Método para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinas
ilustraciones, diagramas, mapas, tablas
- Autores:
-
Palacio Jiménez, David
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81507
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Aguas lluvias
Rain-water (Water supply)
Desastres
Disasters
Inundaciones
Floods
Avenidas torrenciales
Gestión del riesgo
Aprendizaje de máquinas
Datos abiertos
Variables hidrometeorológicas
Desbalanceo de clases
Machine learning
Debris flow
Risk management
Open data
Hydrometeorological variables
Imbalanced classes
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Método para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinas |
| dc.title.translated.eng.fl_str_mv |
Method for the temporal prediction of debris flows from open data using machine learning |
| title |
Método para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinas |
| spellingShingle |
Método para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinas 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología Aguas lluvias Rain-water (Water supply) Desastres Disasters Inundaciones Floods Avenidas torrenciales Gestión del riesgo Aprendizaje de máquinas Datos abiertos Variables hidrometeorológicas Desbalanceo de clases Machine learning Debris flow Risk management Open data Hydrometeorological variables Imbalanced classes |
| title_short |
Método para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinas |
| title_full |
Método para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinas |
| title_fullStr |
Método para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinas |
| title_full_unstemmed |
Método para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinas |
| title_sort |
Método para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinas |
| dc.creator.fl_str_mv |
Palacio Jiménez, David |
| dc.contributor.advisor.none.fl_str_mv |
Martínez Carvajal, Hernán Eduardo ARISTIZABAL GIRALDO, EDIER VICENTE Branch Bedoya, John Willian |
| dc.contributor.author.none.fl_str_mv |
Palacio Jiménez, David |
| dc.contributor.researchgroup.spa.fl_str_mv |
Investigación en Geología Ambiental Gea Gidia: Grupo de Investigación Y Desarrollo en Inteligencia Artificial |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología |
| topic |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología Aguas lluvias Rain-water (Water supply) Desastres Disasters Inundaciones Floods Avenidas torrenciales Gestión del riesgo Aprendizaje de máquinas Datos abiertos Variables hidrometeorológicas Desbalanceo de clases Machine learning Debris flow Risk management Open data Hydrometeorological variables Imbalanced classes |
| dc.subject.lemb.none.fl_str_mv |
Aguas lluvias Rain-water (Water supply) Desastres Disasters Inundaciones Floods |
| dc.subject.proposal.spa.fl_str_mv |
Avenidas torrenciales Gestión del riesgo Aprendizaje de máquinas Datos abiertos Variables hidrometeorológicas Desbalanceo de clases Machine learning |
| dc.subject.proposal.eng.fl_str_mv |
Debris flow Risk management Open data Hydrometeorological variables Imbalanced classes |
| description |
ilustraciones, diagramas, mapas, tablas |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-06-06T15:48:29Z |
| dc.date.available.none.fl_str_mv |
2022-06-06T15:48:29Z |
| dc.date.issued.none.fl_str_mv |
2022 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/81507 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/81507 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Martínez Carvajal, Hernán Eduardo9f4948ce22565e3d5276eaa769f0112e600ARISTIZABAL GIRALDO, EDIER VICENTE90428ddc90f91c351dec58ca14b30d89600Branch Bedoya, John Willian112eaa0bbeeaeb0d3d14dfe15d672a15600Palacio Jiménez, Davidb8ad861d934f27a4a58328836b3f8365Investigación en Geología Ambiental GeaGidia: Grupo de Investigación Y Desarrollo en Inteligencia Artificial2022-06-06T15:48:29Z2022-06-06T15:48:29Z2022https://repositorio.unal.edu.co/handle/unal/81507Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, mapas, tablasLas avenidas torrenciales son fenómenos destructivos característicos de regiones montañosas. En el departamento de Antioquia (Colombia), estos eventos ocurren con frecuencia y las pérdidas en términos económicos y de vidas humanas reflejan la importancia de predecirlos. Las condiciones climáticas extremas, la expansión urbana y el crecimiento poblacional tienden a incrementar el riesgo en aquellas zonas donde ya se han presentado eventos en el pasado. Actualmente, se carece de una base de datos que recopile el detalle de las avenidas torrenciales que han ocurrido en Antioquia con sus respectivas variables hidrometeorológicas, además, la mayoría de las investigaciones están orientadas a identificar la susceptibilidad espacial de estos fenómenos. Con el auge de las técnicas de aprendizaje de máquinas, se propone un método de clasificación binaria para la predicción temporal de avenidas torrenciales a partir de datos abiertos. De esta manera, se identifican las múltiples fuentes de información para construir un inventario de eventos con sus respectivas variables hidrometeorológicas. Luego se realiza el preprocesamiento y entendimiento profundo de los datos, de manera que se seleccionan las variables que más influencia tienen en la ocurrencia de las avenidas torrenciales mediante métodos de envoltura y de filtrado. Seguidamente, se aborda el problema del desbalanceo entre las clases, usando diferentes proporciones de los datos y generando datos sintéticos para evaluar el desempeño del clasificador propuesto. Por último, se obtiene que el algoritmo de bosques aleatorios con el conjunto de datos balanceado y desbalanceado en una proporción de 1:99 entre las clases de ocurrencia y no ocurrencia de avenida torrencial fue el que mejor desempeño obtuvo, logrando un F1-score y sensibilidad del 85% para el conjunto balanceado, mientras que el conjunto de datos desbalanceado obtuvo 66% y 55% respectivamente. Además, se determina que las variables que mayor influencia tienen en el modelo de clasificación corresponden a la lluvia antecedente de 1 día, la escorrentía, la evapotranspiración potencial y el índice de vegetación baja. (Texto tomado de la fuente)Debris flows are destructive phenomena characteristic of mountainous regions. In the Department of Antioquia (Colombia), these events occur frequently and the losses in economic terms and in human lives reflect the importance of predicting them. Extreme weather conditions, urbanization, and population growth tend to increase the risk in those areas where events have already occurred in the past. Currently, there is a lack of a database that compiles the details of the debris flows that have occurred in Antioquia with their respective hydrometeorological variables, in addition, most of the investigations are aimed at identifying the spatial susceptibility of these phenomena. With the rise of machine learning techniques, a binary classification method is proposed for the temporal prediction of debris flows from open data. In this way, multiple sources of information are identified to build an inventory of events with their respective hydrometeorological variables. Then, the preprocessing and deep understanding of the data is carried out, so that the variables that have the most influence on the occurrence of debris flows are selected through wrapping and filtering methods. Next, the problem of imbalance between classes is addressed, using different proportions of the data and generating synthetic data to evaluate the performance of the proposed classifier. Finally, it is obtained that the random forest algorithm with the balanced and unbalanced data set in a ratio of 1:99 between the classes of occurrence and non-occurrence of debris flows was the one that obtained the best performance, achieving an F1-score and sensitivity of 85% for the balanced set, while the unbalanced data set obtained 66% and 55% respectively. In addition, it is determined that the variables that have the greatest influence on the classification model correspond to the antecedent rainfall of 1 day, runoff, potential evapotranspiration, and the low vegetation index.MaestríaMagíster en Ingeniería - AnalíticaÁrea Curricular de Ingeniería de Sistemas e Informáticaxiii, 88 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaDepartamento de la Computación y la DecisiónFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación550 - Ciencias de la tierra::551 - Geología, hidrología, meteorologíaAguas lluviasRain-water (Water supply)DesastresDisastersInundacionesFloodsAvenidas torrencialesGestión del riesgoAprendizaje de máquinasDatos abiertosVariables hidrometeorológicasDesbalanceo de clasesMachine learningDebris flowRisk managementOpen dataHydrometeorological variablesImbalanced classesMétodo para la predicción temporal de avenidas torrenciales a partir de datos abiertos usando aprendizaje de máquinasMethod for the temporal prediction of debris flows from open data using machine learningTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAchour, Y., Gar¸cia, S., y Cavaleiro, V. 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Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA, 442–452. doi: 10.1109/DSAA.2019.00059Universidad Nacional de ColombiaEstudiantesInvestigadoresMaestrosORIGINAL1039463302.2022.pdf1039463302.2022.pdfTesis de Maestría en Ingeniería - Analíticaapplication/pdf15456574https://repositorio.unal.edu.co/bitstream/unal/81507/3/1039463302.2022.pdfd6719d6330f20251513b926c8f5334a0MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81507/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1039463302.2022.pdf.jpg1039463302.2022.pdf.jpgGenerated Thumbnailimage/jpeg4109https://repositorio.unal.edu.co/bitstream/unal/81507/4/1039463302.2022.pdf.jpga8fc18a60d969ac508cf7fd1cde5417aMD54unal/81507oai:repositorio.unal.edu.co:unal/815072023-10-06 16:34:23.705Repositorio Institucional Universidad Nacional de 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