Groundwater resources are essential for ensuring a consistent water supply in many regions. Groundwater potential maps (GPMs) can be utilized in many ways to estimate the quantity, quality, and distribution of subsurface water, supporting the decision-making processes of numerous stakeholders. This study contributes to improving the accuracy of GPMs, focusing on implementing Geospatial Artificial Intelligence (GeoAI) models. For this purpose, the accuracy performance of the Extreme Gradient Boosting (XGBoost) algorithm is improved in this study. To do this, two such popular metaheuristic algorithms, i.e., invasive weed optimization (IWO) and biogeography-based optimization (BBO), are integrated into the XGBoost algorithm for modeling and spatial prediction of the areas prone to groundwater. Three models—XGBoost, XGBoost-IWO, and XGBoost-BBO—are implemented within the Python programming environments to execute spatial modeling and generate predictive maps. The evaluation of results unfolds in two stages: model validation and GPM validation. For the training data, the root mean square error (RMSE) and mean absolute error (MAE) indices were 0.165 and 0.121 for XGBoost, 0.13 and 0.087 for XGBoost-IWO, and 0.114 and 0.082 for XGBoost-BBO, respectively. The test data showed similar trends, with XGBoost yielding RMSE and MAE values of 0.424 and 0.295, XGBoost-IWO at 0.416 and 0.287, and XGBoost-BBO at 0.39 and 0.28. XGBoost-BBO, XGBoost-IWO, and XGBoost had a prediction accuracy higher than other models. The respective area under the curve (AUC) of GMPs using receiver operating characteristic (ROC) curves for XGBoost, XGBoost-IWO, and XGBoost-BBO were 81.8 %, 83.1 %, and 83.7 %. Using bio-inspired metaheuristic algorithms, the GPM accuracy rate has improved further. The study of groundwater resources demonstrated how geological feature extraction by GeoAI may help employ advanced techniques.

地下水资源对于确保许多地区的稳定供水至关重要。地下水潜力图 （GPM） 可以以多种方式用于估计地下水的数量、质量和分布，从而支持众多利益相关者的决策过程。本研究有助于提高 GPM 的准确性，重点是实施地理空间人工智能 （GeoAI） 模型。为此，本研究提高了 Extreme Gradient Boosting （XGBoost） 算法的精度性能。为此，将两种流行的元启发式算法，即侵入性杂草优化 （IWO） 和基于生物地理学的优化 （BBO） 集成到 XGBoost 算法中，用于对易出现地下水的区域进行建模和空间预测。在 Python 编程环境中实施了三个模型（XGBoost、XGBoost-IWO 和 XGBoost-BBO），以执行空间建模并生成预测地图。结果评估分为两个阶段：模型验证和 GPM 验证。对于训练数据，XGBoost 的均方根误差 （RMSE） 和平均绝对误差 （MAE） 指数分别为 0.165 和 0.121，XGBoost-IWO 为 0.13 和 0.087，XGBoost-BBO 为 0.114 和 0.082。测试数据显示出类似的趋势，XGBoost 的 RMSE 和 MAE 值为 0.424 和 0.295，XGBoost-IWO 为 0.416 和 0.287，XGBoost-BBO 为 0.39 和 0.28。XGBoost-BBO 、 XGBoost-IWO 和 XGBoost 的预测精度高于其他模型。使用 XGBoost、XGBoost-IWO 和 XGBoost-BBO 的受试者工作特征 （ROC） 曲线的 GMP 曲线下面积 （AUC） 分别为 81.8 %、83.1 % 和 83.7 %。使用仿生元启发式算法，GPM 准确率进一步提高。 对地下水资源的研究展示了 GeoAI 的地质特征提取如何帮助采用先进技术。