{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/autoencoder-driven-weather-clustering-for","title":"Autoencoder-Driven Weather Clustering for Source Estimation during Nuclear Events","arxiv_id":"1709.05840","date":"2017-09-18","proceeding":null,"authors":["I. A. Klampanos","A. Davvetas","S. Andronopoulos","C. Pappas","A. Ikonomopoulos","V. Karkaletsis"],"abstract":"Emergency response applications for nuclear or radiological events can be\nsignificantly improved via deep feature learning due to the hidden complexity\nof the data and models involved. In this paper we present a novel methodology\nfor rapid source estimation during radiological releases based on deep feature\nextraction and weather clustering. Atmospheric dispersions are then calculated\nbased on identified predominant weather patterns and are matched against\nsimulated incidents indicated by radiation readings on the ground. We evaluate\nthe accuracy of our methods over multiple years of weather reanalysis data in\nthe European region. We juxtapose these results with deep classification\nconvolution networks and discuss advantages and disadvantages.","url_abs":"http://arxiv.org/abs/1709.05840v2","url_pdf":"http://arxiv.org/pdf/1709.05840v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"autoencoder-driven-weather-clustering-for","repo_url":"https://github.com/davidath/ncsr-atmo-learn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}