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Delineation of the groundwater's potential zones is a growing phenomenon worldwide due to the high demand for fresh groundwater. Therefore, the identification of potential groundwater zones is an important tool for groundwater occurrence, protection, and management purposes. More specifically, in arid and semi-arid regions, groundwater is one of the most important natural resources as it supplies water during the drought period. The present research study focused on the delineation of potential groundwater zones in Saveh City, the northern part of the Markazi Province in Iran. The groundwater potential mapping was prepared using hybrid deep learning and machine learning algorithm of the boosted tree (BT), artificial neural network (ANN), deep learning neural network (DLNN), deep learning tree (DLT), and deep boosting (DB). This study was carried out by using fourteen groundwater potential conditioning factors and 349 each for springs and non-springs points. The performance of each model was validated through statistical analysis of sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and receiver operating characteristic (ROC)-area under curve (AUC) analysis. The validation result showed that the success rate of AUC is very good for the DB model (0.87–0.99) and other models are also good i.e. BT (0.81–0.90), ANN (0.77–0.82), DLNN (0.84–0.86), and DLT (0.83–0.91). Among the several factors used in this study altitude, rainfall, distance to fault and soil types are the more important conditioning factors for groundwater potential modeling. Finally, all the models in this study had high efficiency in groundwater potential mapping, but it is recommended to use the Deep Boost model due to the better results in future studies. The result of this work will be useful to planners for optimal use and future planning of groundwater. [ABSTRACT FROM AUTHOR]

Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping erosion is essential. Boosting is a classic algorithm that has been successfully applied to diverse computer vision tasks. Therefore, this work investigated the predictive performance of the Boosted Linear Model (BLM), Boosted Regression Tree (BRT), Boosted Generalized Linear Model (Boost GLM), and Deep Boosting models for piping erosion susceptibility mapping in Zarandieh Watershed located in the Markazi province of Iran. A piping inventory map including 152 piping erosion locations was prepared for algorithm training and testing. 18 initial predisposing factors (altitude, slope, plan curvature, profile curvature, distance from river, drainage density, distance from road, rainfall, land use, soil type, bulk density, CEC, pH, clay, silt, sand, topographical position index (TPI), topographic wetness index (TWI)) was derived from multiple remote sensing (RS) sources to determine the piping erosion prone areas. The most significant predisposing factors were selected using multi-collinearity analysis which indicates linear correlations between predisposing factors. Finally, the results were evaluated for Sensitivity, Specificity, Positive predictive values (PPV) and Negative predictive value (NPV), and Receiver Operation characteristic (ROC) curve. The best Sensitivity (0.80), Specificity (0.84), PPV (0.85), NPV (0.79), and ROC (0.93), were obtained by Deep Boosting model. The results of the piping erosion susceptibility study in agricultural land use showed that 41% of agricultural lands are very sensitive to piping erosion. This outcome will enable natural resource managers and local planners to assess and take effective decisions to minimize damages to agricultural land use by accurately identifying the most vulnerable areas. Hence, this research proved Deep Boosting model's ability for piping erosion susceptibility mapping in comparison to other popular methods such as BLM, BRT, and Boost GLM. [ABSTRACT FROM AUTHOR]