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Graphical abstract Highlights • A novel hybrid model, namely LMT, was applied for groundwater potential mapping. • The result of LMT was validated using AUC-ROC method. • The accuracy of LMT was comprised with C4.5 and SVM models. • The capability and robustness of all three models were assessed. • Relative slope position (RSP) was the most important conditioning factor. Abstract Sustainable water resources management in arid and semi-arid areas needs robust models, which allow accurate and reliable predictive modeling. This issue has motivated the researchers to develop hybrid models that offer solutions on modelling problems and accurate predictions of groundwater potential zonation. For this purpose, this research aims to investigate the capability and robustness of a novel hybrid model, namely the logistic model tree (LMT) and compares it with state-of-the-art models such as the support vector machine and C4.5 models that locate potential zones for groundwater springs. A spring location dataset consisting of 359 springs was provided by field surveys and national reports and from which three different sample data sets (S1–S3) were randomly prepared (70% for training and 30% for validation). Additionally, 16 spring-related factors were analyzed using regression logistic analysis to find which factors play a significant role in spring occurrence. Twelve significant geo-environmental and morphometric factors were identified and applied in all models. The accuracy of models was evaluated by three different threshold-dependent and –Independent methods including efficiency (E), true skill statistic (TSS), and area under the receiver operating characteristics curve (AUC-ROC) methods. Results showed that the LMT model had the highest accuracy performance for all three validation datasets (E mean = 0.860, TSS mean = 0.718, AUC-ROC mean = 0.904); although a slight sensitivity to change in input data was sometimes observed for this model. Furthermore, the findings showed that relative slope position (RSP) was the most important factor followed by distance from faults and lithology. [ABSTRACT FROM AUTHOR]

• Spotting high potential areas for groundwater exploitation. • Application of two state-of-the-art machine learning algorithms. • Feeding only DEM-derived factors to the algorithms to determine groundwater potential. • Extreme gradient boosting and parallel random forest yielded AUC values of about 86% • Convergence, TWI, river density, and altitude are the most important factors. Groundwater (GW) resources provide a large share of the world's water demand for various sections such as agriculture, industry, and drinking water. Particularly in the arid and semi-arid regions, with surface water scarcity and high evaporation, GW is a valuable commodity. Yet, GW data are often incomplete or nonexistent. Therefore, it is a challenge to achieve a GW potential assessment. In this study, we developed methods to produce reliable GW potential maps (GWPM) with only digital elevation model (DEM)-derived data as inputs. To achieve this objective, a case study area in Iran was selected and 13 factors were extracted from the DEM. A spring location dataset was obtained from the water sector organizations and, along with the non-spring locations, fed into machine learning algorithms for training and validation. For delineating reliable GW potential, algorithms including random forest (RF) and its developed version, parallel RF (PRF), as well as extreme gradient boosting (XGB) with different boosters were used. The area under the receiver operating characteristics curve indicated that the PRF and XGB with linear booster give similar high accuracy (about 86%) for GWPM. The most important factors for accurate GWPM in the modeling procedure were convergence, topographic wetness index, river density, and altitude. Overall, we conclude that high-accuracy GWPMs can be produced with only DEM-derived factors with acceptable accuracy. The developed methodology can be employed to produce initial information for GW exploitation in areas facing a lack of data. [ABSTRACT FROM AUTHOR]