{"id":1390,"date":"2025-02-22T16:19:56","date_gmt":"2025-02-22T19:19:56","guid":{"rendered":"https:\/\/datamining.dc.uba.ar\/datamining\/?p=1390"},"modified":"2025-02-27T12:29:25","modified_gmt":"2025-02-27T15:29:25","slug":"defensa-de-tesis-luciana-quarracino-16-12-2024-11-hs","status":"publish","type":"post","link":"https:\/\/datamininguba.com.ar\/datamining\/2025\/02\/22\/defensa-de-tesis-luciana-quarracino-16-12-2024-11-hs\/","title":{"rendered":"Defensa de Tesis \u2013 Luciana Quarracino \u2013 27\/02\/2025 \u2013 11 hs."},"content":{"rendered":"<p>El Jueves 27\/02 se realiz\u00f3 la Defensa de Tesis de Luciana Quarracino titulada \u201cCaracterizaci\u00f3n y modelado de eventos extremos en estaciones centenarias de las cuencas del Paran\u00e1, Uruguay y Limay\u201d.\u00a0 Cont\u00f3 con la direcci\u00f3n de la Dra. Melanie Meis y la co-direcci\u00f3n del Dr. Maximiliano Camporino. Los jurados fueron la Dra. Marcela H. Gonz\u00e1lez, Dr. Federico Pousa y Dr. Guillermo Solovey. La defensa puede verse a trav\u00e9s del siguiente link: <a href=\"https:\/\/youtu.be\/MsbVMTy-ljI?t=2\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.youtube.com\/live\/MsbVMTy-ljI&amp;source=gmail&amp;ust=1738887579966000&amp;usg=AOvVaw1TPDFPf5oB4J-0UHWU2TeY\">https:\/\/www.youtube.com\/live\/<wbr \/>MsbVMTy-ljI<\/a><\/p>\n<p>Resumen:<\/p>\n<p>La presente tesis se centr\u00f3 en el estudio de los r\u00edos Paran\u00e1, Limay y Uruguay analizando los patrones temporales de las series de caudal con el objetivo de agrupar las cr\u00f3nicas hidrol\u00f3gicas y pronosticar el comportamiento futuro de las cuencas.<\/p>\n<p>En una primera instancia se realiz\u00f3 un an\u00e1lisis exploratorio de datos para cada r\u00edo y se analizaron los cambios en las tendencias hist\u00f3ricas. As\u00ed mismo, se encontraron correlaciones entre los meses, trimestres y medidas descriptivas de las cr\u00f3nicas hidrol\u00f3gicas. Adem\u00e1s se identificaron patrones comunes en los reg\u00edmenes de los r\u00edos y se utiliz\u00f3 la t\u00e9cnica de agrupamiento K-means que permiti\u00f3 clasificar las cr\u00f3nicas en cuatro cl\u00fasteres de acuerdo a caracter\u00edsticas comunes relacionadas con la humedad de las cr\u00f3nicas y la temporalidad de esa humedad dentro del a\u00f1o.<\/p>\n<p>Finalmente, los modelos de pron\u00f3stico aplicados mostraron que los \u00e1rboles de decisi\u00f3n son efectivos para predecir a partir de variables del primer trimestre, mientras que la regresi\u00f3n log\u00edstica con penalizaci\u00f3n Ridge es m\u00e1s adecuada para los casos en los que se cuenta tambi\u00e9n con informaci\u00f3n del segundo y tercer trimestre. Adem\u00e1s, se evalu\u00f3 la influencia del ENSO en el comportamiento de las cr\u00f3nicas, concluyendo que en este caso no aporta informaci\u00f3n significativa para mejorar los modelos de pron\u00f3stico.<\/p>\n<p>Estos resultados podr\u00edan replicarse en otras cuencas del pa\u00eds, permitiendo un uso m\u00e1s eficiente de los recursos h\u00eddricos y un mejor manejo anticipatorio de los mismos. As\u00ed mismo, abre la posibilidad de investigar otras oscilaciones clim\u00e1ticas y per\u00edodos de tiempo para mejorar la precisi\u00f3n de los modelos de pron\u00f3stico.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>El Jueves 27\/02 se realiz\u00f3 la Defensa de Tesis de [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":43,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1390","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categoria"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Defensa de Tesis \u2013 Luciana Quarracino \u2013 27\/02\/2025 \u2013 11 hs. - Maestr\u00eda en Data Mining<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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