Assessment of the predictability of inflow to reservoirs through Bayesian causality

This research assesses the predictive capacity of Bayesian causality through causal reasoning (CR), which has been successfully applied to the study of reservoir inflows. We combined autoregressive development with a causal modelling approach through a “proof of concept” that assesses the predictive...

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Autores: Zazo del Dedo, Santiago, Molina González, José Luis, Macian Sorribes, Héctor, Pulido-Velázquez, Manuel
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/162128
Acesso em linha:http://hdl.handle.net/10366/162128
Access Level:acceso abierto
Palavra-chave:Causality
Bayes’ theorem
Predictive models
Temporal runoff fractions
Temporal series analysis
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spelling Assessment of the predictability of inflow to reservoirs through Bayesian causalityZazo del Dedo, SantiagoMolina González, José LuisMacian Sorribes, HéctorPulido-Velázquez, ManuelCausalityBayes’ theoremPredictive modelsTemporal runoff fractionsTemporal series analysisThis research assesses the predictive capacity of Bayesian causality through causal reasoning (CR), which has been successfully applied to the study of reservoir inflows. We combined autoregressive development with a causal modelling approach through a “proof of concept” that assesses the predictive capacity of the approach. The analytical power of CR revealed the logical temporal structure that defines the general behaviour of inflows, which was latent in the historical records. This allowed identifying/quantifying, through a dependence matrix, two temporal runoff fractions, one due to time and the other not. Finally, a predictive model for each temporal fraction was implemented, evaluating its forecasting skills through mean absolute error and root mean square error. This was applied to the reservoirs that supply water to the city of Ávila (Spain), whose watersheds present strong independent temporal behaviour. These results open new possibilities for developing predictive hydrological models within a CR modelling framework.Call for Concept Testing and Results Protection, TCUE PLAN 2018-2020.202520252023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/162128reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)Inglésinfo:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1621282026-06-07T06:28:51Z
dc.title.none.fl_str_mv Assessment of the predictability of inflow to reservoirs through Bayesian causality
title Assessment of the predictability of inflow to reservoirs through Bayesian causality
spellingShingle Assessment of the predictability of inflow to reservoirs through Bayesian causality
Zazo del Dedo, Santiago
Causality
Bayes’ theorem
Predictive models
Temporal runoff fractions
Temporal series analysis
title_short Assessment of the predictability of inflow to reservoirs through Bayesian causality
title_full Assessment of the predictability of inflow to reservoirs through Bayesian causality
title_fullStr Assessment of the predictability of inflow to reservoirs through Bayesian causality
title_full_unstemmed Assessment of the predictability of inflow to reservoirs through Bayesian causality
title_sort Assessment of the predictability of inflow to reservoirs through Bayesian causality
dc.creator.none.fl_str_mv Zazo del Dedo, Santiago
Molina González, José Luis
Macian Sorribes, Héctor
Pulido-Velázquez, Manuel
author Zazo del Dedo, Santiago
author_facet Zazo del Dedo, Santiago
Molina González, José Luis
Macian Sorribes, Héctor
Pulido-Velázquez, Manuel
author_role author
author2 Molina González, José Luis
Macian Sorribes, Héctor
Pulido-Velázquez, Manuel
author2_role author
author
author
dc.subject.none.fl_str_mv Causality
Bayes’ theorem
Predictive models
Temporal runoff fractions
Temporal series analysis
topic Causality
Bayes’ theorem
Predictive models
Temporal runoff fractions
Temporal series analysis
description This research assesses the predictive capacity of Bayesian causality through causal reasoning (CR), which has been successfully applied to the study of reservoir inflows. We combined autoregressive development with a causal modelling approach through a “proof of concept” that assesses the predictive capacity of the approach. The analytical power of CR revealed the logical temporal structure that defines the general behaviour of inflows, which was latent in the historical records. This allowed identifying/quantifying, through a dependence matrix, two temporal runoff fractions, one due to time and the other not. Finally, a predictive model for each temporal fraction was implemented, evaluating its forecasting skills through mean absolute error and root mean square error. This was applied to the reservoirs that supply water to the city of Ávila (Spain), whose watersheds present strong independent temporal behaviour. These results open new possibilities for developing predictive hydrological models within a CR modelling framework.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
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/10366/162128
url http://hdl.handle.net/10366/162128
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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 15,811543

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