In the drought prone district of Dholpur in Rajasthan, India, groundwater is a lifeline for its inhabitants. With population explosion and rapid urbanization, the groundwater is being critically over-exploited. Hence the current groundwater potential mapping study was undertaken to ascertain the areas that are more likely to yield a larger volume of groundwater against those areas that have poor groundwater potential and accordingly perpetuate the much needed damage control. Thematic layers for 14 groundwater influencing factors were considered for the study region, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), geology, soil, land use, normalized difference vegetation index (NDVI), surface temperature, precipitation, distance from roads, and distance from rivers. These were then subjected to an overlay operation, with the groundwater inventory which comprised of the locations of observational groundwater wells. The resulting geospatial database was then used to train two decision tree based ensemble models: gradient boosted decision trees (GBDT) and random forest (RF). The predictive performance of these models was then compared using various performance metrics such as area under curve (AUC) of receiver operating characteristics (ROC), sensitivity, accuracy, etc. It was found that GBDT (AUC: 0.79) outperformed RF (AUC: 0.71). The validated GBDT model was then used to construct the groundwater potential zonation map. The generated map showed that about 20.2% of the region has very high potential, while 22.6% has high potential to yield groundwater, and approximately 19.9–17.5% of the study region has very low to low groundwater potential.

在印度拉贾斯坦邦易旱的多尔布尔地区，地下水是其居民的生命线。随着人口爆炸和快速的城市化，地下水正被严重地过度开采。因此，目前进行的地下水潜力测绘研究是为了确定那些较可能产生大量地下水的地区，而不是那些地下水潜力较弱的地区，从而使迫切需要的破坏得以持续。研究区域考虑了14个地下水影响因素的主题层，包括海拔，坡度，坡向，平面曲率，剖面曲率，地形湿度指数（TWI），地质，土壤，土地利用，归一化植被指数（NDVI），地表温度，降水，距道路的距离以及距河流的距离。然后对它们进行覆盖作业，其中地下水清单包括观察性地下水井的位置。然后，将生成的地理空间数据库用于训练两个基于决策树的集成模型：梯度增强决策树（GBDT）和随机森林（RF）。然后，使用各种性能指标（例如接收器工作特性（ROC）的曲线下面积（AUC），灵敏度，准确性等）对这些模型的预测性能进行了比较。发现GBDT（AUC：0.79）优于RF（AUC： 0.71）。经过验证的GBDT模型随后用于构建地下水潜力分区图。生成的地图显示，该地区约20.2％的潜力很大，而22.6％的潜力很大，约有19.9–17。