MaxEnt machine learning model predicts high groundwater potential areas in a fractured volcanic aquifer system

Abstract

Groundwater constitutes a vital resource for public water supply, and thus, it is imperative to recognize the areas of highest potential for increasing availability. The present study employs the MaxEnt model to discern the most favorable areas for locating high-yield wells in Caxias do Sul, Rio Grande do Sul, southern Brazil, where the Serra Geral Aquifer System, a fractured volcanic aquifer, emerges. This aquifer system is characterized by its heterogeneous, discontinuous, and highly anisotropic nature. A dataset comprising 83 wells with high flow rates (≥10 m³/h) was selected from the municipal registry of deep tubular wells, along with 14 factors that influence groundwater occurrence (specific capacity, transmissivity, altitude, slope, horizontal curvature, vertical curvature, relief dissection index, drainage density, distance to drainage, topographic wetness index, distance to lineament, lineament density, precipitation, and soil hydrological group). The model output was a Groundwater Potential Map, which stochastically expresses the probability of obtaining flow rates ≥10 m³/h. The map was validated through cross-validation, resulting in an average accuracy of 65.14%, and by the Receiver Operating Characteristic analysis, resulting in an Area Under the Curve value of 0.911, indicating satisfactory validation. While the MaxEnt model is widely used in ecology to model species distribution, its application in groundwater prediction remains limited, particularly in fractured aquifers associated with volcanic rocks. Apart from optimizing the use of groundwater resources, this study also enhances the understanding of natural phenomena in this type of aquifer.