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This study makes a significant contribution to the field of groundwater potential mapping (GWPM) by exploring the application of ensemble learning models (ELMs), specifically boosting ensemble models (BEMs), which have not been fully utilized in GWPM. By employing six ELMs (random forest, AdaBoost, XGBoost, CatBoost, GBDT and LightGBM), along with Tree-structured Parzen Estimator in Luoning County, China, this study identifies key indicators (topographic position index, distance to rivers and topographic wetness index) and demonstrates the superior model performance of XGBoost compared to other ELMs. Additionally, correlation analysis confirms the accuracy of XGBoost in predicting relationships between important indicators and groundwater potentials. Finally, the findings provide valuable insights for sustainable groundwater management strategies in Luoning County and emphasize the need for further exploration of ELMs, development of comprehensive performance evaluation and indicator systems, reduction of the inconsistencies between indicators and predication results and practical research to support future sustainable groundwater management. [ABSTRACT FROM AUTHOR]

• A total of 23 factors covering almost all factors frequently used in GPM. • LightGBM-C model achieved the highest model performance for GPM. • Arid regions are influenced significantly by climate factors PRE, EVA. • Future research on GPM should also consider DD, EV, NDVI, and GWD. Groundwater potential mapping (GPM) provides the valuable information on groundwater volume that can be withdrawn from the aquifer without affecting the environmental conditions. In this study, GPM was innovatively explored by including three typical climate factors - precipitation (PRE), evaporation (EVA), and ground surface temperature (GST) - and by taking into consideration a total of 23 conditional groundwater factors during the model construction. Three ensemble learning models are used for the modeling process: random forest (RF), XGBoost, and LightGBM. The study was conducted in the southern regions of Yinchuan Plain, which is known for its temperate continental climate with low precipitation and high evaporation. It was found that all six adopted models (i.e., RF-C, XGBoost-C, LightGBM-C, RF, XGBoost, LightGBM) made reasonable predictions of groundwater potential, with areas with high and very high potential concentrated in the southwest region, where the Yellow River enters the study area. The LightGBM-C model performs the best (OA: 0.769, F1 score: 0.667, AUC: 0.921), while the RF model performs the worst (OA: 0.654, F1 score: 0.4, AUC: 0.757). According to the LightGBM-C model, there are more productive wells in areas with high groundwater potential (22 wells, 0.0152 per km2), while fewer non-productive wells are found (3 wells, 0.0021 per km2). The performance of LightGBM-C and RF-C models has been notably enhanced by climate factors (AUC + 0.073, OA + 0.025, F1 score + 0.052 for LightGBM, and AUC + 0.073, OA + 0.051, F1 score + 0.149 for RF). The further cumulative importance results indicate that the three ensemble models which considered climate factors demonstrated a substantial sensitivity to PRE (27.78%), EVA (28.14%), and GST (23.34%). In arid and semi-arid regions, PRE and EVA are highly recommended factors for GPM, while DD (35.17%), EV (35.22%), NDVI (28.06%) and GWD (25.77%) are also confirmed to be important when climate factors are not taken into account. [ABSTRACT FROM AUTHOR]

Groundwater nitrate contamination has emerged as a pressing global concern. Given its potential for long-term impacts on aquifers, protective measures should primarily focus on prevention. Drawing on the theory of groundwater vulnerability (GV), the original DRASTIC model and parameters related to human activities are employed as inputs and integrated with the LightGBM regression algorithm to facilitate nitrate index (NI) prediction tasks. The SHAP analysis is conducted to effectively examine the contribution of parameters to the NI prediction and interpret the issue of parameter interactions. In addition, to mitigate the limitations of the intrinsic GV model, a composite nitrate index (CNI) is developed by linearly combining the DRASTIC index with the NI. The framework presented in this study provides adaptive strategies for managing groundwater resources over different time periods. A representative region for arid and semiarid climates, the Yinchuan region, is studied using the framework. As compared to 2012, the intrinsic GV index has changed spatially in 2022. Human activities have increased the influence of the nitrate concentration as shown by the Pearson correlation coefficient of −0.082 between the DRASTIC index and nitrate concentration. A significant increase in pollution levels was predicted by NI, ranging from −0.116 to 0.968. According to SHAP analysis, the significant increase in NI levels in 2022 was mainly due to high-value industrial and agricultural production. In 2022, 12.02% of the areas had an increase of at least 0.549 in the CNI. 42.1% of the areas were classified as moderate or high CNI levels. The farm was identified as a high-contributing source to nitrate pollution. The small-scale agricultural and livestock activities in non-urban areas also contribute to groundwater pollution. Dynamic groundwater management strategies need to be implemented in high-growth and high-level CNI areas. [Display omitted] • DRASTIC model and anthropogenic parameters were combined to predict nitrate index. • The innovative composite nitrate SGV framework were applied to Yinchuan regions. • SHAP analysis results support an increase in nitrate loading caused by human activity. • Dynamic groundwater management strategies were designed for different levels of CNI. [ABSTRACT FROM AUTHOR]