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...

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Bibliographic Details
Published in:Hydrological sciences journal 2023-07, Vol.68 (10), p.1323-1337
Main Authors: Zazo, Santiago, Molina, Jose-Luis, Macian-Sorribes, Hector, Pulido-Velazquez, Manuel
Format: Article
Language:English
Subjects:
Bayes' theorem
Bayesian analysis
Bayesian theory
causality
Hydrologic models
Hydrology
Inflow
Mathematical models
Modelling
Prediction models
predictive models
Probability theory
Reservoirs
Runoff
temporal runoff fractions
temporal series analysis
Water inflow
Watersheds
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Summary: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.
ISSN:0262-6667
2150-3435
DOI:10.1080/02626667.2023.2200143