Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff

This paper describes the joint development of two different methods for temporal riverś runoff assessment. This is performed through a hybrid approach by means of Multivariate General Linear Models (MGLM; inspired by MLR as a statistical method), and Causal Reasoning (CR; as non-linear ones). This...

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Autores: Molina González, José Luis, Patino Alonso, María Carmen, Zazo del Dedo, Santiago
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/154518
Acceso en línea:http://hdl.handle.net/10366/154518
Access Level:acceso embargado
Palabra clave:H_CMLM method
Causal Reasoning
Multivariate Linear Modelling
Runoff
Temporal dependence
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spelling Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoffMolina González, José LuisPatino Alonso, María CarmenZazo del Dedo, SantiagoH_CMLM methodCausal ReasoningMultivariate Linear ModellingRunoffTemporal dependenceThis paper describes the joint development of two different methods for temporal riverś runoff assessment. This is performed through a hybrid approach by means of Multivariate General Linear Models (MGLM; inspired by MLR as a statistical method), and Causal Reasoning (CR; as non-linear ones). This innovative methodological approach, named Hybrid Causal Multivariate Linear Modelling (H-CMLM), is mainly aimed to empower the analysis of temporal hydrological records behaviour. H-CMLM has been successfully applied to three different Spanish basins (Adaja, Mijares and Porma) which were chosen due to their disparate features. Results were divided in quantitative and qualitative. Numerical results show a very high level of equivalence between the average value of temporal dependence provided by MLM module and the continuous behaviour of temporal dependence computed by CR module and visualized through Dependence Mitigation Graph (DMG). This high coherent outcome from both modules makes the analysis much more robust from a stochastic hydrology point of view. Values for average temporal dependence are very useful for the optimal dimensioning of hydraulic infrastructures like reservoirs. Furthermore, given the annual scale of the analysis, water planning and management of several water uses such as domestic water supply, agriculture, industrial demands, among others, can be highly assisted by this new H_C-MLM method.Elsevierinfo202420242021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/154518reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/embargoedAccessoai:gredos.usal.es:10366/1545182026-06-07T06:28:51Z
dc.title.none.fl_str_mv Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff
title Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff
spellingShingle Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff
Molina González, José Luis
H_CMLM method
Causal Reasoning
Multivariate Linear Modelling
Runoff
Temporal dependence
title_short Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff
title_full Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff
title_fullStr Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff
title_full_unstemmed Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff
title_sort Hybrid causal multivariate linear modelling (H_CMLM) method for the analysis of temporal rivers runoff
dc.creator.none.fl_str_mv Molina González, José Luis
Patino Alonso, María Carmen
Zazo del Dedo, Santiago
author Molina González, José Luis
author_facet Molina González, José Luis
Patino Alonso, María Carmen
Zazo del Dedo, Santiago
author_role author
author2 Patino Alonso, María Carmen
Zazo del Dedo, Santiago
author2_role author
author
dc.subject.none.fl_str_mv H_CMLM method
Causal Reasoning
Multivariate Linear Modelling
Runoff
Temporal dependence
topic H_CMLM method
Causal Reasoning
Multivariate Linear Modelling
Runoff
Temporal dependence
description This paper describes the joint development of two different methods for temporal riverś runoff assessment. This is performed through a hybrid approach by means of Multivariate General Linear Models (MGLM; inspired by MLR as a statistical method), and Causal Reasoning (CR; as non-linear ones). This innovative methodological approach, named Hybrid Causal Multivariate Linear Modelling (H-CMLM), is mainly aimed to empower the analysis of temporal hydrological records behaviour. H-CMLM has been successfully applied to three different Spanish basins (Adaja, Mijares and Porma) which were chosen due to their disparate features. Results were divided in quantitative and qualitative. Numerical results show a very high level of equivalence between the average value of temporal dependence provided by MLM module and the continuous behaviour of temporal dependence computed by CR module and visualized through Dependence Mitigation Graph (DMG). This high coherent outcome from both modules makes the analysis much more robust from a stochastic hydrology point of view. Values for average temporal dependence are very useful for the optimal dimensioning of hydraulic infrastructures like reservoirs. Furthermore, given the annual scale of the analysis, water planning and management of several water uses such as domestic water supply, agriculture, industrial demands, among others, can be highly assisted by this new H_C-MLM method.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024
2024
info
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/154518
url http://hdl.handle.net/10366/154518
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
eu_rights_str_mv embargoedAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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