Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin

Descripción del Articulo

En: Proceedings of Hidrology Days, Colorado State University, USA, March 22-24, 2010, American Geophysical Union (AGU), p. 58-68.
Detalles Bibliográficos
Autor: Latínez Sotomayor, Karen Alexandra
Formato: objeto de conferencia
Fecha de Publicación:2010
Institución:Instituto Geofísico del Perú
Repositorio:IGP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.igp.gob.pe:20.500.12816/943
Enlace del recurso:http://hdl.handle.net/20.500.12816/943
Nivel de acceso:acceso abierto
Materia:Weather forecast
Atmospheric precipitation
Atmospheric temperature
Neural networks
Mathematical models
http://purl.org/pe-repo/ocde/ford#1.05.00
http://purl.org/pe-repo/ocde/ford#1.05.09
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dc.title.none.fl_str_mv Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin
title Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin
spellingShingle Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin
Latínez Sotomayor, Karen Alexandra
Weather forecast
Atmospheric precipitation
Atmospheric temperature
Neural networks
Mathematical models
http://purl.org/pe-repo/ocde/ford#1.05.00
http://purl.org/pe-repo/ocde/ford#1.05.09
title_short Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin
title_full Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin
title_fullStr Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin
title_full_unstemmed Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin
title_sort Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin
author Latínez Sotomayor, Karen Alexandra
author_facet Latínez Sotomayor, Karen Alexandra
author_role author
dc.contributor.author.fl_str_mv Latínez Sotomayor, Karen Alexandra
dc.subject.none.fl_str_mv Weather forecast
Atmospheric precipitation
Atmospheric temperature
Neural networks
Mathematical models
topic Weather forecast
Atmospheric precipitation
Atmospheric temperature
Neural networks
Mathematical models
http://purl.org/pe-repo/ocde/ford#1.05.00
http://purl.org/pe-repo/ocde/ford#1.05.09
dc.subject.ocde.none.fl_str_mv http://purl.org/pe-repo/ocde/ford#1.05.00
http://purl.org/pe-repo/ocde/ford#1.05.09
description En: Proceedings of Hidrology Days, Colorado State University, USA, March 22-24, 2010, American Geophysical Union (AGU), p. 58-68.
publishDate 2010
dc.date.accessioned.none.fl_str_mv 2018-03-28T13:17:17Z
dc.date.available.none.fl_str_mv 2018-03-28T13:17:17Z
dc.date.issued.fl_str_mv 2010
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.citation.none.fl_str_mv Latínez, K. A. (2010). Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin. In Hydrology Days 2010, 58-68.
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12816/943
identifier_str_mv Latínez, K. A. (2010). Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin. In Hydrology Days 2010, 58-68.
url http://hdl.handle.net/20.500.12816/943
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licences/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.coverage.spatial.none.fl_str_mv Cuenca del río Mantaro
Perú
dc.publisher.none.fl_str_mv Colorado State University
publisher.none.fl_str_mv Colorado State University
dc.source.none.fl_str_mv reponame:IGP-Institucional
instname:Instituto Geofísico del Perú
instacron:IGP
instname_str Instituto Geofísico del Perú
instacron_str IGP
institution IGP
reponame_str IGP-Institucional
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spelling Latínez Sotomayor, Karen AlexandraCuenca del río MantaroPerú2018-03-28T13:17:17Z2018-03-28T13:17:17Z2010Latínez, K. A. (2010). Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin. In Hydrology Days 2010, 58-68.http://hdl.handle.net/20.500.12816/943En: Proceedings of Hidrology Days, Colorado State University, USA, March 22-24, 2010, American Geophysical Union (AGU), p. 58-68.The Mantaro river basin is an area that is exposed to high climatic variability due to the geography and factors that are not completely known. The agriculture is very important for people who live at and around it. The quality and productivity of the products are related to rainfall and air temperature, commonly farmers sow when their ancestral knowledge indicate it, in many cases the crops were blighted because they have not enough rain or have too low or high temperatures. That is why the farmers need reliable forecasts of precipitation and temperature. This investigation try to do forecast timely and reliable based on global index around the world as predictors. To accomplish this challenge, this investigation used two methods to determine three-month forecasts, using multivariate adaptive regression splines and artificial neural networks backpropagation. Twelve MARS model were estimated for each response variable, each one represent a month. Instead, only one ANNB model was estimated with the same variables because the neural networks need a lot of data. At Huayao; precipitation showed a predictive relative error (PRE) equal to 1.04 for MARS while 2.15 for ANNB; minimum temperature showed a PRE = 0.45 for MARS and PRE = 0.67 for ANNB; maximum temperature showed a PRE = 7.34 for MARS and PRE = 1.41 for ANNB, a high value of PRE for MARS may be due to an unusual value in predictor set at validation stage. At Jauja; the precipitation showed a PRE = 0.62 for MARS and PRE = 1.18 for ANNB; minimum temperature showed a PRE = 0.27 for MARS and PRE = 1.20 for ANNB and; precipitation showed a PRE = 0.66 for MARS and PRE = 3.00 for ANNB. At lower value of PRE, better are the results thus forecasts are more accurate. Then, the validation results showed that MARS models were more accurate than ANNB.application/pdfengColorado State Universityinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licences/by/4.0/Weather forecastAtmospheric precipitationAtmospheric temperatureNeural networksMathematical modelshttp://purl.org/pe-repo/ocde/ford#1.05.00http://purl.org/pe-repo/ocde/ford#1.05.09Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basininfo:eu-repo/semantics/conferenceObjectreponame:IGP-Institucionalinstname:Instituto Geofísico del Perúinstacron:IGPORIGINALLatinez_paperHD2010.pdfLatinez_paperHD2010.pdfapplication/pdf687897https://repositorio.igp.gob.pe/bitstreams/1a32019a-0e19-4bf4-8ee7-9c4f292402b6/downloada8d8611545295be397d40d721bc39120MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8389https://repositorio.igp.gob.pe/bitstreams/808657e9-2644-4687-a810-7e44ef0bb1b7/download930f6bfdae21cbde24d380117f74129cMD52THUMBNAILLatinez_paperHD2010.pdf.jpgLatinez_paperHD2010.pdf.jpgIM Thumbnailimage/jpeg94749https://repositorio.igp.gob.pe/bitstreams/bd20ffb7-4b3f-471f-9827-d0246f4d04a5/download1b1aae32ecf478d4dc2e19be9a7415b2MD53TEXTLatinez_paperHD2010.pdf.txtLatinez_paperHD2010.pdf.txtExtracted texttext/plain16520https://repositorio.igp.gob.pe/bitstreams/184ed95e-ee91-4cce-8923-705dbdef23f8/downloadad18adc6fd79f61acd491340d1e22614MD5420.500.12816/943oai:repositorio.igp.gob.pe:20.500.12816/9432024-10-01 16:35:38.25https://creativecommons.org/licences/by/4.0/info:eu-repo/semantics/openAccessopen.accesshttps://repositorio.igp.gob.peRepositorio Geofísico Nacionalbiblio@igp.gob.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Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin
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