## 6 Discussion  
Machine learning modeling of environmental problems has gained popularity because machine learning methods show promise when dealing with manifold geospatial data [69; 87; 88; 89]. As such, machine learning modeling can effectively alleviate the difficulty associated with the identification  
Figure 7: Analysis of the groundwater potential maps.  
of groundwater potential zones over large-scale regions, which often suffer a lack of accurate and long-term geotechnical and hydrogeological data for the implementation of physically based and/or numerical models [11]. However, the utility of different machine learning methods should be broadly investigated via their applications in different regions with different geo-environmental settings to find the best model with the highest accuracy and lowest sensitivity to noisy input data [33; 70; 87; 90; 91].  
The results of our study revealed that all four ensemble techniques used in this study proficiently optimized the performance of the base LR model and provided reliable estimations of groundwater potential based on different validation methods. More specifically, the results of the ROC-AUC method demonstrated that the single LR model and its four derived ensemble models with a mean AUC of 0.88 had an excellent goodness-of-fit with the training dataset. During the validation phase, performances of the models that indicate their capabilities to estimate groundwater potential [37] were decreased to a mean AUC of 0.73. Based on the relationship between AUC values and the predictive capability of the models that has been suggested in the literature [14; 19; 22; 24; 84], we can conclude that our models performed decently in estimating groundwater potential and developing distribution maps. Further, the results demonstrated the capability of ensemble learning techniques for improving LR performance. Among the four ensemble techniques used in this study, dagging was identified as the most effective technique, which was followed by RSS, bagging, and CG techniques, suggesting that dagging was the most capable model in reducing the variance, bias, and noise of the groundwater potential modeling.  
Although the literature has mostly reported on the effectiveness of the ensemble techniques, these techniques showed different performances for different problems in different areas. For example, [PERSON] et al. [36] showed that reduced error pruning tree (REPT) performed better in combination with RSS than the bagging and AdaBoost techniques for gully erosion prediction, whereas [PERSON] et al. [92] reported that the REPT model performed better with rotation forest and bagging than its combination with the RSS and multiboost for landslide prediction. Different results have also been reported for flood prediction based on the ensemble models [93; 94]. From these studies, we can conclude that the machine learning and ensemble learning techniques are greatly case- and site-specific, and that their performances depend heavily on the local conditions that the training datasets are developed upon, indicating that the application of different methods in different regions should be continued to find the optimum method for each environmental setting [95].