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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Resource type Article
Date of creation 2024-11-05
Date of last revision 2024-11-05
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Metadata identifier 070071e8-e1be-59fb-8953-edf35b775f10
Metadata language Spanish
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High-value dataset category Earth observation and environment
ISO 19115 topic category biota
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Name of the dataset creator Molina, J.L. y Garcia-Arostegui, J.L.
Name of the dataset editor Elsevier B.V.
Other identifier DOI: 10.1016/j.scitotenv.2023.161492
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Email of the dataset creator jlmolina@usal.es
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