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 parameter's 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.

Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models

“…Knowledge of spatial ground water recharge potential zones has placed considerable emphasis to addressing effectively planning and better managing water resources. Correspondingly, there has been growing numbers of research addressing zoning of groundwater recharge potential in recent years 19,27,29,36,37,54,55 . Although www.nature.com/scientificreports/ there still remains much work to understanding the effect of multiple drivers on the distribution and pattern of GWR potential.…”

“…This supports the previous findings from Tehrany et al 74 ; Razavi-Termeh et al 28 and Di Napoli et al 87 that ensemble classifiers allow more accurate prediction than classical models and alternative to the individual models in susceptibility and potential mapping. A research carried out in Qued Guenniche in the north of Tunisia to produce a GWR potential map by Chenini and Msaddek 36 . The authors found that the bivariate and multivariate statistical approaches provide more accurate results than the AHP.…”

“…They concluded that based on the area under the curve (AUC) measure, EBF, as a data driven technique, outperformed the AHP. Chenini and Msaddek 36 compared the bivariate statistical analysis and logistic regression model (LR) to produce a GWRP map in Qued Guenniche in the north of Tunisia. The authors found that the bivariate and multivariate statistical approaches provide more accurate results than the AHP.…”

Geological hazards susceptibility evaluation based on multi‐year spatial–temporal evolution of assessment factors in Luding area, Sichuan Province, China

“…Tian C.S. et al and Wang H. et al conducted geological hazard susceptibility research in Guangdong Province and Shuangbai County, China, using logistic regression analysis (Tian et al, 2016; Wang et al, 2023). Shujun Tian et al and Lei Wang et al used grid units to measure sensitivity to geological hazards (Tian et al, 2019; Wang et al, 2021).…”

Assessment of Groundwater Potential Zones of Upper Blue Nile River Basin Using Multi-Influencing Factors under GIS and RS Environment: A Case Study on Guder Watersheds, Abay Basin, Oromia Region, Ethiopia

“…The application of geographical information systems in groundwater assessing, monitoring, and management such as delineation of groundwater potential zones has been reported by many scholars [3,4,11,[31][32][33][34][35][36][37][38][39][40][41][42]. Several researchers in current days perform several advanced methodological approaches for groundwater potential investigation, amongst which frequency ratio [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61], logistic regression [50][51][52][53][54][55][56], fuzzy logic [22][23][24][57][58][59][60][61]…”