exposure in nuclear power plant environments, including the origins and effects of radiation. Finally, we evaluated the prerequisites and considerations for deploying a comparable application in a production environment.  \nThrough a combination of literature and experimental analysis, a basis for machine learning analysis was established, adopting five different models: 1) Random Forest, 2) Balanced Random Forest, 3) XGBoost, 4) LightGBM and 5) Easy Ensemble with AdaBoost. Among the models tested, LightGBM achieved the most promising results, however, its performance fell short of expectations due to the inherent imbalance and lack of descriptiveness in the dataset. While the models demonstrated an ability to learn from the data, this learning was insufficient to effectively distinguish between all class intervals. These limitations emphasise the value of integrating additional contextual information, such as the specific work tasks completed during visits, to enhance the dataset’s descriptiveness and improve the model’s performance. By addressing these limitations, this study highlights the broader potential for data-driven modelling and further research. Specifically, we demonstrate that the descriptiveness and contextual relevance of data are as, if more, important as its quantity, as the mere existence or abundance of data does not guarantee its applicability to similar data-driven methods.  \nKeywords: machine learning, ml, radiation exposure, occupational exposure, radiation dose, radiation, as low as reasonably achievable (ALARA), nuclear powerplant, npp, nuclear energy, crisp-dm  \nAcknowledgements  \nI would like to begin by thanking my supervisors, Jari Björne, Jussi Nieminen and Eemeli Härmälä, for their good and constructive approach to my work. I felt that I was contributing to something meaningful, both for my own career and for the company that made my research possible, Teollisuuden Voima Oyj. In addition, I would also like to express my gratitude to everyone else who has helped me with my work, both with the databases and with the data, tools and systems. Your support has been invaluable and has greatly accelerated my progress over the past year.  \nFurthermore, I am grateful to my previous employers, and my current employer, Teollisuuden Voima Oyj, for enabling me to be here today. The opportu","cbCaijZLG4h7JLsP","https://ap.wps.com/l/cbCaijZLG4h7JLsP","pdf",3888214,1,96,"English","en",105,"# Introduction\n# Research process\n# Radiation and machine learning\n## Legislative approach to dose regulation\n## Nuclear power plant setting\n## Related studies and machine learning\n# Machine learning approach\n## Data\n## Applying machine learning\n## Evaluation\n# Results\n## Evaluation analysis\n## Operational use\n# Conclusions\n# References\n# List of acronyms","[{\"question\":\"What problem does this thesis address in nuclear power plant radiation safety?\",\"answer\":\"It focuses on predicting radiation doses for workers to support maintaining radiation safety and avoid overly high exposures.\"},{\"question\":\"How is the proposed machine learning approach built?\",\"answer\":\"It uses time-relational, visit-based data about personnel visits to the controlled area, including measured radiation doses, to predict dose-interval classes.\"},{\"question\":\"Which models are evaluated and what is the main outcome?\",\"answer\":\"Random Forest, Balanced Random Forest, XGBoost, LightGBM, and Easy Ensemble with AdaBoost are tested; LightGBM achieves the most promising results, but performance is limited by dataset imbalance and low descriptiveness.\"}]","Applying Supervised Machine Learning for Radiation Dose Accumulation - Master of Science (Tech) Thesis | PDF",1785723756,242,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"applying-supervised-machine-learning-for-radiation-dose-accumulation-master-of-science-tech-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/applying-supervised-machine-learning-for-radiation-dose-accumulation-master-of-science-tech-thesis/119333/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this thesis address in nuclear power plant radiation safety?","Question",{"text":75,"@type":76},"It focuses on predicting radiation doses for workers to support maintaining radiation safety and avoid overly high exposures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the proposed machine learning approach built?",{"text":80,"@type":76},"It uses time-relational, visit-based data about personnel visits to the controlled area, including measured radiation doses, to predict dose-interval classes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models are evaluated and what is the main outcome?",{"text":84,"@type":76},"Random Forest, Balanced Random Forest, XGBoost, LightGBM, and Easy Ensemble with AdaBoost are tested; 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