Data-Driven Modelling of Water Table Oscillations for a Porous Aquifer Occasionally Flowing under Pressure

Abstract

Modelling of shallow porous aquifers in scenarios where boundary conditions change over time can be a difficult task. In particular, this is true when data modelling is pursued, i.e., models are directly constructed by measured data. In fact, data contain not only the information related to the physical phenomenon under investigation, but also the effects of time-varying boundary conditions, which work as a disturbance. This undesired component conditions the training of data-driven models, as they are fitted by models, which can produce predictions diverging from measured data. Here, a very shallow porous aquifer is modelled in terms of its response to water table to precipitation. The aquifer is characterized by the presence of a low permeability silty top layer covering the lower sandy strata, where the aquifer normally flows. Therefore, when the piezometric level increases up to the low permeability layer, the aquifer changes its behavior from phreatic to confined. This determines the changing boundary condition, which makes the response of the aquifer to rain precipitations complex, as it is related to a two-fold condition: confined or phreatic. The aquifer here is investigated by two machine learning approaches, the earlier based on an evolutionary modeling, and the latter based on artificial neural networks. Evolutionary modeling returned explicit equations with a fitness efficiency up to 0.8 for 1 month for predictions and 0.48 for simulations, while neural networks arrived at 0.85 and 0.28, respectively. The aim of this study is to get an explicit model of the response of the piezometric heights of the aquifer to the precipitations, which is useful for planning the use of groundwater resources.

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

“…Sensitivity analysis is an essential process for differentiating (gray) EPR models from black-box approaches. For these reasons, EPR models have found its way into engineering practice (Ahangar-Asr et al, 2011b;Alani and Faramarzi, 2014;Balf et al, 2018;Berardi et al, 2008;Bruno et al, 2018;Costa et al, 2020;Doglioni et al, 2010;Doglioni and Simeone, 2021;Faramarzi et al, 2012;Fiore et al, 2012Fiore et al, , 2016Giustolisi et al, 2007Giustolisi et al, , 2008Gomes et al, 2021a;Jin and Yin, 2020;Laucelli and Giustolisi, 2011;Montes et al, 2020;Rezania et al, 2008Rezania et al, , 2010Rezania et al, , 2011Shahin, 2015).…”