Alternate Groundwater Modelling Strategies: A Multi-Faceted Data-Driven Approach

TL;DR

This paper evaluates traditional and modern statistical models, including neural networks and copula strategies, for groundwater data analysis, emphasizing model validation, longevity, and efficiency to improve forecasting accuracy.

Contribution

It introduces a comprehensive validation framework for groundwater models, combining traditional, neural, and copula-based approaches, with a focus on model longevity and robustness.

Findings

Neural networks can effectively analyze groundwater data.

Copula-based methods reveal directional dependencies in groundwater levels.

Validation techniques improve model robustness and reduce computational costs.

Abstract

The impact of statistical methodologies on studying groundwater has been significant in the last several decades, due to cheaper computational abilities and presence of technologies that enable us to extract and measure more and more data. This paper focuses on the validation of statistical methodologies that are in practice and continue to be at the earliest disposal of the researcher, demonstrating how traditional time-series models and modern neural networks may be a viable option to analyze and make viable forecasts from data commonly available in this domain, and suggesting a copula-based strategy to obtain directional dependencies of groundwater level, spatially. This paper also proposes a sphere of model validation, seldom addressed in this domain: the model longevity or the model shelf-life. Use of such validation techniques not only ensure lower computational cost while maintaining reasonably high accuracy, but also, in some cases, ensure robust predictions or forecasts, and assist in comparing multiple models.