Groundwater spring potential mapping: Assessment the contribution of hydrogeological factors

Abstract

Groundwater, a fundamental asset, isn't effectively accessible in some parts of the world. The current research work pointed toward obtaining precise maps of potential groundwater zones. This study aimed for potential groundwater modeling and extracting the precise maps using four new advanced hybrid ML models (Dagging-HP, Bagging-HP, AdaBoost-HP, Decorate-HP) and one single model Hyperpipes (HP) in the Doji Watershed, situated in the eastern part of Golestan province, Iran. Among the selected models, the AdaBoost-HP model is the most efficient, with an AUC - ROC of 0.972, accuracy (0.922), sensitivity (0.906), and specificity (0.938), which gives the most promising values, when determining the collinearity between the 14 training factors, which are, in descending order of significance, LULC, Distance to stream (DtS), Topography wetness index (TWI), HAND, Distance to the road (DtR), Geomorphology, Topography position index (TPI), Lithology, Drainage density (DD), Elevation, Slope, Rainfall, and Clay (%). The AUC-ROC approach was employed to assess the model's performance along with Accuracy, Specificity, and Sensitivity. This model revealed that 7.37% has very high groundwater potential in the eastern and south-western parts of the study, whereas 36.8% has a very low groundwater potential in the north-western and south-eastern parts of the study. It can be said from this assessment that results obtained from this investigation are better and more reliable, which gives essential encouragement for further study put on this method for groundwater potential mapping of other areas of the world along with other areas of hydrogeological investigations.