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 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). [Display omitted] •Causal Reasoning modelling for the study of groundwater hydrodynamic processes•Analyze and model the relationship of the binomial Rainfall-Piezometry•Bayesian Causal Reasoning (BCR) to capture the inherent causality in data records•Hourly Rainfall and Piezometry data were used to capture the sudden aquifer response.•Applied to the Quaternary aquifer of the Campo de Cartagena groundwater body