Comparison of different statistical approaches to assess spring potential mapping in a multi-aquifer system: a case study of Kurdistan Region, Iraq

For the current study, four geostatistical approaches, i.e., logistic regression (LR), frequency ratio (FR), Shannon entropy (SE), and weight of evidence (WOE) were utilized to construct the spring potential index in part of the Duhok area, Iraq. The collection and analysis of the data were based on the most influential factors and frequently used parameters in such a study. Almost 450 springs and 15 causative factors as dependent and independent variables were applied. Among the causative factors utilized to generate the SPM, LU/LC, LS, precipitation, distance from the fault, and SPI have the most influencing effects on spring emergence. Moreover, from the 18 lithologic units cropping out in the area of interest, five lithologic units exhibit the most effectiveness in spring occurrence; they are the Gercus Fn. with 91 springs, or 21%; the slope sediment (76 springs, or 17.4%); the Pilaspi PilaSpi Fn. (75 springs, or 17.4%); the Mukdadiyah Fn. (72 springs, or 17.3%) and the Bai Hassan Fn. (41 springs, or 9.4%), of all the springs, respectively. The spring density index (SDI), the area under the curve (AUC), and the receiver operating characteristic curve (ROC) were used to assess the validity of each constructed model using the training and validating data sets. The results of the SDI values showed that for the LR and WOE maps, the sum of very high and high spring power index (SPI) contains about 80% of the validation spring sites (VSP), while the final FR and SE maps contain 68% and 43% of the validation spring sites, respectively. By applying the AUC performance method, the LR model provides the highest AUC value (0.834), followed by the FR and WOE (0.759 and 0.782), as well as the SE (0.689) model. The same performance pattern was estimated based on the validation data set, with the WOE model providing the highest AUC value (0.773), followed by the LR and FR (0.759 and 0.758, respectively), and the SE (0.654) models. These results indicated that the springs potential mapping (SPM) produced by all the mentioned models showed better performance than that of the SE model. The outcomes of this study conclude that all the models could be considered valid and acceptable approaches that can be utilized by decision-makers for sustainable management of groundwater in the area of interest.