Water table prediction through causal reasoning modelling

This research is mainly aimed to analyze and model 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 two main stages, first an analytical method from classic regression analysis, and second, a Bayesian Causal Modelling Translation (BCMT) that itself comprises sev- eral iterative steps. This research ultimately becomes a tool for aquifers management that comprises a bivariate func- ti on 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).

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Molina J.L. y Garcia-Arostegui J.L. Water table prediction through causal reasoning modelling. Elsevier B.V., 2023. https://doi.org/10.1016/j.scitotenv.2023.161492

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Tipo de recurso Artículo
Fecha de creación 05-11-2024
Fecha de última modificación 20-01-2025
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Identificador de los metadatos 070071e8-e1be-59fb-8953-edf35b775f10
Idioma de los metadatos Español
Temáticas (NTI-RISP)
Categoría del conjunto de alto valor (HVD) Observación de la Tierra y medio ambiente
Categoría temática ISO 19115 biota
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Nombre del autor Molina, J.L. y Garcia-Arostegui, J.L.
Nombre del editor Elsevier B.V.
Identificador alternativo DOI: 10.1016/j.scitotenv.2023.161492
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Email del autor jlmolina@usal.es
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