Towards explainable and physically-based deep learning statistical downscaling methods

To design strategies against climate change, it is crucial to have projections generated by climate models. Unfortunately, the coarse resolution of these models poses challenges in simulating variables at the local scale. To overcome this limitation, statistical downscaling uses statistical models t...

Descripción completa

Detalles Bibliográficos
Autor: González Abad, José|||0000-0002-0764-3872
Tipo de recurso: tesis doctoral
Fecha de publicación:2024
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/32096
Acceso en línea:https://hdl.handle.net/10902/32096
Access Level:acceso abierto
Palabra clave:Statistical downscaling
Deep learning
Explainable artificial intelligence
Cluster computing
Downscaling estadístico
Aprendizaje profundo
Explicabilidad
Computación
id ES_2adda1806384a63abe2e75fc34bc7abc
oai_identifier_str oai:repositorio.unican.es:10902/32096
network_acronym_str ES
network_name_str España
repository_id_str
spelling Towards explainable and physically-based deep learning statistical downscaling methodsDowsncaling estadístico con aprendizaje profundo: explicabilidad y consistencia físicaGonzález Abad, José|||0000-0002-0764-3872Statistical downscalingDeep learningExplainable artificial intelligenceCluster computingDownscaling estadísticoAprendizaje profundoExplicabilidadComputaciónTo design strategies against climate change, it is crucial to have projections generated by climate models. Unfortunately, the coarse resolution of these models poses challenges in simulating variables at the local scale. To overcome this limitation, statistical downscaling uses statistical models to understand the relationship between local and large-scale variables unaffected by this coarse resolution. Although deep learning models have emerged as a promising technique to learn this relationship, they are considered black-boxes as the learned relationships are challenging to comprehend, leading to mistrust among experts. To overcome this limitation, this Thesis explores eXplainable Artificial Intelligence techniques to assess these models in terms of the learned relationships, achieving a more robust and physically-based validation of deep learning models for statistical downscaling, as well as aligning the resultant projections with some fundamental physical constraints. In addition to the application of these diagnostics to a real use-case in Iberia in the context of the Escenario-PNACC initiative, some computational aspects involved in this process are also explored.Para diseñar estrategias frente al cambio climático, es crucial contar con proyecciones generadas por modelos climáticos. Sin embargo, la baja resolución de estos modelos plantea desafíos en la simulación de variables a escala local. Para superar esta limitación, el downscaling estadístico emplea modelos estadísticos para aprender la relación entre variables locales y variables de larga escala no afectadas por la baja resolución. Aunque los modelos de aprendizaje profundo han surgido como una técnica prometedora para captar esta relación, se consideran "cajas negras" debido a que las relaciones aprendidas son difíciles de comprender, generando desconfianza entre los expertos. A fin de superar esta limitación, en esta Tesis se exploran técnicas basadas en eXplainable Artificial Intelligence para evaluar los modelos de aprendizaje profundo en términos de las relaciones aprendidas, logrando una validación más robusta y con consistencia fı́sica de estos modelos aplicados al downscaling estadístico, así como alinear las proyecciones resultantes con algunas restricciones fı́sicas fundamentales. Además de la aplicación de estos diagnósticos a un caso de uso real en Iberia en el contexto de la iniciativa Escenario-PNACC, también se exploran algunos aspectos computacionales implicados en este proceso.Gutiérrez Llorente, José ManuelLópez García, ÁlvaroUniversidad de Cantabria20242024-02-22doctoral thesishttp://purl.org/coar/resource_type/c_db06NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/doctoralThesishttps://hdl.handle.net/10902/32096reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/320962026-06-02T12:39:31Z
dc.title.none.fl_str_mv Towards explainable and physically-based deep learning statistical downscaling methods
Dowsncaling estadístico con aprendizaje profundo: explicabilidad y consistencia física
title Towards explainable and physically-based deep learning statistical downscaling methods
spellingShingle Towards explainable and physically-based deep learning statistical downscaling methods
González Abad, José|||0000-0002-0764-3872
Statistical downscaling
Deep learning
Explainable artificial intelligence
Cluster computing
Downscaling estadístico
Aprendizaje profundo
Explicabilidad
Computación
title_short Towards explainable and physically-based deep learning statistical downscaling methods
title_full Towards explainable and physically-based deep learning statistical downscaling methods
title_fullStr Towards explainable and physically-based deep learning statistical downscaling methods
title_full_unstemmed Towards explainable and physically-based deep learning statistical downscaling methods
title_sort Towards explainable and physically-based deep learning statistical downscaling methods
dc.creator.none.fl_str_mv González Abad, José|||0000-0002-0764-3872
author González Abad, José|||0000-0002-0764-3872
author_facet González Abad, José|||0000-0002-0764-3872
author_role author
dc.contributor.none.fl_str_mv Gutiérrez Llorente, José Manuel
López García, Álvaro
Universidad de Cantabria
dc.subject.none.fl_str_mv Statistical downscaling
Deep learning
Explainable artificial intelligence
Cluster computing
Downscaling estadístico
Aprendizaje profundo
Explicabilidad
Computación
topic Statistical downscaling
Deep learning
Explainable artificial intelligence
Cluster computing
Downscaling estadístico
Aprendizaje profundo
Explicabilidad
Computación
description To design strategies against climate change, it is crucial to have projections generated by climate models. Unfortunately, the coarse resolution of these models poses challenges in simulating variables at the local scale. To overcome this limitation, statistical downscaling uses statistical models to understand the relationship between local and large-scale variables unaffected by this coarse resolution. Although deep learning models have emerged as a promising technique to learn this relationship, they are considered black-boxes as the learned relationships are challenging to comprehend, leading to mistrust among experts. To overcome this limitation, this Thesis explores eXplainable Artificial Intelligence techniques to assess these models in terms of the learned relationships, achieving a more robust and physically-based validation of deep learning models for statistical downscaling, as well as aligning the resultant projections with some fundamental physical constraints. In addition to the application of these diagnostics to a real use-case in Iberia in the context of the Escenario-PNACC initiative, some computational aspects involved in this process are also explored.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-02-22
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/10902/32096
url https://hdl.handle.net/10902/32096
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405099248320512
score 15.300724

Similares en LA Referencia