Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina

Autores
Teverovsky Korsic, Sofia Andrea; Notarnicola. Claudia; Uriburu Quirno, Marcelo; Cara Ramirez, Leandro Javier
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the semi-arid Central Andes of Argentina, the water from snowmelt runoff plays a fundamental role as a provider of ecosystem services. Nowadays, the global climate change has an observable negative impact on this area, due, principally, to the decrease in both liquid and solid rainfall, with the consequent decrease in water availability. In this context, runoff prediction acquires vital importance for the integrated water resources management. The aim of this study is to investigate the performance of the Support Vector Regression (SVR) technique in predicting monthly discharges with 1-month lead-time in the Tupungato River basin in the Central Andes of Argentina. This methodology has never been applied before in this mountainous region. Different inputs, like meteorological data and satellite-based snow cover area estimates from MODIS, were analyzed in order to identify the suitable inputs predictors to forecast monthly streamflow. The results were compared against the results derived from a Classification and Regression Tree (CART) model and, also, against an Auto-regressive Integrated Moving-average (ARIMA) model. Different metrics were used to evaluate the performance of the SVR tests in reproducing streamflow observations at the basin outlet. The coefficient of determination for each of the analyzed tests lays between 0.75 and 0.89 in the validation set. The comparison with the other models showed a significant improvement in performance of SVR in respect of CART and ARIMA model. SVR models proved a promising approach to support water management and decision making for productive activities, potentially also in other basins in the region.
Fil: Teverovsky Korsic, Sofia Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comision Nacional de Actividades Espaciales; Argentina. Universidad Nacional de Luján; Argentina
Fil: Notarnicola. Claudia. European Academy of Bozen; Italia
Fil: Uriburu Quirno, Marcelo. Comision Nacional de Actividades Espaciales; Argentina
Fil: Cara Ramirez, Leandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
Materia
Support Vector Regression
Runoff Prediction
Remote Sensing
Machine learning techniques
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/233024
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/233024
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of ArgentinaTeverovsky Korsic, Sofia AndreaNotarnicola. ClaudiaUriburu Quirno, MarceloCara Ramirez, Leandro JavierSupport Vector RegressionRunoff PredictionRemote SensingMachine learning techniqueshttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1In the semi-arid Central Andes of Argentina, the water from snowmelt runoff plays a fundamental role as a provider of ecosystem services. Nowadays, the global climate change has an observable negative impact on this area, due, principally, to the decrease in both liquid and solid rainfall, with the consequent decrease in water availability. In this context, runoff prediction acquires vital importance for the integrated water resources management. The aim of this study is to investigate the performance of the Support Vector Regression (SVR) technique in predicting monthly discharges with 1-month lead-time in the Tupungato River basin in the Central Andes of Argentina. This methodology has never been applied before in this mountainous region. Different inputs, like meteorological data and satellite-based snow cover area estimates from MODIS, were analyzed in order to identify the suitable inputs predictors to forecast monthly streamflow. The results were compared against the results derived from a Classification and Regression Tree (CART) model and, also, against an Auto-regressive Integrated Moving-average (ARIMA) model. Different metrics were used to evaluate the performance of the SVR tests in reproducing streamflow observations at the basin outlet. The coefficient of determination for each of the analyzed tests lays between 0.75 and 0.89 in the validation set. The comparison with the other models showed a significant improvement in performance of SVR in respect of CART and ARIMA model. SVR models proved a promising approach to support water management and decision making for productive activities, potentially also in other basins in the region.Fil: Teverovsky Korsic, Sofia Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comision Nacional de Actividades Espaciales; Argentina. Universidad Nacional de Luján; ArgentinaFil: Notarnicola. Claudia. European Academy of Bozen; ItaliaFil: Uriburu Quirno, Marcelo. Comision Nacional de Actividades Espaciales; ArgentinaFil: Cara Ramirez, Leandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaElsevier2023-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/233024Teverovsky Korsic, Sofia Andrea; Notarnicola. Claudia; Uriburu Quirno, Marcelo; Cara Ramirez, Leandro Javier; Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina; Elsevier; Environmental Challenges; 10; 1-2023; 1-92667-0100CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2667010023000033info:eu-repo/semantics/altIdentifier/doi/10.1016/j.envc.2023.100680info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:39:15Zoai:ri.conicet.gov.ar:11336/233024instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:39:15.7CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina
title Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina
spellingShingle Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina
Teverovsky Korsic, Sofia Andrea
Support Vector Regression
Runoff Prediction
Remote Sensing
Machine learning techniques
title_short Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina
title_full Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina
title_fullStr Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina
title_full_unstemmed Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina
title_sort Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina
dc.creator.none.fl_str_mv Teverovsky Korsic, Sofia Andrea
Notarnicola. Claudia
Uriburu Quirno, Marcelo
Cara Ramirez, Leandro Javier
author Teverovsky Korsic, Sofia Andrea
author_facet Teverovsky Korsic, Sofia Andrea
Notarnicola. Claudia
Uriburu Quirno, Marcelo
Cara Ramirez, Leandro Javier
author_role author
author2 Notarnicola. Claudia
Uriburu Quirno, Marcelo
Cara Ramirez, Leandro Javier
author2_role author
author
author
dc.subject.none.fl_str_mv Support Vector Regression
Runoff Prediction
Remote Sensing
Machine learning techniques
topic Support Vector Regression
Runoff Prediction
Remote Sensing
Machine learning techniques
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In the semi-arid Central Andes of Argentina, the water from snowmelt runoff plays a fundamental role as a provider of ecosystem services. Nowadays, the global climate change has an observable negative impact on this area, due, principally, to the decrease in both liquid and solid rainfall, with the consequent decrease in water availability. In this context, runoff prediction acquires vital importance for the integrated water resources management. The aim of this study is to investigate the performance of the Support Vector Regression (SVR) technique in predicting monthly discharges with 1-month lead-time in the Tupungato River basin in the Central Andes of Argentina. This methodology has never been applied before in this mountainous region. Different inputs, like meteorological data and satellite-based snow cover area estimates from MODIS, were analyzed in order to identify the suitable inputs predictors to forecast monthly streamflow. The results were compared against the results derived from a Classification and Regression Tree (CART) model and, also, against an Auto-regressive Integrated Moving-average (ARIMA) model. Different metrics were used to evaluate the performance of the SVR tests in reproducing streamflow observations at the basin outlet. The coefficient of determination for each of the analyzed tests lays between 0.75 and 0.89 in the validation set. The comparison with the other models showed a significant improvement in performance of SVR in respect of CART and ARIMA model. SVR models proved a promising approach to support water management and decision making for productive activities, potentially also in other basins in the region.
Fil: Teverovsky Korsic, Sofia Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comision Nacional de Actividades Espaciales; Argentina. Universidad Nacional de Luján; Argentina
Fil: Notarnicola. Claudia. European Academy of Bozen; Italia
Fil: Uriburu Quirno, Marcelo. Comision Nacional de Actividades Espaciales; Argentina
Fil: Cara Ramirez, Leandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina
description In the semi-arid Central Andes of Argentina, the water from snowmelt runoff plays a fundamental role as a provider of ecosystem services. Nowadays, the global climate change has an observable negative impact on this area, due, principally, to the decrease in both liquid and solid rainfall, with the consequent decrease in water availability. In this context, runoff prediction acquires vital importance for the integrated water resources management. The aim of this study is to investigate the performance of the Support Vector Regression (SVR) technique in predicting monthly discharges with 1-month lead-time in the Tupungato River basin in the Central Andes of Argentina. This methodology has never been applied before in this mountainous region. Different inputs, like meteorological data and satellite-based snow cover area estimates from MODIS, were analyzed in order to identify the suitable inputs predictors to forecast monthly streamflow. The results were compared against the results derived from a Classification and Regression Tree (CART) model and, also, against an Auto-regressive Integrated Moving-average (ARIMA) model. Different metrics were used to evaluate the performance of the SVR tests in reproducing streamflow observations at the basin outlet. The coefficient of determination for each of the analyzed tests lays between 0.75 and 0.89 in the validation set. The comparison with the other models showed a significant improvement in performance of SVR in respect of CART and ARIMA model. SVR models proved a promising approach to support water management and decision making for productive activities, potentially also in other basins in the region.
publishDate 2023
dc.date.none.fl_str_mv 2023-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/233024
Teverovsky Korsic, Sofia Andrea; Notarnicola. Claudia; Uriburu Quirno, Marcelo; Cara Ramirez, Leandro Javier; Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina; Elsevier; Environmental Challenges; 10; 1-2023; 1-9
2667-0100
CONICET Digital
CONICET
url http://hdl.handle.net/11336/233024
identifier_str_mv Teverovsky Korsic, Sofia Andrea; Notarnicola. Claudia; Uriburu Quirno, Marcelo; Cara Ramirez, Leandro Javier; Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina; Elsevier; Environmental Challenges; 10; 1-2023; 1-9
2667-0100
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2667010023000033
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.envc.2023.100680
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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