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Título
Water table prediction through causal reasoning modelling
Autor(es)
Materia
Hydrodynamics
Bayesian Causal Modelling
Groundwater
Uncertainty
Aquifers
Water management
Fecha de publicación
2023
Editor
Elsevier
Resumen
This research is mainly aimed to analyze andmodel the relationship of the binomial Rainfall-Piezometry. In this sense, the inherent causality contained in temporal hourly Rainfall and Groundwater levels (piezometry) data records has been taken. This has been done through Bayesian Causal Reasoning (BCR) which is technique belonging to Artificial Intelligence (AI) based on Bayesian Theorem. The methodology comprises twomain stages, first an analyticalmethod from classic regression analysis, and second, a Bayesian CausalModelling Translation (BCMT) that itself comprises several iterative steps. This research ultimately becomes a tool for aquifers management that comprises a bivariate function made of two variables Rainfall and Piezometry (Temporal Groundwater level evolution). This innovative methodology has been successfully applied in the Quaternary aquifer of the Campo de Cartagena groundwater body, which is an aquifer system that directly is connected to Mar Menor coastal lagoon (Murcia region, SE Spain).
URI
ISSN
0048-9697
DOI
10.1016/j.scitotenv.2023.161492
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