Desarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianas

181 páginas

Autores:
Cardozo Vásquez, Andrés
Tipo de recurso:
Fecha de publicación:
2012
Institución:
Universidad de la Sabana
Repositorio:
Repositorio Universidad de la Sabana
Idioma:
spa
OAI Identifier:
oai:intellectum.unisabana.edu.co:10818/9320
Acceso en línea:
http://hdl.handle.net/10818/9320
Palabra clave:
Zonas climáticas -- Colombia
Clima -- Colombia
Climatología -- Colombia
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http://purl.org/coar/access_right/c_abf2
id REPOUSABA2_d167e3adb57abe9e36718829c35d3f83
oai_identifier_str oai:intellectum.unisabana.edu.co:10818/9320
network_acronym_str REPOUSABA2
network_name_str Repositorio Universidad de la Sabana
repository_id_str
dc.title.es_CO.fl_str_mv Desarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianas
title Desarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianas
spellingShingle Desarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianas
Magíster en Diseño y Gestión de Procesos
Zonas climáticas -- Colombia
Clima -- Colombia
Climatología -- Colombia
title_short Desarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianas
title_full Desarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianas
title_fullStr Desarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianas
title_full_unstemmed Desarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianas
title_sort Desarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianas
dc.creator.fl_str_mv Cardozo Vásquez, Andrés
author Magíster en Diseño y Gestión de Procesos
author_facet Magíster en Diseño y Gestión de Procesos
author_role author
dc.contributor.advisor.none.fl_str_mv Gaitán Ospina, Carlos Felipe
Agudelo Otálora, Luis Mauricio
dc.contributor.author.none.fl_str_mv Cardozo Vásquez, Andrés
dc.contributor.author.fl_str_mv Magíster en Diseño y Gestión de Procesos
dc.subject.none.fl_str_mv Zonas climáticas -- Colombia
Clima -- Colombia
Climatología -- Colombia
topic Zonas climáticas -- Colombia
Clima -- Colombia
Climatología -- Colombia
description 181 páginas
publishDate 2012
dc.date.issued.none.fl_str_mv 2012
dc.date.accessioned.none.fl_str_mv 2013-12-16T14:14:08Z
dc.date.available.none.fl_str_mv 2013-12-16T14:14:08Z
dc.date.created.none.fl_str_mv 2013-12-162
dc.type.none.fl_str_mv masterThesis
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_bdcc
dc.type.local.none.fl_str_mv Tesis de maestría
dc.type.hasVersion.none.fl_str_mv publishedVersion
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dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10818/9320
dc.identifier.local.none.fl_str_mv 256456
TE06209
identifier_str_mv Abdel-Aal, R. E. (2004). "Hourly temperature forecasting using abductive networks." Engineering Applications of Artificial Intelligence 17(5): 543-556.
Aksornsingchai, P. and C. Srinilta (2011). Statistical downscaling for rainfall and temperature prediction in Thailand.
Aminian, A. (2010). "Prediction of temperature elevation for seawater in multi-stage flash desalination plants using radial basis function neural network." Chemical Engineering Journal 162(2): 552-556.
Anderson, J. A. and E. Rosenfeld (1987). Neurocomputing. Cambrindge, MIT press
Balaghi, R., B. Tychon, H. Eerens and M. Jlibene (2008). "Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco." International Journal of Applied Earth Observation and Geoinformation 10(4): 438-452.
DkcŽqdt¦gyumk." K0" *422:+0" $Pgwtcn" oqfgnkpi" qh" tgncvkxg" ckt" jwokfkv{0$" Computers and Electronics in Agriculture 60(1): 1-7
Bishop, C. M. (1995). Neural Networks for Pattern Recognition. New York, Oxford University Press Inc.
Bradley, J. B. (1995). "Neural networks: A comprehensive foundation: S. HAYKIN. New York: Macmillan College (IEEE Press Book) (1994). v + 696 pp. ISBN 0-02-352761-7." Information Processing & Management 31(5): 786.
Bronstert, A., D. Niehoff and G. Burger (2002). "Effects of climate and land-use change on storm runoff generation: present knowledge and modelling capabilities." Hydrological Processes 16(2): 509-529.
Cannon, A. J. (2011). "Quantile regression neural networks: Implementation in R and application to precipitation downscaling." Computers and Geosciences 37(9): 1277-1284.
Cao, Q., B. T. Ewing and M. A. Thompson (2012). "Forecasting wind speed with recurrent neural networks." European Journal of Operational Research 221(1): 148-154.
Cavazos, T. (1999). "Large-scale circulation anomalies conducive to extreme precipitation events and derivation of daily rainfall in northeastern Mexico and southeastern Texas." Journal of Climate 12(5 II): 1506-1523.
Chattopadhyay, S., D. Jhajharia and G. Chattopadhyay (2011). "Univariate modelling of monthly maximum temperature time series over northeast India: Neural network versus Yule-Walker equation based approach." Meteorological Applications 18(1): 70-82.
Chronopoulos, K., A. Kamoutsis, A. Matsoukis and E. Manoli (2012). "An artificial neural network model application for the estimation of thermal comfort conditions in mountainous regions, Greece." Atmosfera 25(2): 171-181
Compostela , B. (2003). Análisis de Regresión Lineal. Madrid, Universidad Complutense de Madrid.
Coulibaly, P. (2010). "Reservoir Computing approach to Great Lakes water level forecasting." Journal of Hydrology 381(1-2): 76-88.
Coulibaly, P. and N. D. Evora (2007). "Comparison of neural network methods for infilling missing daily weather records." Journal of Hydrology 341(1-2): 27-41.
Crane, R. G. and B. C. Hewitson (1998). "Doubled CO2 precipitation changes for the Susquehanna basin: Down-scaling from the GENESIS general circulation model." International Journal of Climatology 18(1): 65-76
Daly, C., R. P. Neilson and D. L. Phillips (1994). "A statistical-topographic model for mapping climatological precipitation over mountainous terrain." Journal of Applied Meteorology 33(2): 140-158.
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Journal of Climate 12(8 PART 2): 2474-2489.http://hdl.handle.net/10818/9320256456TE06209181 páginasSe desarrolló un modelo basado en redes neuronales artificiales (RNA) para el pronóstico de la temperatura media diaria a escala local en 5 zonas climáticas de Colombia. Se probaron perceptrones multicapa (MLP), redes recurrentes (RN), Generalized Feedforward (GFF), Time Lagged Recurrent Networks (TLRN), Time Delayed Neural Networks (TDNN) y Radial Basis Function (RBF). Se encontraron modelos RNA que superaron métodos lineales y que simularon mejor los datos de anomalías de la temperatura media diaria que el reanálisis NCEP/NCAR. Posteriormente se hizo una proyección de la temperatura media diaria en el periodo del 1 de enero de 2001 al 31 de diciembre de 2100 bajo los escenarios A2 y A1B descritos por el Panel Intergubernamental sobre el Cambio Climático. Nota: Para consultar la carta de autorización de publicación de este documento por favor copie y pegue el siguiente enlace en su navegador de internet: http://hdl.handle.net/10818/9321spaUniversidad de La SabanaMaestría en Diseño y Gestión de ProcesosFacultad de IngenieríaUniversidad de La SabanaIntellectum Repositorio Universidad de La SabanaZonas climáticas -- ColombiaClima -- ColombiaClimatología -- ColombiaDesarrollo de un modelo de red neuronal artificial para la reducción de escala (downscaling) de datos de temperatura del modelo Climático Global Canadiense 3.1 a estaciones meteorológicas colombianasmasterThesisTesis de maestríapublishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_bdcchttp://purl.org/coar/access_right/c_abf2ORIGINALAndrés Cardozo Vásquez (TESIS).pdfAndrés Cardozo Vásquez (TESIS).pdfVer documento en PDFapplication/pdf9691628https://intellectum.unisabana.edu.co/bitstream/10818/9320/1/Andr%c3%a9s%20Cardozo%20V%c3%a1squez%20%28TESIS%29.pdfa58224851c830cc7ba1397082f7c3a93MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8498https://intellectum.unisabana.edu.co/bitstream/10818/9320/2/license.txtf52a2cfd4df262e08e9b300d62c85cabMD52Andrés Cardozo Vásquez (carta).pdfAndrés Cardozo Vásquez (carta).pdfapplication/pdf1180893https://intellectum.unisabana.edu.co/bitstream/10818/9320/4/Andr%c3%a9s%20Cardozo%20V%c3%a1squez%20%28carta%29.pdf600c381283571cb7ea0f4b398f56f7f9MD54TEXTAndrés Cardozo Vásquez (TESIS).pdf.txtAndrés Cardozo Vásquez (TESIS).pdf.txtExtracted Texttext/plain183https://intellectum.unisabana.edu.co/bitstream/10818/9320/3/Andr%c3%a9s%20Cardozo%20V%c3%a1squez%20%28TESIS%29.pdf.txt9e0c344161b57545a702ed675b6f5910MD5310818/9320oai:intellectum.unisabana.edu.co:10818/93202019-10-15 10:22:20.324Intellectum Universidad de la Sabanacontactointellectum@unisabana.edu.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