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Water table prediction through causal reasoning modelling.

José-Luis Molina1, Jose-Luis García-Aróstegui2

  • 1IGA Research Group, Salamanca University, High Polytechnic School of Engineering Avila, Av. de los Hornos Caleros, 50, 05003 Ávila, Spain.

The Science of the Total Environment
|January 13, 2023
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Summary
This summary is machine-generated.

This study models the rainfall-groundwater level relationship using Bayesian Causal Reasoning. The developed tool aids aquifer management by analyzing temporal data for better groundwater resource understanding.

Keywords:
AquifersBayesian Causal ModellingGroundwaterHydrodynamicsUncertaintyWater management

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Area of Science:

  • Hydrogeology
  • Artificial Intelligence
  • Causal Inference

Background:

  • Understanding the complex relationship between rainfall and groundwater levels (piezometry) is crucial for effective aquifer management.
  • Temporal data analysis is essential for modeling hydrological processes and predicting groundwater behavior.
  • Bayesian Causal Reasoning (BCR) offers a robust framework for inferring causality from observational data.

Purpose of the Study:

  • To analyze and model the causal relationship between hourly rainfall and groundwater levels.
  • To develop a bivariate function representing the rainfall-piezometry dynamic for aquifer management.
  • To apply an innovative AI-based methodology to a real-world aquifer system.

Main Methods:

  • Employed Bayesian Causal Reasoning (BCR), an AI technique based on the Bayesian Theorem.
  • Utilized a two-stage methodology: classic regression analysis followed by Bayesian Causal Modelling Translation (BCMT).
  • Incorporated iterative steps within the BCMT for refined causal modeling.

Main Results:

  • Successfully modeled the inherent causality between temporal rainfall and piezometry data.
  • Developed a functional tool for aquifer management based on the rainfall-piezometry relationship.
  • Validated the methodology on the Quaternary aquifer of Campo de Cartagena, connected to the Mar Menor lagoon.

Conclusions:

  • The developed AI-driven methodology provides a novel approach to understanding rainfall-groundwater interactions.
  • The bivariate function serves as a valuable tool for sustainable aquifer management and groundwater resource assessment.
  • The successful application highlights the potential of BCR in hydrogeological studies and environmental management.