发表机构
Rhodes University（罗德斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合GRACE数据与无监督机器学习，检测加纳2004-2024年地下水储量异常，识别出12个异常月，为数据匮乏地区地下水监测提供实用框架。
AI 中文摘要
由于长期原位观测有限，加纳的地下水变异性特征仍未得到充分表征。本研究结合2004-2024年GRACE衍生数据、统计分析与无监督机器学习方法，对地下水储量异常展开研究。采用Z分数对地下水异常进行标准化处理，同时应用基于集成的Isolation Forest框架开展异常检测。结果显示存在显著的时间变异性：2004-2009年期间持续出现地下水亏缺，2018年后正异常逐渐增多；共识别出12个异常月份，包含5次亏缺事件与7次盈余事件，其中最强异常与地下水亏缺相关。空间分析表明，加纳北部亏缺异常更频繁，南部盈余异常更显著；与统计阈值对比显示，该机器学习框架可捕捉到传统阈值方法无法检测到的细微偏差。总体而言，将GRACE观测数据与无监督异常检测相结合，为数据匮乏环境下的地下水监测提供了实用框架。
英文摘要
Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations. This study investigates groundwater storage anomalies using GRACE-derived data from 2004-2024 combined with statistical analysis and unsupervised machine learning. Groundwater anomalies were standardized using Z-scores, while an ensemble-based Isolation Forest framework was applied for anomaly detection. The results revealed substantial temporal variability, with persistent groundwater deficits during 2004-2009 followed by increasing positive anomalies after 2018. A total of 12 anomalous months were identified, comprising 5 deficit and 7 surplus events, with the strongest anomalies associated with groundwater deficits. Spatial analysis showed more frequent deficit anomalies in northern Ghana and stronger surplus occurrence in southern regions. Comparison with statistical thresholds further indicated that the machine learning framework captured additional subtle deviations beyond conventional threshold-based methods. Overall, the integration of GRACE observations with unsupervised anomaly detection provides a practical framework for groundwater monitoring in data-scarce environments.
Comments8 pages, 7 figures, accepted at ICECCME 2026
