Groundwater recharge susceptibility mapping using logistic regression model and bivariate statistical analysis

Abstract

A logistic regression model and a bivariate statistical analysis were used in this paper to evaluate the groundwater recharge susceptibility. The approach is based on the assessment of the relationship involving groundwater recharge and parameters that influence this hydrological process. Surface parameters and aquifer-related parameters were evaluated as thematic map layers using ArcGIS. Then, a weighted-rating method was adopted to categorize each parameter9s map. To assess the role of each parameter in the aquifer recharge, a logistic regression model and a bivariate statistical analysis were applied to the Guenniche phreatic aquifer (Tunisia). Models are explored to establish a map showing the aquifer recharge susceptibility. The code Modflow was used to simulate the consequence of the recharge. The recharge amount was introduced in the model and was tested to verify the recharge effect on the hydraulic head for the two models. The obtained results reveal that the recharge as mapped in the bivariate statistical model has a minor impact on the hydraulic head. Results of the logistic regression model are more significant as the hydraulic head is widely affected. This model provides good results in mapping the spatial distribution of the aquifer recharge susceptibility.